Title of Invention

CONTROL SYSTEM FOR PLANT

Abstract A control system for a plant which controls the plant using an identifier and a sliding mode controller. The identifier identifies a model parameter vector of a controlled object model of the plant. The sliding mode controller controls the plant using the model parameter vector identified by the identifier. According to the identifier, an identifying error of the model parameter vector is calculated and an updating vector is calculated according to the identifying error. The updating vector is corrected by multiplying a past value of at least one element of the updating vector by a predetermined value which is greater than "0" and less than "I". The model parameter vector is calculated by adding the corrected updating vector to a reference vector of the model parameter vector.
Full Text DESCRIPTION
Title of Invention
CONTROL SYSTEM FOR PLANT
Technical Field
The present invention relates to a control system for a plant, and more particularly to a control system for controlling a plant with a sliding mode controller based on a sliding mode control theory which is one of robust control theories.
Background Art
One known control system based on a sliding mode control theory is disclosed in Japanese Patent Laid-open No. Hei 9-274504, for example. The publication proposes a method of setting a hyperplane in the sliding mode control theory according to the convergence state of a controlled state quantity. According to the proposed method, the convergence response and convergence stability of the sliding mode control is improved.
For controlling a plant, which is a controlled object, with the sliding mode controller, it is necessary to produce a model of the plant and determine model parameters representing the characteristics of the model of the plant (i.e. the controlled object). The model parameters may be set to predetermined constant values. However, the values of the model parameters usually change due to aging and disturbance. Therefore, it is desirable ro use a model parameter identifier for identifying the model parameters on a real-time basis and carry out the sliding mode control using the model parameters that are identified by the model parameter identifier.
The model parameter identifier detects an identifying error which is a difference between the output of the plant which is calculated using the identified model parameters and the actual output of the plant,

and corrects the model parameters in order to eliminate the identifying error. Therefore, regarding the model parameter identifier, the following problems may occur.
Due to nonlinear characteristics and disturbance whose average value is not "0", the identifying error does not become "0" even though substantially optimum model parameters have actually been obtained. Therefore, the model parameters which do not need to be corrected are occasionally corrected. As a result, a drift occurs in which the values of the model parameters gradually shift from their optimum values to some other values to make the control performed by the sliding mode controller unstable.
Conventionally known is an air-fuel ratio control system in which a controlled object model is defined by modeling a plant, i.e., a controlled object, and model parameters of the controlled object model are identified by a parameter adjusting device (for example, Japanese Patent Laid-open No. Hei 11-73206). According to this system, the air-fuel ratio of an air-fuel mixture to be supplied to the internal combustion engine is feedback-controlled to a target air-fuel ratio by a self-tuning regulator using the identified model parameters.
In the above control system, each model parameter is calculated by adding an initial value of the model parameter and an updating component which is calculated according to the identifying error of the model parameters.
In the above control system, the initial values of the model parameter is determined irrespective of the state quantity of the plant at the time of calculating a control input to the plant. Therefore, the time period which is necessary for the model parameters to converge on their optimum values becomes longer when the characteristics of the plant change in a relatively short time period, which results in insufficient controlling performance.
One known control system based on the sliding mode control theory is disclosed in Japanese Patent Laid-open No. Hei 9-274504, for

example. The publication shows that the stability of the sliding mode control is determined, and when the sliding mode control is determined to be unstable, the value calculated before the sliding mode control is determined to be unstable is used for a control input to the controlled object.
Another control system is known from Japanese Patent Laid-open No. Hei 11-93741. In this control system, the stability of the snaing mode control is determined, and when the sliding mode control is determined to be unstable, the control input to the controlled object is set to a predetermined value.
However, according to the above-de scribed conventional control systems, when the sliding mode control is determined to be unstable, the control input to the plant is fixed, which makes it impossible to converge an output of the plant to the control target value.
In the control system shown in Japanese Patent Laid-open No. Hei 11-73206 described above, the model parameters are calculated by adding updating components, which are calculated according to the identifying error of the model parameters, to the initial values of the model parameters.
According to this control system shown in Japanese Patent Laid-open No. Hei 11-73206, a drift of the model parameters due to disturbahces"Ts prevented. However, since the model parameters calculated by the parameter adjusting device are used per se in the self tuning regulator, there remains a room for improvement in maintaining stability of the self tuning regulator.
A control system in which model parameters of a controlled object model which is obtained by modeling a plant, is identified in real time, and the plant is controlled using the identified model parameters, is shown in Japanese Patent Laid-open 2000-179384.
The control system shown in this publication includes an identifier identifying the model parameters, an estimator estimating an output of the plant, and a sliding mode controller which calculates a

control input to the plant with the sliding mode control, using the model parameters identified by the identifier and the plant output estimated by the estimator. A difference between an output of the plant and a target value of the plant output, and a difference between an input of the plant and a reference value, are input to the identifier and the estimator. The reference value is variably set according to a manipulating quantity for manipulating the input of the plant. By setting the reference value variably according to the manipulating quantity, an effect of raising a

Since the throttle valve actuating device, which is a controlled object, has non-linear characteristics, a sufficient control performance in control accuracy, control stability, and a followup characteristic (convergence characteristic) of the throttle valve opening to a target value, is not obtained by the conventional PID control.
Disclosure of Invention
It is therefore an object of the present invention to provide a control system for a plant, which can control the plant more stably when

the model parameters of the controlled object model which are obtained by modeling the plant, which is a controlled object, are identified and the sliding mode control is performed using the identified model parameters. Another object of the present invention is to provide a control system for a plant, which can promptly converge one or more model parameter of the controlled object model on its optimum value to thereby maintain good controllability, when the characteristics of the plant changes in a relatively short time period.
Another object of the present invention is to provide a control system for a plant, which can continue the control of converging an output of the plant to the control target value and execute a suitable process for stabilizing a sliding mode control, when the sliding mode control is determined to be unstable, to thereby improve the stability of the sliding mode control.
^1108.,7 Another object of the present invention is to provide a
control system for a plant, which identifies model parameters of a controlled object model which is obtained by modeling the plant (i.e. the controlled object), and improve stability of the control using the model parameters.
■fc4&49 Another object of the present invention is to provide a
"control system for a plant, in which when using a controlled object model
which is defined using a reference value of the input or output of the
plant, the reference value is set by a widely applicable method, to
thereby improve control performance.
\"*3*96 ) Another object of the present invention is to provide a
i^c&ptrol system for a throttle valve actuating device, which can improve
control accuracy, control stability, and a follow-up characteristic of the
throttle valve opening to a target value.
To achieve the above object, the present invention provides a control system for a plant, comprising identifying means and a sliding mode controller. The identifying means identifies a model parameter vector (8) of a controlled object model of a plant which is obtained by

modeling the plant. The sliding mode controller controls the plant using the model parameter vector identified by the identifying means. The identifying means comprises an identifying error calculating means, an updating vector calculating means, and an updating vector correcting means. The identifying error calculating means calculates an identifying error (ide) of the model parameter vector. The updating vector calculating means calculates an updating vector (d9) according to the identifying error. The updating vector correcting means corrects the updating vector by multiplying a past value of at least one element of the updating vector by a predetermined value (DELTAi, EPSi) which is greater than "0" and less than "1". The identifying means calculates the model parameter vector by adding the corrected updating vector to a reference vector (0base, 6(0)) of the model parameter vector.
With this configuration, the updating vector is calculated according to the identifying error of the model parameter vector, and corrected by multiplying the past value of at least one element of the updating vector by the predetermined value which is greater than "0" and less than "1". The corrected updating vector is added to the reference vector of the model parameter vector to calculate the model parameter vector. Accordingly, values of the elements of the updating vector are limited, thus stabilizing the model parameter vector in the vicinity of the reference vector. As a result, the drift of the model parameters is prevented, to thereby improve the stability of the sliding mode control performed by the sliding mode controller.
Preferably, the updating vector correcting means does not multiply one of an element of the updating vector which is relevant to the input of the plant (an element relevant to the calculation of bl) and an element of the updating vector which is irrelevant to the input and the output of the plant (an element relevant to the calculation of cl), by the predetermined value. According to this configuration, the steady-state deviation which may be caused by correcting these elements can be prevented from occurring.

Preferably, the updating vector correcting multiplies at least one element of the reference vector (8(0)) by the predetermined value (EPSi). Also in this case, the drift of the model parameters is prevented, to thereby improve the stability of the sliding mode control performed by the sliding mode controller.
The present invention further provides a control system for a plant, comprising an identifying means and a sliding mode controller. The identifying means identifies a model parameter vector of a controlled object model which is obtained by modeling the plant, based on an input and an output of the plant. The sliding mode controller controls the plant using the model parameter vector identified by the identifying means. The identifying means comprises identifying error calculating means and identifying error correcting means. The identifying error calculating means calculates an identifying error (ide) of the model

identifying error (idenl) corrected by the identifying error correcting means.
With this configuration, if the identifying error is in the predetermined range, the identifying error is corrected in a decreasing direction, and the model parameter vector is calculated using the corrected identifying error. Therefore, the identifying error is suppressed to less accumulate in the model parameters, that is, the drift of the model parameters is prevented, to thereby improve the stability of the sliding mode control carried out by the sliding mode controller.
Preferably, the identifying error correcting means sets the identifying error to "0", if the identifying error is in the predetermined range. With this configuration, the effect of the identifying error which is not to be reflected to the values of model parameters is eliminated, resulting in an increased effect of preventing the model parameters from


appropriate. Accordingly, it is prevented that the identifying error which is to be reflected to the values of model parameters is reduced or neglected as an unnecessary error.
Preferably, the identifying means identifies the model parameter vector according to a fixed gain algorithm. With this configuration, the amount of calculations for identifying the model parameters can be reduced.
Preferably, the identifying error calculating means performs a lowpass filtering of the identifying error and outputs the identifying error after the low-pass filtering.
With this configuration, the model parameter vector is identified using the identifying error which has been processed by the low-pass filtering. Accordingly, the frequency characteristics of the controlled object model are made closer to the actual frequency characteristics of the plant, to thereby improve the robustness and stability of the control carried out by the sliding mode controller.
Preferably, the control system further comprises predicting means for calculating a predicted value (PREDTH) of the output of the plant. With this configuration, the predicted value of the output of the plant is calculated by the predicting means. Therefore, the plant which has a dead time element can be controlled accurately.
Preferably, the predicting means calculates the predicted value using the model parameter vector identified by the identifying means. With this configuration, it is possible to calculate an accurate predicted value even when the dynamic characteristics of the plant changes with time or changes according to environmental conditions.
Preferably, a control input applied from the sliding mode

controller to the plant includes an adaptive law input. With this configuration, good controllability can be obtained even in the presence of disturbance and/or a modeling error, which is a difference between the characteristics of the actual plant and the characteristics of the controlled object model.
Preferably, the plant includes a throttle valve actuating device (10) having a throttle valve (3) of an internal combustion engine and actuating means (6) for actuating the throttle valve, and the sliding mode controller calculates a parameter for determining a control input to be applied to the throttle valve actuating device to make an opening (TH) of the throttle valve coincide with a target opening (THR).
With this configuration, the sliding mode controller performs the control to make the opening of the throttle valve coincide with the target opening, using stable model parameters identified by the identifying means. Consequently, the controllability of the throttle valve opening to the target opening can be improved, and the throttle valve opening can be controlled more stably.
Preferably, the plant includes an engine system having an internal combustion engine and fuel supply means for supplying fuel to the engine, and the sliding mode controller calculates a parameter for determining a control input to be applied to the engine system to make an air-fuel ratio of an air-fuel mixture to be supplied to the engine coincide with a target air-fuel ratio.
With this configuration, the sliding mode controller performs the control to make the air-fuel ratio coincide with the target air-fuel ratio, using stable model parameters identified by the identifying means. Consequently, the controllability of the air-fuel ratio to the target air-fuel ratio can be improved, and the air-fuel ratio can be controlled more stably. -*#i48 ,y The present invention further provides a control system for" a plant, comprising identifying means and control means. The identifying means identifies at least one model parameter ( 6 ) of a controlled object model of a plant which is obtained by modeling the

plant. The control means controls the plant using the at least one model parameter identified by the identifying means. The identifying means comprises a reference model parameter calculating means, an identifying error calculating means, and an updating component calculating means. The reference model parameter calculating means calculates at least one reference model parameter (8 base) according to a state quantity (DTH) of the plant. The identifying error calculating means calculates an identifying error (ide) of the at least one model parameter- The updating component calculating means calculates at least one updating component (d 8) according to the identifying error (ide). The identifying means calculates the at least one model parameter by adding the at least one updating component to the at least one reference model parameter.
With this configuration, the updating component(s) is/are calculated according to the identifying error, and the model parameter(s) is/are calculated by adding the updating component(s) to the reference model parameter(s) which is/are calculated according to the state quantity of the plant. Accordingly, the model parameter(s) is/are calculated by means of correcting the reference model paranieter(s) corresponding to the always-changing state quantity of the plant, with the updating component(s), which makes it possible to converge the model parameter(s) more promptly compared with the conventional method.
Preferably, the state quantity of the plant is a parameter indicative of a change in the dynamic characteristic of the plant. The parameter indicative of a change in the dynamic characteristic of the plant may be an output (DTH, TH) of the plant, or a control target value (DTHR, THR) to which the output of the plant is controlled to converge. With this configuration, the reference model parameter(s) which reflect(s) the change in the dynamic characteristics of the plant can be obtained. This makes it possible to promptly converge the model parameter(s) especially when the plant includes a nonlinear element.
Preferably, the control means controls the plant with the sliding

mode control. With this configuration, good controllability can be obtained even in the presence of disturbance, a modeling error (a difference between the characteristics of the actual plant and the characteristics of the controlled object model), and/or a dead time (a delay between the input and output of the controlled object).
Preferably the control input applied from the control means to the plant includes an adaptive law input. With this configuration, better controllability can be obtained even in the presence of disturbance and/or the modeling error.
Preferably, the plant includes a throttle valve actuating device having a throttle valve of an internal combustion engine and actuating means for actuating the throttle valve, and the sliding mode controller calculates a parameter for determining a control input to be applied to the throttle valve actuating device to make an opening of the throttle valve coincide with a target opening.
With this configuration, the control means performs the control to make the opening of the throttle valve coincide with the target opening, using model parameters identified by the identifying means. Consequently, the controllability of the throttle valve opening to the target opening can be improved.
ir00^& The present invention further provides a control system
for a plant, including a sliding mode controller for controlling the plant with the sliding mode control. The sliding mode controller includes switching function value calculating means, equivalent control input calculating means, reaching law input calculating means, control input calculating means, stability determining means, and stabilizing means. The switching function value calculating means calculates a value (o) of a switching function defined as a linear function of a deviation between an output of the plant and a control target value. The equivalent control input calculating means calculates an equivalent control input (Ueq) that contributes to constraining a deviation state quantity on the switching straight line on which the value of the switching function

becomes zero. The deviation state quantity is defined based on the deviation between the output of the plant and the control target value. The reaching law input calculating means calculates a reaching law input (Urch) that contributes to placing the deviation state quantity onto the switching straight line. The control input calculating means calculates a control input (Usl) to the plant by adding at least the equivalent control input (Ueq) and the reaching law input (Urch). The stability determining means determines whether or not the sliding mode control is stable. The stabilizing means reduces an absolute value of the equivalent control input (Ueq) when the sliding mode control is determined to be unstable.
With this configuration, the control input to the plant is calculated using the equivalent control input that contributes to constraining the deviation state quantity on the switching straight line, and the reaching law input that contributes to placing the deviation state quantity onto the switching straight line. When the sliding mode control is determined to be unstable, the absolute value of the equivalent control input is reduced. The equivalent control input may be most greatly influenced when the sliding mode control becomes unstable. Therefore, the reduction of the absolute value of the equivalent control input makes it possible to stabilize the sliding mode control while continuing the control of converging an output of the plant to the control target value.
The present invention provides another control system for a plant, including a sliding mode controller for controlling the plant with a sliding mode control. The sliding mode controller includes switching function value calculating means for calculating a value ( o ) of a switching function defined as a linear function of a deviation between an output of the plant and a control target value, equivalent control input calculating means for calculating an equivalent control input (Ueq) that contributes to constraining a deviation state quantity which is defined based on the deviation between the output of the plant and the control

target value, on a switching straight line on which the value of the switching function becomes zero, reaching law input calculating means for calculating a reaching law input (Urch) that contributes to placing the deviation state quantity onto the switching straight line, control input calculating means for calculating a control input to the plant by adding at least the equivalent control input (Ueq) and the reaching law input (Urch), stability determining means for determining whether or not the sliding mode control is stable, and stabilizing means for changing a control gain (F) that is used in calculating the reaching law input (Urch), to a value (XKRCHSTB) that makes the sliding mode control more stable, when the sliding mode control is determined to be unstable.
With this configuration, the control input to the plant is calculated using the equivalent control input that contributes to constraining the deviation state quantity on the switching straight line, and the reaching law input that contributes to placing the deviation state quantity onto the switching straight line. When the sliding mode control is determined to be unstable, the control gain that is used in calculating the reaching law input, to a value that makes the sliding mode control more stable. Accordingly, it is possible to stabilize the sliding mode control while continuing the control of converging an output of the plant to the control target value.
Preferably, the sliding mode controller further includes adaptive law input calculating means for calculating an adaptive law input (Uadp) that contributes to placing the deviation state quantity onto the switching straight line, and the control input calculating means calculates the control input (Usl) to the plant by adding the equivalent control input (Ueq), the reaching law input (Urch), and the adaptive law input (Uadp).
With this configuration, the control input applied to the plant is calculated by further adding the adaptive law input to the equivalent control input and the reaching law input. Accordingly, better controllability can be obtained even in the presence of disturbance and/or

the modeling error (a difference between the characteristics of the actual plant and the characteristics of the controlled object model).
The present invention provides another control system for a plant, including a sliding mode controller for controlling the plant with a sliding mode control. The sliding mode controller includes switching function value calculating means for calculating a value ( o )of a switching function defined as a linear function of a deviation between an output of the plant and a control target value, equivalent control input calculating means for calculating an equivalent control input (Ueq) that contributes to constraining a deviation state quantity which is defined based on the deviation between the output of the plant and the control target value, on a switching straight line on which the value of the switching function becomes zero, reaching law input calculating means for calculating a reaching law input (Urch) that contributes to placing the deviation state quantity onto the switching straight line, adaptive law input calculating means for calculating an adaptive law input (Uadp) that contributes to placing the deviation state quantity onto the switching straight line, control input calculating means for calculating a control input to the plant by adding the equivalent control input (Ueq), the reaching law input (Urch), and the adaptive law input (Uadp), stability determining means for determining whether or not the sliding mode control is stable, and stabilizing means for changing a control gain (G) that is used in calculating the adaptive law input, to a value (XKADPSTB) that makes the sliding mode control more stable, when the sliding mode control is determined to be unstable.
With this configuration, the control input to the plant is calculated using the equivalent control input that contributes to constraining the deviation state quantity on the switching straight line, the reaching law input that contributes to placing the deviation state quantity onto the switching straight line, and the adaptive law input that contributes to placing the deviation state quantity onto the switching straight line. When the sliding mode control is determined to

be unstable, the control gain that is used in calculating the adaptive law input, to a value that makes the sliding mode control more stable. Accordingly, it is possible to stabilize the sliding mode control while continuing the control of converging an output of the plant to the control target value. Using the adaptive law input makes it possible to obtain good controllability even in the presence of disturbance and/or the modeling error, thereby stabilizing the sliding mode control while continuing the control of converging an output of the plant to the control target value.
The present invention provides another control system for a plant, including a sliding mode controller for controlling the plant with a sliding mode control. The sliding mode controller includes switching function value calculating means for calculating a value ( o ) of a switching function defined as a linear function of a deviation between an output of the plant and a control target value, stability determining means for determining whether or not the sliding mode control is stable, and stabilizing means for changing the switching functiq^(VPOLE^ so that a response speed of the sliding mode control may decrease, when the sliding mode control is determined to be unstable.
With this configuration, the switching function is changed so that a response speed of the sliding mode control may decrease, when the sliding mode control is determined to be unstable. Accordingly, it is possible to stabilize the sliding mode control while continuing the control of converging an output of the plant to the control target value.
Preferably, the stability determining means performs the stability determination, based on a value of the switching function. The tendency where the absolute value of the switching function increases indicates that the control becomes unstable. Therefore, by determining such tendency based on the value of the switching function, an unstable condition of the sliding mode control can easily be detected.
The present invention provides another control system for a plant, including identifying means for identifying at least one model

parameter of a controlled object model that is obtained by modeling the plant, based on an input and an output of the plant, and a sliding mode controller for controlling the plant with a sliding mode control, using the at least one model parameter identified by the identifying means. The sliding mode controller includes stability determining means for determining whether or not the sliding mode control is stable, and stabilizing means for inhibiting the use of a predetermined model parameter (bl), when the sliding mode control is determined to be unstable.
With this configuration, the use of the predetermined model parameter (bl) is inhibited, when the sliding mode control is determined to be unstable. The sliding mode control becomes unstable when the identification by the identifying means becomes unstable. Therefore, inhibiting the use of the predetermined model parameter that gives great influence to the control, makes it possible to stabilize the sliding mode control while continuing the control of converging an output of the plant to the control target value.
Preferably, the plant includes a throttle valve actuating device having a throttle valve of an internal combustion engine and actuating means for actuating the throttle valve, and the sliding mode controller calculates a parameter for determining a control input to be applied to the throttle valve actuating device to make the opening of the throttle valve coincide with a target opening.
With this configuration, the control of making the opening of the throttle valve coincide with the target opening is performed with the sliding mode control. Accordingly, controllability of the throttle opening to the target opening is improved. In addition, more stable control can be accomplished by the stabilizing means.
JhilOG The present invention provides another control system
for a plant, including identifying means for identifying a model parameter vector of a controlled object model which is obtained by modeling the plant, based on an input and an output of the plant, and a

sliding mode controller for controlling the plant using the model parameter vector identified by the identifying means. The identifying means includes identifying error calculating means for calculating an identifying error (ide) of the model parameter vector, updating vector calculating means for calculating an updating vector (d9) according to the identifying error, updating vector correcting means for correcting the updating vector so that an influence of the past value of the identifying error may decrease, model parameter vector calculating means for calculating the model parameter vector by adding the corrected updating vector to a reference vector (9 base, 6 (0)) of the model parameter vector, and limiting means for limiting a value of at least one element (al, a2, bl, cl) of the model parameter vector calculated by the model parameter vector calculating means, within a predetermined limit range.
With this configuration, the updating vector is calculated according to the identifying error of the model parameter vector, and corrected so that an influence of the past value of the identifying error may decrease. The corrected updating vector is added to the reference vector of the model parameter vector to calculate the model parameter vector. Further, the value of at least one element of the model parameter vector calculated by the model parameter vector calculating means is limited within the predetermined limit range. Accordingly, a drift of the model parameter(s) are prevented, and the control stability is further improved.
Preferably, the updating vector calculating means calculates the updating vector using a fixed gain algorithm. With this configuration, an amount of calculation can be reduced.
Preferably, the updating vector correcting means corrects the updating vector by multiplying a past value of at least one element of the updating vector by a predetermined value (DELTAi, EPSi) which is greater than 0 and less than 1. With this configuration, an influence of the past value of the identifying error is reduced, and the drift of the model parameter(s) is prevented.

Preferably, the updating vector correcting means does not multiply one of an element (an element relating to the calculation of bl) of the updating vector which is relevant to the input of the plant and an element (an element relating to the calculation of cl) of the updating vector which is irrelevant to the input and the output of the plant, by the predetermined value. According to this configuration, the steady-state deviation which may be caused by correcting these elements can be prevented from occurring.
Preferably, the updating vector correcting means multiplies at least one element of the reference vector (8(0)) by the predetermined value. According to this configuration, the drift of the model parameters is prevented.
Preferably, the reference vector is calculated according to a parameter (DTH) indicative of a change in a dynamic characteristic of the plant. This makes it possible to promptly converge the model parameter(s) especially when the plant includes a nonlinear element.
The present invention provides another control system for a plant, including identifying means for identifying a model parameter vector of a controlled object model which is obtained by modeling the plant, based on an input and an output of the plant, and a sliding mode controller for controlling the plant using the model parameter vector identified by the identifying means. The identifying means includes identifying error calculating means for calculating an identifying error (ide) of the model parameter vector, identifying error correcting means for correcting the identifying error in a decreasing direction, if the identifying error is in a predetermined range (-EIDNRLMT 1& ide ^ EIDNRLMT), model parameter vector calculating means for calculating the model parameter vector using the identifying error (idenl) corrected by the identifying error correcting means, and limiting means for limiting a value of at least one element of the model parameter vector calculated by the model parameter vector calculating means, within a predetermined limit range.

With this configuration, if the identifying error is in the predetermined range, the identifying error is corrected in a decreasing direction, and the model parameter vector is calculated using the corrected identifying error. Further, the value of at least one element of the model parameter vector within the predetermined limit range. Therefore, the drift of the model parameters is prevented, to thereby improve the control stability.
Preferably, the limiting means limits values of a plurality of elements (al, a2) of the model parameter vector so that the plurality of elements satisfies a predetermined relation (FIG. 18).
With this configuration, the values of a plurality of elements of the model parameter vector is limited so that the plurality of elements satisfies a predetermined relation. Accordingly, stability of the control using the model parameter vector can be improved.
Preferably, the identifying error correcting means sets the identifying error (ide) to "0", if the identifying error (ide) is in the predetermined range (-EIDNRLMT ^ ide ^EIDNRLMT).
Preferably, the plant includes a throttle valve actuating device having a throttle valve of an internal combustion engine and actuating means for actuating the throttle valve, and the sliding mode controller calculates a parameter for determining a control input to be applied to the throttle valve actuating device to make the opening of the throttle valve coincide with a target opening.
With this configuration, the control to make the opening of the
throttle valve coincide with the target opening is performed using model
parameters identified by the identifying means. Consequently, the
controllability of the throttle valve opening to the target opening can be
improved, and the throttle valve opening can be controlled more stably.
"^WSf Further, the present invention provides another control
system for a plant, including identifying means for identifying a plurality of model parameters of a controlled object model which^-is obtained by modeling the plant using a predetermined reference value (THDEFL-the

plurality of model parameters including a model parameter (cl) which is irrelevant to an input and an output of the plant, control means for controlling the plant using the model parameters identified by the identifying means, and correcting means for correcting the predetermined reference value (THDEF) according to the model parameter (cl) which is irrelevant to an input and an output of the plant.
With this configuration, the plurality of model parameters includes a model parameter which is irrelevant to an input and an output of the plant, and the predetermined reference value is corrected according to the model parameter which is irrelevant to an input and an output of the plant. Since the reference value is corrected by the model parameter itself of the controlled object model, the present invention is applicable to any control system which employs any control method. Further, the reference value of the controlled object model, which has a direct influence on the modeling error (a difference between the characteristics of the actual plant and the characteristics of the controlled object model), can be made coincide with an actual reference value of the plant. Accordingly, the modeling error can be reduced and the control performance can be improved.
Preferably, the correcting means calculates a correction value (thdefadp) by statistically processing the model parameter (cl) which is irrelevant to an input and an output of the plant, and corrects the predetermined reference value (THDEF) with the correction value.
Preferably, the controlled object model is defined by at least one first model parameter (al, a2) which is relevant to an output of the plant, a second model parameter (bl) which is relevant to a control input of the plant, and a third model parameter (cl) which is irrelevant to the output and the control input of the plant.
Preferably, the plant includes a throttle valve actuating device (10) having a throttle valve (3) of an internal combustion engine and actuating means (6) for actuating the throttle valve, and the control means calculates a parameter (DUT) for determining a control input to

be applied to the throttle valve actuating device (10) to make the opening (TH) of the throttle valve coincide with a target opening (THR).
Preferably, the throttle valve actuating device (10) includes a first energizing means (4) for energizing the throttle valve in a closing direction, and a second energizing means (5) for energizing the throttle valve in an opening direction, and maintains the throttle valve at a default opening (THDEF) by means of the first and second energizing means (4, 5), when the throttle valve is not actuated by the actuating means (6). The predetermined reference value is set to the default pjjaQM(THDEF).
"^,14$)&"" The present invention provides another control system
for a throttle valve actuating device including a throttle valve of an internal combustion engine and actuating means for actuating said throttle valve. The control system includes identifying means for identifying at least one model parameter of a controlled object model which is obtained by modeling said throttle valve actuating device, and control means for controlling an opening of said throttle valve to a target opening according to the at least one model parameter identified by said identifying means. The identifying means includes identifying error calculating means for calculating an identifying error (ide) of the at least one model parameter, and identifying error correcting means for correcting the identifying error in a decreasing direction, if the identifying error is in a predetermined range (-EIDNRLMT ^ ide ^ EIDNRLMT). The identifying means calculates the at least one model parameter using the identifying error corrected by said identifying error correcting means, and the predetermined range is set according to a value (DDTHRSQA) depending on the opening (TH) of said throttle valve or the target opening (THR).
With this configuration, the identifying error is corrected in a decreasing direction, if the identifying error is in a predetermined range, and the at least one model parameter is calculated using the corrected identifying error. Accordingly, an integration of the identifying error to

the at least one model parameter is suppressed, which results in preventing a drift of the model parameter and improving a control stability.
Preferably, the control system further includes predicting means for calculating a predicted value (PREDTH) of a future throttle valve opening, using the at least one model parameter identified by said identifying means.
With this configuration, the predicted value of a future throttle valve opening is predicted using the at least one model parameter identified by said identifying means. Accordingly, it is possible to calculate an accurate predicted value even when the dynamic characteristics of the plant changes with time or changes according to environmental conditions.
Preferably, said control means controls said throttle valve actuating device with a sliding mode control, using the at least one model parameter identified by said identifying means. According to this configuration, the controllability of the throttle valve opening to the target opening can be improved even in the presence of a modeling error (a difference between the characteristics of the actual plant and the characteristics of the controlled object model), and the throttle valve opening can be controlled more stably.

Accordingly the present invention provides a control system for a plant, comprising: identifying means for identifying at least one mode] parameter of a controlled object model which is obtained by modeling said plant, based on an input and an output of said plant; and control means for controlling said plant using the at least one model parameter identified by said identifying means; said identifying means comprising: reference model parameter calculating means for calculating at least one reference model parameter according to a state quantity of said plant; identifying error calculating means for calculating an identifying error of the at least one model parameter; and updating component calculating means for calculating at least one updating component according to the identifying error, wherein said identifying means calculates the at least one model parameter by adding the at least one updating component to the at least one reference model parameter.
With reference to the accompanying drawings, in which :
FIG. 1 is a schematic view of a throttle valve control system according to a first embodiment of the present invention;
FIGS. 2A and 2B are diagrams showing frequency characteristics of the throttle valve actuating device shown in FIG. 1;
FIG. 3 is a functional block diagram showing functions realized by an electronic control unit (ECU) shown in FIG.l;
FIG. 4 is a diagram showing the relationship between control characteristics of a sliding mode controller and the value of a switching function setting parameter (VPOLE);

FIG. 5 is a diagram showing a range for setting control gains (F, G) of the sliding mode controller;
FIGS. 6A and 6B are diagrams illustrative of a drift of model parameters;
FIGS. 7A through 7C are diagrams showing functions for correcting an identifying error;
FIG. 8 is a diagram illustrating that a default opening deviation of a throttle valve is reflected to a model parameter (&")",
FIG. 9 is a flowchart showing a throttle valve opening control process;
FIG. 10 is a flowchart showing a process of setting state variables in the process shown in FIG. 9;
FIG. 11 is a flowchart showing a process of performing calculations of a model parameter identifier in the process shown in FIG.
9;
FIG. 12 is a flowchart showing a process of calculating an identifying error (ide) in the process shown in FIG. 11;
FIGS. 13Aand 13B are diagrams illustrative of a process of low-pass filtering on the identifying error (ide);
FIG. 14 is a flowchart showing the dead zone process in the process shown in FIG. 12;
FIG. 15 is a diagram showing a table used in the process shown in FIG. 14;
FIG. 16 is a flowchart showing a process of stabilizing a. model parameter vector (9) in the process shown in FIG. 11;
FIG. 17 is a flowchart showing a limit process of model parameters (al\ a2") in the process shown in FIG. 16;
FIG. 18 is a diagram illustrative of the change in the values of the model parameters in the process shown in FIG. 16;
FIG. 19 is a flowchart showing a limit process of a model parameter (bl") in the process shown in FIG. 16;
FIG. 20 is a flowchart showing a limit process of a model

parameter (clO in the process shown in FIG. 16;
FIG. 21 is a flowchart showing a process of performing calculations of a state predictor in the process shown in FIG. 9,"
FIG. 22 is a flowchart showing a process of calculating a control input (Usl) in the process shown in FIG. 9;
FIG. 23 is a flowchart showing a process of calculating a predicted switching function value (opre) in the process shown in FIG.
22;
FIG. 24 is a flowchart showing a process of calculating the switching function setting parameter (VPOLE) in the process shown in FIG. 23;
FIGS. 25A through 25C are diagrams showing maps used in the process shown in FIG. 24;
FIG. 26 is a flowchart showing a process of calculating an integrated value of the predicted switching function value (opre) in the process shown in FIG. 22;
FIG. 27 is a flowchart showing a process of calculating a reaching law input (Urch) in the process shown in FIG. 22;
FIG. 28 is a flowchart showing a process of calculating an adaptive law input (Uadp) in the process shown in FIG. 22;
FIG. 29 is a flowchart showing a process of determining the stability of the sliding mode controller in the process shown in FIG. 9i
FIG. 30 is a flowchart showing a process of calculating a default opening deviation (thdefadp) in the process shown in FIG. 9;
FIG. 31 is a functional block diagram showing functions realized by the electronic control unit (ECU) shown in FIG. 1 according to a second embodiment of the present invention;
FIG. 32 is a flowchart showing a throttle valve opening control process according to the second embodiment;
FIG. 33 is a flowchart showing a process of performing calculations of a model parameter identifier in the process shown in FIG. 32:

FIG. 34 is a diagram showing a table used in the process shown in FIG. 33;
FIG. 35 is a flowchart showing a process of calculating an identifying error (ide) in the process shown in FIG. 33;
FIG. 36 is a flowchart showing a process of calculating a control input (UsI) in the process shown in FIG. 32;
FIG. 37 is a flowchart showing a process of calculating a switching function value (a) in the process shown in FIG. 36;
FIG. 38 is a flowchart showing a process of calculating an integrated value of the switching function value (a) in the process shown in FIG. 36;
FIG. 39 is a flowchart showing a process of calculating a reaching law input (Urch) in the process shown in FIG. 36;
FIG. 40 is a flowchart showing a process of calculating an adaptive law input (Uadp) in the process shown in FIG. 36;
FIG. 41 is a flowchart showing a process of determining the stability of a sliding mode controller in the process shown in FIG. 32;
FIG. 42 is a block diagram of a control system according to a third embodiment of the present invention;
FIG. 43 is a block diagram of a modification of the control system shown in FIG. 42;
FIG. 44 is a block diagram of a control system according to a fourth embodiment of the present invention;
FIG. 45 is a block diagram of a modification of the control system shown in FIG. 44.
FIG. 46 is a block diagram of a control system according to a fifth embodiment of the present invention", and
FIG. 47 is a block diagram of a control system according to a sixth embodiment of the present invention!
Best Mode for Carrying Out the Invention a control system according to a fourth embodiment of the present invention!

First Embodiment
FIG. 1 schematically shows a configuration of a throttle valve control system according to a first embodiment of the present invention. An internal combustion engine (hereinafter referred to as "engine") 1 has an intake passage 2 with a throttle valve 3 disposed therein. The throttle valve 3 is provided with a return spring 4 as a first energizing means for energizing the throttle valve 3 in a closing direction, and a resilient member 5 as a second energizing means for energizing the throttle valve 3 in an opening direction. The throttle valve 3 can be actuated by a motor 6 as an actuating means through gears (not shown). When the actuating force from the motor 6 is not applied to the throttle valve 3, an opening TH of the throttle valve 3 is maintained at a default opening THDEF (for example, 5 degrees) where the energizing force of the return spring 4 and the energizing force of the resilient member 5 are in equilibrium.
The motor 6 is connected to an electronic control unit (hereinafter referred to as "ECU") 7. The operation of the motor 6 is controlled by the ECU 7. The throttle valve 3 is associated with a throttle valve opening sensor 8 for detecting the throttle valve opening TH. A detected signal from the throttle valve opening sensor 8 is supplied to the ECU 7.
Further, the ECU 7 is connected to an acceleration sensor 9 for detecting a depression amount ACC of an accelerator pedal to detect an output demanded by the driver of the vehicle on which the engine 1 is mounted. A detected signal from the acceleration sensor 9 is supplied to the ECU 7.
The ECU 7 has an input circuit, an A/D converter, a central processing unit (CPU), a memory circuit, and an output circuit. The input circuit is supplied with detected signals from the throttle valve opening sensor 8 and the acceleration sensor 9. The A/D converter converts input signals into digital signals. The CPU carries out various
process operations. The memory circuit has a ROM (read only memory) for storing processes executed by the CPU, and maps and tables that are referred to in the processes, a RAM for storing results of executing processes by the CPU. The output circuit supplies an energizing current to the motor G. The ECU 7 determines a target opening THR of the throttle valve 3 according to the depression amount ACC of the accelerator pedal, determines a control quantity DUT for the motor 6 in order to make the detected throttle valve opening TH coincide with the target opening THR, and supplies an electric signal according to the control quantity DUT to the motor 6.
In the present embodiment, a throttle valve actuating device 10 that includes the throttle valve 3, the return spring 4, the resilient member 5, and the motor 6 is a controlled object. An input to be applied to the controlled object is a duty ratio DUT of the electric signal applied to the motor 6. An output from the controlled object is the throttle valve opening TH detected by the throttle valve opening sensor 8.
When frequency response characteristics of the throttle valve actuating device 10 are measured, gain characteristics and phase characteristics indicated by the solid lines in FIGS. 2A and 2B are obtained. A model defined by the equation (1) shown below is set as a controlled object model. Frequency response characteristics of the model are indicated by the broken-line curves in FIGS. 2A and 2B. It has been confirmed that the frequency response characteristics of the model are similar to the characteristics of the throttle valve actuating device 10.

where k is a parameter representing discrete time, and DTH(k) is a throttle valve opening deviation amount defined by the equation (2) shown below. DTH(k+l) is a throttle valve opening deviation amount at a discrete time (k+1).
DTH(k) = TH(k) - THDEF (2)

where TH is a detected throttle valve opening, and THDEF is the default opening.
In the equation (l), al, a2, bl, and cl are parameters determining the characteristics of the controlled object model, and d is a dead time. The dead time is a delay between the input and output of the controlled object model.
The model defined by the equation (l) is a DARX model (delayed auto regressive model with exogeneous input) of a discrete time system, which is employed for facilitating the application of an adaptive control.
In the equation (l), the model parameter cl which is irrelevant to the input and output of the controlled object, in addition to the model parameters al and a2 which are relevant to the output deviation amount DTH and the model parameter bl which is relevant to the input duty ratio DUT. The model parameter cl is a parameter representing a deviation amount of the default opening THDEF and disturbance applied to the throttle valve actuating device 10. In other words, the default opening deviation amount and the disturbance can be identified by identifying the model parameter cl simultaneously with the model parameters al, a2, and bl by a model parameter identifier.
FIG. 3 is a functional block diagram of the throttle valve control system which is realized by the ECU 7. The throttle valve control system as configured includes, an adaptive sliding mode controller 21, a model parameter identifier 22, a state predictor 23 for calculating a predicted throttle valve opening deviation amount (hereinafter referred to as "predicted deviation amount" or PREDTH(k)) where PREDTH(k) (= DTH(k+d)) after the dead time d has elapsed, and a target opening setting unit 24 for setting a target opening THR for the throttle valve 3 according to the accelerator pedal depression amount ACC.
The adaptive sliding mode controller 21 calculates a duty ratio DUT according to an adaptive sliding mode control in order to make the detected throttle valve opening TH coincide with the target opening THR, and outputs the calculated duty ratio DUT.

By using the adaptive sliding mode controller 21, it is possible to change the response characteristics of the throttle valve opening TH to the target opening THR, using a specific parameter (VPOLE). As a result, it is possible to avoid shocks at the time the throttle valve 3 moves from an open position to a fully closed position, i.e., at the time the throttle valve 3 collides with a stopper for stopping the throttle valve 3 in the fully closed position. It is also possible to make the engine response corresponding to the operation of the accelerator pedal variable. Further, it is also possible to obtain a good stability against errors of the model parameters.
The model parameter identifier 22 calculates a corrected model parameter vector 9L (0LT = [al, a2, bl, cl]) and supplies the calculated corrected model parameter vector 6L to the adaptive sliding mode controller 21. More specifically, the model parameter identifier 22 calculates a model parameter vector 6 based on the throttle valve opening TH and the duty ratio DUT. The model parameter identifier 22 then carries out a limit process of the model parameter vector 6 to calculate the corrected model parameter vector 6L, and supplies the corrected model parameter vector 6L to the adaptive sliding mode controller 21. In this manner, the model parameters al, a2, and bl which are optimum for making the throttle valve opening TH follow up the target opening THR are obtained, and also the model parameter cl indicative of disturbance and a deviation amount of the default opening THDEF is obtained.
By using the model parameter identifier 22 for identifying the model parameters on a real-time basis, adaptation to changes in engine operating conditions, compensation for hardware characteristics variations, compensatation for power supply voltage fluctuations, and adaptation to aging-dependent changes of hardware characteristics are possible.
The state predictor 23 calculates a throttle valve opening TH (predicted value) after the dead time d has elapsed, or more specifically a

predicted deviation amount PREDTH, based on the throttle valve opening TH and the duty ratio DUT, and supplies the calculated deviation amount PREDTH to the adaptive sliding mode controller 21. By using the predicted deviation amount PREDTH, the robustness of the control system against the dead time of the controlled object is ensured, and the controllability in the vicinity of the default opening THDEF where the dead time is large is improved.
Next, principles of operation of the adaptive sliding mode controller 21 will be hereinafter described.
First, a target value DTHR(k) is defined as a deviation amount between the target opening THR(k) and the default opening THDEF by the following equation (3).
DTHR(k) = THR(k) - THDEF (3)
If a deviation e(k) between the throttle valve opening deviation amount DTH and the target value DTHR is defined by the following equation (4), then a switching function value o(k) of the adaptive sliding mode controller is set by the following equation (5).
e(k) = DTH(k) - DTHR(k) (4)
o(k) = e(k) + VPOLE x e(k-l) (5)
= (DTH(k) - DTHR(k))
+ VPOLE x (DTH(k-l) - DTHR(k-l)) where VPOLE is a switching function setting parameter that is set to a value which is greater than - 1 and less than 1.
On a phase plane defined by a vertical axis representing the deviation e(k) and a horizontal axis representing the preceding deviation e(k-l), a pair of the deviation e(k) and the preceding deviation e(k-l) satisfying the equation of "a(k) = 0" represents a straight line. The straight line is generally referred to as a switching straight line. A sliding mode control is a control contemplating the behavior of the deviation e(k) on the switching straight line. The sliding mode control is carried out so that the switching function value a(k) becomes 0, i.e., the pair of the deviation e(k) and the preceding deviation e(k-l) exists on the

switching straight line on the phase plane, to thereby achieve a robust control against disturbance and the modeling error (the difference between the characteristics of an actual plant and the characteristics of a controlled object model). As a result, the throttle valve opening deviation amount DTH is controlled with good robustness to follow up the target value DTHR.
As shown in FIG. 4, by changing the value of the switching function setting parameter VPOLE in the equation (5), it is possible to change damping characteristics of the deviation e(k), i.e., the follow-up characteristics of the throttle valve opening deviation amount DTH to follow the target value DTHR. Specifically, if VPOLE equals -1, then the throttle valve opening deviation amount DTH completely fails to follow up the target value DTHR. As the absolute value of the switching function setting parameter VPOLE is reduced, the speed at which the throttle valve opening deviation amount DTH follows up the target value DTHR increases.
The throttle valve control system is required to satisfy the following requirements Al andA2:
Al) When the throttle valve 3 is shifted to the fully closed position, collision of the throttle valve 3 with the stopper for stopping the throttle valve 3 in the fully closed position should be avoided; and
A2) The controllability with respect to the nonlinear characteristics in the vicinity of the default opening THDEF (a change in the resiliency characteristics due to the equilibrium between the energizing force of the return spring 4 and the energizing force of the resilient member 5, backlash of gears interposed between the motor 6 and the throttle valve 3, and a dead zone where the throttle valve opening does not change even when the duty ratio DUT changes) should be improved.
Therefore, it is necessary to lower the speed at which the deviation e(k) converges, i.e., the converging speed of the deviation e(k), in the vicinity of the fully closed position of the throttle valve, and to

increase the converging speed of the deviation e(k) in the vicinity of the default opening THDEF.
According to the sliding mode control, the converging speed of e(k) can easily be changed by changing the switching function setting parameter VPOLE. Therefore in the present embodiment, the switching function setting parameter VPOLE is set according to the throttle valve opening TH and an amount of change DDTHR (= DTHR(k) ■ DTHR(k-l» of the target value DTHR, to thereby satisfy the requirements A1 andA2.
As described above, according to the sliding mode control, the deviation e(k) is converged to 0 at an indicated converging speed and robustly against disturbance and the modeling error by constraining the pair of the deviation e(k) and the preceding deviation e(k-l) on the switching straight line (the pair of e(k) and e(k-l) will be hereinafter referred to as "deviation state quantity"). Therefore, in the sliding mode control, it is important how to place the deviation state quantity onto the switching straight line and constrain the deviation state quantity on the switching straight line.
From the above standpoint, an input DUT(k) (also indicated as Usl(k)) to the controlled object (an output of the controller) is expressed as the sum of an equivalent control input Ueq(k), a reaching law input Urch(k), and an adaptive law input Uadp(k), as indicated by the following equation (6).
DUT(k) = Usl(k)
= Ueq(k) + Urch(k) + Uadp(k) (6)
The equivalent control input Ueq(k) is an input for constraining the deviation state quantity on the switching straight line. The reaching law input Urch(k) is an input for placing deviation state quantity onto the switching straight line. The adaptive law input Uadp(k) is an input for placing deviation state quantity onto the switching straight line while reducing the modeling error and the effect of disturbance. Methods of calculating these inputs Ueq(k), Urch(k),

and Uadp(k) will be described below.
Since the equivalent control input Ueq(k) is an input for constraining the deviation state quantity on the switching straight line, a condition to be satisfied is given by the following equation (7):
a(k) = a(k+l) (7)
Using the equations (l), (4), and (5), the duty ratio DUT(k) satisfying the equation (7) is determined by the equation (9) shown below. The duty ratio DUT(k) calculated with the equation (9) represents the equivalent control input Ueq(k). The reaching law input Urch(k) and the adaptive law input Uadp(k) are defined by the respective equations (10) and (11) shown below.

where F and G respectively represent a reaching law control gain and an adaptive law control gain, which are set as described below, and AT represents a control period.
Calculating the equation (9) requires a throttle valve opening deviation amount DTH(k+d) after the elapse of the dead time d and a corresponding target value DTHR(k+d+l). Therefore, the predicted deviation amount PREDTH(k) calculated by the state predictor 23 is used as the throttle valve opening deviation amount DTH(k+d) after the elapse of the dead time d, and the latest target value DTHR is used as the target value DTHR(k+d+l).
Next, the reaching law control gain F and the adaptive law

control gain G are determined so that the deviation state quantity can stably be placed onto the switching straight line by the reaching law input Urch and the adaptive law input Uadp.
Specifically, a disturbance V(k) is assumed, and a stability
condition for keeping the switching function value a(k) stable against the
disturbance V(k) are determined to obtain a condition for setting the
gains F and G. As a result, it has been obtained as the stability
condition that the combination of the gains F and G satisfies the
following equations (12) through (14), in other words, the combination of
the gains F and G should be located in a hatched region shown in FIG. 5.
F > 0 (12)
G > 0 (13)
F As described above, the equivalent control input Ueq(k), the reaching law input Urch(k), and the adaptive law input Uadp(k) are calculated from the equations (9) through (ll), and the duty ratio DUT(k) is calculated as the sum of those inputs.
The model parameter identifier 22 calculates a model parameter vector of the controlled object model, based on the input (DUT(k)) and output (TH(k)) of the controlled object, as described above. Specifically, the model parameter identifier 22 calculates a model parameter vector 6(k) according to a sequential identifying algorithm (generalized sequential method-of-least-squares algorithm) represented by the following equation (15).
6(k) = e(k-l) + KHk)ide(k) (15)
e(k)T=[al", a2\ bl", cl"] (16)
where al", a2", bl", cl" represent model parameters before a limit process described later is carried out, ide(k) represents an identifying error defined by the equations (17), (18), and (19) shown below, where DTHFfAT(k) represents an estimated value of the throttle valve opening deviation amount DTH(k) (hereinafter referred to as "estimated throttle valve opening deviation amount") which is calculated using the latest

model parameter vector 6(k-l), and KP(k) represents a gain coefficient vector defined by the equation (20) shown below. In the equation (20), P(k) represents a quartic square matrix calculated from the equation (21)
shown below.

In the present embodiment, it is required that the following requirements Bl, B2, and B3 are satisfied:

Bl) Adaptation to quasi-static dynamic characteristics changes and hardware characteristics variations
"Quasi-static dynamic characteristics changes" mean slow-rate charactei"istics changes such as power supply voltage fluctuations or hardware degradations due to aging.
B2) Adaptation to high-rate dynamic characteristics changes
Specifically, this means adaptation to dynamic characteristics changes depending on changes in the throttle valve opening TH.
B3) Prevention of a drift of model parameters
The drift, which is an excessive increase of the absolute values of model parameters, should be prevented. The drift of model parameters is caused by the effect of the identifying error, which should not be reflected to the model parameters, due to nonlinear characteristics of the controlled object.
In order to satisfy the requirements Bl and B2, the coefficients kl and X2 are set respectively to a given value X and "0" so that the weighted Method-of-least-squares algorithm is employed.
Next, the drift of model parameters will be described below. As shown in FIG. 6A and FIG. 6B, if a residual identifying error, which is caused by nonlinear characteristics such as friction characteristics of the throttle valve, exists after the model parameters have been converged to a certain extent, or if a disturbance whose average value is not zero is steadily applied, then residual identifying errors are accumulated, causing a drift of model parameters.
Since such a residual identifying error should not be reflected to the values of model parameters, a dead zone process is carried out using a dead zone function Fnl as shown in FIG. 7A. Specifically, a co»rected identifying error idenKk) is calculated from the following equation (23), and a model parameter vector 8(k) is calculated using the corrected identifying error idenKk). That is, the following equation (15a) is used instead of the above equation (15). In this manner, the requirement B3) can be satisfied.

idenl(k) - FnKide(k)) (23)
8(k) = e(k-l) + KP(k)idenKk) (15a)
The dead zone function Fnl is not limited to the function shown in FIG. 7A. A discontinuous dead zone function as shown in FIG. 7B or an incomplete dead zone function as shown in FIG. 7C may be used as the dead zone function Fnl. However, it is impossible to completely prevent the drift if the incomplete dead zone function is used.
The amplitude of the residual identifying error changes according to an amount of change in the throttle valve opening TH. In the present embodiment, a dead zone width parameter EIDNRLMT which defines the width of the dead zone shown in FIGS. 7A through 7C is set according to the square average value DDTHRSQA of an amount of change in the target throttle valve opening THR. Specifically, the dead zone width parameter EIDNRLMT is set such that it increases as the square average value DDTHRSQA increases. According to such setting of the dead zone width parameter EIDNRLMT, it is prevented to neglect an identifying error to be reflected to the values of the model parameters as the residual identifying error. In the following equation (24), DDTHR represents an amount of change in the target throttle valve opening THR, which is calculated from the following equation (25):

Since the throttle valve opening deviation amount DTH is controlled to the target value DTHR by the adaptive sliding mode controller 21, the target value DTHR in the equation (25) may be changed to the throttle valve opening deviation amount DTH. In this case, an amount of change DDTH in the throttle valve opening deviation amount DTH may be calculated, and the dead zone width parameter EIDNRLMT may be set according to the square average value

DDTHRSQA obtained by replacing DDTHR in the equation (24) with DDTH.
For further improving the robustness of the control system, it is effective to further stabilize the adaptive sliding mode controller 21. In the present embodiment, the elements al", a2\ bl\ and cl" of the model parameter vector 8(k) calculated from the equation (15) are subjected to the limit process so that a corrected model parameter vector 6L(k) (8L(k)T = [al, a2, bl, ell) is calculated. The adaptive sliding mode controller 21 performs a sliding mode control using the corrected model parameter vector 9L(k). The limit process will be described later in detail referring to the flowcharts.
Next, a method for calculating the predicted deviation amount PREDTH in the state predictor 23 will be described below.
First, matrixes A. B and vectors X(k), U(k) are defined according to the following equations (26) through (29).

By rewriting the equation (1) which defines the controlled object model, using the matrixes A. B and the vectors X(k). U(k). the following equation (30) is obtained.
X(k+l)=AX(k) + BU(k-d) , ■.■ • " (30)
Determining X(k+d) from the equation (30). the following equation (31) is obtained.


If matrixes A" and B" are defined by the following equations (32). (33), using the model parameters al\ a2". bl\ and cl" which are not subjected to the limit process, a predicted vector XHAT(k-d) is given by the following equation (34).

The first-row element DTHHATXk-d) of the predicted v^-.r XHAT"k-rd; corresponds to :he predicted deviation amount PREDTn £. and is given by uhe following equation (35). PREDTH(k) = DTHHAT(k-d)
= al x DTH(k) + a2 x DTH(k-l)
+ pi x DUT(k-l) -h (32 x DUTlk-2)+ ■ - ■
+ [3d x DUT(k-d) + yl + y2 + ■ ■ ■ + yd (3o)
where al represents a first-row, first-column element of the matrix A"-. ct2 represents a first-row. second-column element of the matrix A"d. pi represents a first-row. first-column element of the matrix A"d";B\ and 71

represents a first-row, second-column element of the matrix A"dvB".
By applying the predicted deviation amount PREDTH(k) calculated from the equation (35) to the equation (9), and replacing the target values DTHRfk+d+1). DTHROt+d), and DTHR(k+d-l) respectively with DTHR(k). DTHR(k-l), and DTHR(k-2), the following equation (9a) is obtained. From the equation (9a), the equivalent control input Ueqfk) is calculated.

Using the predicted deviation amount PREDTH(k) calculated from the equation "35). a predicted switching function value opre(k) is defined by the following equation (36). The reaching law input Urch(k) and the adaptive law input Uadp(k) are calculated respectively from the following equation.- " iOa." and (lla).

The model parameter cl" is a paz"ameter representing a deviation of the default opening THDEF and disturbance. Therefore, as shown in FIG. 8. the model parameter cl" changes with disturbance, but can be regarded as substantially constant in a relatively short period. In the present embodiment, the model parameter cl" is statistically

processed, and the central value of its variations is calculated as a default opening deviation, thdefadp. The default opening deviation thdefadp is used for calculating the throttle valve opening deviation amount DTH and the target value DTHR.
Generally, the method of least squares is known as a method of the statistic process. In the statistic process according to the method of least squares, all data, i.e., all identified parameters cl\ obtained in a certain period are stored in a memory and the stored data is subjected to a batch calculation of the statistic process at a certain timing. However, the batch calculation requires a memory having a large storage capacity for storing all data, and an increased amount of calculations are necessary because inverse matrix calculations are required.
Therefore, according to the present embodiment, the sequential method-oHeast-squares algorithm for adaptive control which is indicated by the equations (15) through (21) is applied to the statistic process, and the central value of the least squares of the model parameter cl is calculated as the default opening deviation thdefadp.
Specifically, in the equations (15) through (21), by replacing 6(k) and 8(k)T with thdefadp. replacing "c(k) and t(k)T with "1". replacing ide(k) with ecKk). replacing KP(kj with KPTH.l and },2 respectively with >.l" and >.2". the following equations (37) through (40) are obtained.

One of the four algorithms described above can be selected according to the setting of the coefficients A.1" and k"2". In the equation

(39), the coefficient XI" is set to a given value other than 0 or 1, and the coefficient X2" is set to 1, thus employing the weighted method of least squares.
For the calculations of the equations (37) through (40), the values to be stored are thdefadp(k+l) and PTh(k+l) only, and no inverse matrix calculations are required. Therefore, by employing the sequential method-of-least-squares algorithm, the model parameter cl can be statistically processed according to the method of least squares while overcoming the shortcomings of a general method of least squares.
The default opening deviation thdefadp obtained as a result of the statistic process is applied to the equations (2) and (3), and the throttle valve opening deviation amount DTH(k) and the target value DTHR(k) are calculated from the following equations (41) and (42) instead of the equations (2) and (3).
DTH(k) - TH(k) • THDEF + thdefadp (41)
DTHR(k) = THR(k) ■ THDEF + thdefadp (42)
Using the equations (41) and (42), even when the default opening THDEF is shifted from its designed value due to characteristic variations or aging of the hardware, the shift can be compensated to perform an accurate control process.
Operation processes executed by the CPU in the ECU 7 for realizing the functions of the adaptive sliding mode controller 21, the model parameter identifier 22, and the state predictor 23 will be described below.
FIG. 9 is a flowchart showing a process of the throttle valve opening control. The process is executed by the CPU in the ECU 7 in every predetermined period of time (e.g., 2 msec).
In step Sll, a process of setting a state variable shown in FIG. 10 is performed. Calculations of the equations (41) and (42) are executed to determine the throttle valve opening deviation amount DTH(k) and the target value DTHR(k) (steps S21 and S22 in FIG. 10). The symbol (k) representing a current value may sometimes be omitted

as shown in FIG. 10.
In step S12, a process of performing calculations of the model parameter identifier as shown in FIG. 11, i.e., a process of calculating the model parameter vector 0(k) from the equation (15a), is carried out. Further, the model parameter vector B(k) is subjected to the limit process so that the corrected model parameter vector BL(k) is calculated.
In step S13, a process of performing calculations of the state predictor as shown in FIG. 21 is carried out to calculate the predicted deviation amount PREDTH(k).
Next, using the corrected model parameter vector 8L(k) calculated in step S12, a process of calculating the control input Usl(k) as shown in FIG. 22 is carried out in step S14. Specifically, the equivalent control input Ueq, the reaching law input Urch(k), and the adaptive law input Uadp(k) are calculated, and the control input Usl(k) (= duty ratio DUT(k)) is calculated as a sum of these inputs Ueq(k), Urch(k), and Uadp(k).
In step S16, a process of stability determination of the sliding mode controller as shown in FIG. 29 is carried out. Specifically, the stability is determined based on a differential value of the Lyapunov function, and a stability determination flag FSMCSTAB is set. When the stability determination flag FSMCSTAB is set to "1", this indicates that the adaptive sliding mode controller 21 is unstable.
If the stability determination flag FSMCSTAB is set to "1", indicating that the adaptive sliding mode controller 21 is unstable, the switching function setting parameter VPOLE is set to a predetermined stabilizing value XPOLESTB (see steps S231 and S232 in FIG. 24), and the equivalent control input Ueq is set to "0". That is, th% control process by the adaptive sliding mode controller 21 is switched to a control process based on only the reaching law input Urch and the adaptive law input Uadp, to thereby stabilize the control (see steps S206 and S208 in FIG. 22).
Further, when the adaptive sliding mode controller 21 has

become unstable, the equations for calculating the reaching law input Urch and the adaptive law input Uadp are changed. Specifically, the values of the reaching law control gain F and the adaptive law control gain G are changed to values for stabilizing the adaptive sliding mode controller 21, and the reaching law input Urch and the adaptive law input Uadp are calculated without using the model parameter bl (see FIGS. 27 and 28). According to the above stabilizing process, it is possible to quickly terminate the unstable state of the adaptive sliding mode controller 21, and to bring the adaptive sliding mode controller 21 back to its stable state.
In step S17, a process of calculating the default opening deviation thdefadp as shown in FIG. 30 is performed to calculate the default opening deviation thdefadp.
FIG. 11 is a flowchart showing a process of performing calculations of the model parameter identifier 22.
In step S31, the gain coefficient vector KP(k) is calculated from the equation (20). Then, the estimated throttle valve opening deviation amount DTHHAT(k) is calculated from the equation (18) in step S32. In step S33, a process of calculating the identifying error idenl(k) as shown in FIG. 12 is carried out. The estimated throttle valve opening deviation amount DTHHAT(k) calculated in step S32 is applied to the equation (17) to calculate the identifying error ide(k). Further in step S32, the dead zone process using the function shown in FIG. 7A is carried out to calculate the corrected identifying error idenl.
In step S34, the model parameter vector 6(k) is calculated from the equation (15a). Then, the model parameter vector 6(k) is subjected to the stabilization process in step S35. That is, each of the model parameters is subjected to the limit process to calculate the corrected model parameter vector 9L(k).
FIG. 12 is a flowchart showing a process of calculating the identifying error idenl(k) which is carried out in step S33 shown in FIG. 11.

In step S51, the identifying error ide(k) is calculated from the equation (17). Then, it is determined whether or not the value of a counter CNTIDST which is incremented in step S53 is greater than a predetermined value XCNTIDST that is set according to the dead time d of the controlled object (step S52). The predetermined value XCNTIDST is set, for example, to "3" according to a dead time d = 2. Since the counter CNTIDST has an initial value of "0", the process first goes to step S53, in which the counter CNTIDST is incremented by "1". Then, the identifying error ide(k) is set to "0" in step S54, after which the process goes to step S55. Immediately after starting identifying the model parameter vector 8(k), no correct identifying error can be obtained by the equation (17). Therefore, the identifying error ide(k) is set to "0" according to steps S52 through S54, instead of using the calculated result of the equation (17).
If the answer to the step S52 is affirmative (YES), then the process immediately proceeds to step S55.
In step S55, the identifying error ide(k) is subjected to a low-pass filtering. Specifically, when identifying the model parameters of an controlled object which has low-pass characteristics, the identifying weight of the method-of-least-squares algorithm for the identifying error ide(k) has frequency characteristics as indicated by the solid line LI in FIG. 13A. By the low-pass filtering of the identifying error ide(k), the frequency characteristics as indicated by the solid line LI is changed to a frequency characteristics as indicated by the broken line L2 where the high-frequency components are attenuated. The reason for executing the low-pass filtering will be described below.
The frequency characteristics of the actual controlled object having low-pass characteristics and the controlled object model thereof are represented respectively by the solid lines L3 and L4 in FIG. 13B. Specifically, if the model parameters are identified by the model parameter identifier 22 with respect to the controlled object which has low-pass characteristics (characteristics of attenuating high-frequency

components), the identified model parameters are largely affected by the high-frequency-rejection characteristics so that the gain of the controlled object model becomes lower than the actual characteristics in a low-frequency range. As a result, the sliding mode controller 21 excessively corrects the control input.
By changing the frequency characteristics of the weighting of the identifying algorithm to the characteristics indicated by the broken line L2 in FIG. 13A according to the low-pass filtering, the frequency characteristics of the controlled object are changed to frequency characteristics indicated by the broken line L5 in FIG 13B. As a result, the frequency characteristics of the controlled object model is made to coincide with the actual frequency characteristics, or the low frequency gain of the controlled object model is corrected to a level which is slightly higher than the actual gain. Accordinly, it is possible to prevent the control input from being excessively corrected by the sliding mode controller 21, to thereby improve the robustness of the control system and further stabilize the control system.
The low-pass filtering is carried out by storing past values ide(k-i) of the identifying error (e.g., 10 past values for i = 1 through 10) in a ring buffer, multiplying the past values by weighting coefficients, and adding the products of the past values and the weighting coefficients.
Since the identifying error ide(k) is calculated from the equations (17), (18), and (19), the same effect as described above can be obtained by performing the same low-pass filtering on the throttle valve opening deviation amount DTH(k) and the estimated throttle valve opening deviation amount DTHHAT(k), or by performing the same low-pass filtering on the throttle valve opening deviation amounts DTH(k-l), DTH(k-2) and the duty ratio DUT(k-d-l).
Referring back to FIG. 12, the dead zone process as shown in FIG. 14 is carried out in step S56. In step S61 shown in FIG. 14, "n" in the equation (24) is set, for example, to "5" to calculate the square average value DDTHRSQA of an amount of change of the target throttle

valve opening THR. Then, an EIDNRLMT table shown in FIG. 15 is retrieved according to the square average value DDTHRSQA to calculate the dead zone width parameter EIDNRLMT (step S62).
In step S63, it is determined whether or not the identifying error ide(k) is greater than the dead zone width parameter EIDNRLMT. If ide(k) is greater than EIDNRLMT, the corrected identifying error idenl(k) is calculated from the following equation (43) in step S67.
idenl(k) = ide(k) - EIDNRLMT (43)
If the answer to step S63 is negative (NO), it is determined whether or not the identifying error ide(k) is greater than the negative value of the dead zone width parameter EIDNRLMT with a minus sign (step S64).
If ide(k) is less than -EIDNRLMT, the corrected identifying error idenl(k) is calculated from the following equation (44) in step S65.
idenl(k) = ide(k) + EIDNRLMT (44)
If the identifying error ide(k) is in the range between +EIDNRLMT and -EIDNRLMT, the corrected identifying error idenl(k) is set to "0" in step S66.
FIG. 16 is a flowchart showing a process of stabilizing the model parameter vector 6(k), which is carried out in step S35 shown in FIG. 11.
In step S71 shown in FIG. 16, flags FA1STAB, FA2STAB, FB1LMT, and FC1LMT used in this process are initialized to be set to "0". In step S72, the limit process of the model parameters al" and a2" shown in FIG. 17 is executed. In step S73. the limit process of the model parameter bl" shown in FIG. 19 is executed. In step S74, the limit process of the model parameter cl" shown in FIG. 20 is executed.
FIG. 17 is a flowchart showing the limit process of the model parameters al" and a2\ which is carried out in the step S72 shown in FIG. 16. FIG. 18 is a diagram illustrative of the process shown in FIG. 17, and will be referred to with FIG. 17.
In FIG. 18, combinations of the model parameters al" and a2" which are required to be limited are indicated by "x" symbols, and the

range of combinations of the model parameters al" and a2" which are stable are indicated by a hatched region (hereinafter referred to as "stable region"). The limit process shown in FIG. 17 is a process of moving the combinations of the model parameters al" and a2" which are in the outside of the stable region into the stable region at positions indicated by "O" symbols.
In step S81, it is determined whether or not the model parameter a2" is greater than or equal to a predetermined a2 lower limit value XIDA2L. The predetermined a2 lower limit value XIDA2L is set to a negative value greater than "-1". Stable corrected model parameters al and a2 are obtained when setting the predetermined a2 lower limit value XIDA2L to "-1". However, the predetermined a2 lower limit value XIDA2L is set to a negative value greater than "-1" because the matrix A defined by the equation (26) to the "n"th power may occasionally become unstable (which means that the model parameters al" and a2r do not diverge, but oscillate).
If a2" is less than XIDA2L in step S81, the corrected model parameter a2 is set to the lower limit value XIDA2L, and an a2 stabilizing flag FA2STAB is set to "1". When the a2 stabilizing flag FA2STAB is set to "1", this indicates that the corrected model parameter a2 is set to the lower limit value XIDA2L. In FIG. 18, the correction of the model parameter in a limit process Pi of steps S81 and S82 is indicated by the arrow lines with "PI".
If the answer to the step S81 is affirmative (YES), i.e., if a2" is greater than or equal to XIDA2L, the corrected model parameter a2 is set to the model parameter a2" in step S83.
In steps S84 and S85, it is determined whether or not the model parameter al" is in a range defined by a predetermined al lower limit value XIDA1L and a predetermined al upper limit value XIDAlH. The predetermined al lower limit value XIDA1L is set to a value which is equal to or greater than "-2" and lower than "0", and the predetermined al upper limit value XIDAlH is set to "2", for example.

If the answers to steps S84 and S85 are affirmative (YES), i.e., if al" is greater than or equal to XIDA1L and less than or equal to XIDA1H, the corrected model parameter al is set to the model parameter al" in step S88.
If al" is less than XIDA1L in step S84, the corrected model parameter al is set to the lower limit value XIDA1L and an al stabilizing flag FAISTAB is set to "1" in step S86. If al" is greater than XIDA1H in step S85, the corrected model parameter al is set to the upper limit value XIDA1H and the al stabilizing flag FAISTAB is set to "1" in step S87. When the al stabilizing flag FAISTAB is set to "1", this indicates that the corrected model parameter al is set to the lower limit value XIDA1L or the upper limit value XIDA1H. In FIG. 18, the correction of the model parameter in a limit process P2 of steps S84 through S87 is indicated by the arrow lines with "P2".
In step S90, it is determined whether or not the sum of the absolute value of the corrected model parameter al and the corrected model parameter a2 is less than or equal to a predetermined stability determination value XA2STAB. The predetermined stability determi¬nation value XA2STAB is set to a value close to "1" but less than "1" (e.g., "0.99").
Straight lines Ll and L2 shown in FIG. 18 satisfy the following equation (45).
a2 + | al | = XA2STAB (45)
Therefore, in step S90, it is determined whether or not the combination of the corrected model parameters al and a2 is placed at a position on or lower than the straight lines Ll and L2 shown in FIG. 18. If the answer to step S90 is affirmative (YES), the limit process immediately ends, since the combination of the corrected model parameters al and a2 is in the stable region shown in FIG. 18.
If the answer to step S90 is negative (NO), it is determined whether or not the corrected model parameter al is less than or equal to a value obtained by subtracting the predetermined a2 lower limit value

XIDA2L from the predetermined stability determination value XA2STAB in step S91 (since XIDA2L is less than "0", XA2STAB - XIDA2L is greater than XA2STAB). If the corrected model parameter al is equal to or less than (XA2STAB - XIDA2L), the corrected model parameter a2 is set to (XA2STAB ■ | al |) and the a2 stabilizing flag FA2STAB is set to "1" in step S92.
If the corrected model parameter al is greater than (XA2STAB -XIDA2L) in step S91, the corrected model parameter al is set to (XA2STAB - XIDA2L) in step S93. Further in step S93, the corrected model parameter a2 is set to the predetermined a2 lower limit value XIDA2L, and the al stabilizing flag FAlSTAB and the a2 stabilizing flag FA2STAB are set to "1" in step S93.
In FIG. 18, the correction of the model parameter in a limit process P3 of steps S91 and S92 is indicated by the arrow lines with "P3", and the correction of the model parameter in a limit processP4 in steps S91 and S93 is indicated by the arrow lines with "P4".
As described above, the limit process shown in FIG. 17 is carried out to bring the model parameters al" and a2" into the stable region shown in FIG. 18, thus calculating the corrected model parameters al and a2.
FIG. 19 is a flowchart showing a limit process of the model parameter bl", which is carried out in step S73 shown in FIG. 16.
In steps S101 and. S102, it is determined whether or not the model parameter bl" is in a range defined by a predetermined bl lower limit value XIDB1L and a predetermined bl upper limit value XIDBIH. The predetermined bl lower limit value XIDB1L is set to a positive value (e.g., "0.1"), and the predetermined bl upper limit value XIDBIH is set to "1", for example.
If the answers to steps S101 and S102 are affirmative (YES), i.e., if bl" is greater than or equal to XIDB1L and less than or equal to XIDBIH, the corrected model parameter bl is set to the model parameter bl"instepS105.

If bl" is less than XIDBIL in step S101, the corrected model parameter bl is set to the lower limit value XIDBIL, and a bl limiting flag FB1LMT is set to "1" in step S104. If bl" is greater than XIDB1H in step S102, then the corrected model parameter bl is set to the upper limit value XIDB1H, and the bl limiting flag FBlLMT is set to "1" in step S103. When the bl limiting flag FBlLMT is set to "1", this indicates that the corrected model parameter bl is set to the lower limit value XIDBIL or the upper limit value XIDB1H.
FIG. 20 is a flowchart showing a limit process of the model parameter cl", which is carried out in step S74 shown in FIG. 16.
In steps Sill and S112, it is determined whether or not the model parameters cl" is in a range defined by a predetermined cl lower limit value XIDClL and a predetermined cl upper limit value XIDC1H. The predetermined cl lower limit value XIDClL is set to "-60", for example, and the predetermined cl upper limit value XIDClH is set to "W\ for example.
If the answers to steps Sill and S112 are affirmative (YES), i.e., if cl" is greater than or equal to XIDClL and less than or equal to XIDClH, the corrected model parameter cl is set to the model parameter cl" in step S115.
If cl" is less than XIDClL in step Sill, the corrected model parameter cl is set to the lower limit value XIDClL, and a cl limiting flag FC1LMT is set to "1" in step S114. If cl" is greater than XIDClH in step S112, the corrected model parameter cl is set to the upper limit value XIDClH, and the cl limiting flag FC1LMT is set to "1" in step S113. When the cl limiting flag FClLMT is set to "1", this indicates that the corrected model parameter cl is set to the lower limit value XIDClL or the upper limit value XIDClH.
FIG. 21 is a flowchart showing a process of calculations of the state predictor, which is carried out in step S13 shown in FIG. 9.
In step S121, the matrix calculations are executed to calculate the matrix elements cd, a2, pi, p2, yl through yd in the equation (35).

In step S122, the predicted deviation amount PREDTH(k) is calculated from the equation (35).
FIG. 22 is a flowchart showing a process of calculation of the control input Usl (= DUT) applied to the throttle valve actuating device 10, which is carried out in step S14 shown in FIG. 9.
In step S201, a process of calculation of the predicted switching function value opre, which is shown in FIG. 23, is executed. In step S202, a process of calculation of the integrated value of the predicted switching function value opre, which is shown in FIG. 26, is executed. In step S203, the equivalent control input Ueq is calculated from the equation (9). In step S204, a process of calculation of the reaching law input Urch, which is shown in FIG. 27, is executed. In step S205, a process of calculation of the adaptive law input Uadp, which is shown in FIG. 28, is executed.
In step S206, it is determined whether or not the stability determination flag FSMCSTAB set in the process shown in FIG. 29 is "1". When the stability determination flag FSMCSTAB is set to "1", this indicates that the adaptive sliding mode controller 21 is unstable.
If FSMCSTAB is "0" in step S206, indicating that the adaptive sliding mode controller 21 is stable, the control inputs Ueq, Urch, and Uadp which are calculated in steps S203 through S205 are added, thereby calculating the control input Usl in step S207.
If FSMCSTAB is "1" in step S206, indicating that the adaptive sliding mode controller 21 is unstable, the sum of the reaching law input Urch and the adaptive law input Uadp is calculated as the control input Usl. In other words, the equivalent control input Ueq is not used for calculating the control input Usl, thus preventing the control system from becoming unstable.
In steps S209 and S210, it is determined whether or not the calculated control input Usl is in a range defined between a predetermined upper limit value XUSLH and a predetermined lower limit value XUSLL. If the control input Usl is in the range between

XUSLH and XUSLL, the process immediately ends. If the control input Usl is less than or equal to the predetermined lower limit value XUSLL in step S209, the control input Usl is set to the predetermined lower limit value XUSLL in step S212. If the control input Usl is greater than or equal to the predetermined upper limit value XUSLH in step S210, the control input Usl is set to the predetermined upper limit value XUSLH in step S211.
FIG. 23 is a flowchart showing a process of calculating the predicted switching function value opre, which is carried out in step S201 shown in FIG. 22.
In step S221, the process of calculating the switching function setting parameter VPOLE, which is shown in FIG. 24 is executed. Then, the predicted switching function value opre(k) is calculated from the equation (36) in step S222.
In steps S223 and S224, it is determined whether or not the calculated predicted switching function value opre(k) is in a range defined between a predetermined upper value XSGMH and a predetermined lower limit value XSGML. If the calculated predicted switching function value apre(k) is in the range between XSGMH and XSGML, the process shown in FIG. 23 immediately ends. If the calculated predicted switching function value opre(k) is less than or equal to the predetermined lower limit value XSGML in step S223, the calculated predicted switching function value cpre(k) is set to the predetermined lower limit value XSGML in step S225. If the calculated predicted switching function value opre(k) is greater than or equal to the predetermined upper limit value XSGMH in step S224, the calculated predicted switching function value opre(k) is set to the predetermined upper limit value XSGMH in step S226.
FIG. 24 is a flowchart showing a process of calculating the switching function setting parameter VPOLE, which is carried out in step S221 shown in FIG. 23.
In step S231, it is determined whether or not the stability

determination flag FSMCSTAB is "1". If FSMCSTAB is "1" in step S231, indicating that the adaptive sliding mode controller 21 is unstable, the switching function setting parameter VPOLE is set to a predetermined stabilizing value XPOLESTB in step S232. The predetermined stabilizing value XPOLESTB is set to a value which is greater than "-1" but very close to "-1" (e.g., "-0.999").
If FSMCSTAB is "0", indicating that the adaptive sliding mode controller 21 is stable, an amount of change DDTHR(k) in the target value DTHR(k) is calculated from the following equation (46) in step S233.
DDTHR(k) = DTHRlk) - DTHR(k-l) (46)
In step S234, a VPOLE map is retrieved according to the throttle valve opening deviation amount DTH and the amount of change DDTHR calculated in step S233 to calculate the switching function setting parameter VPOLE. As shown in FIG. 25A, the VPOLE map is set so that the switching function setting parameter VPOLE increases when the throttle valve opening deviation amount DTH has a value in the vicinity of "0", i.e., when the throttle valve opening TH is in the vicinity of the default opening THDEF, and the switching function setting parameter VPOLE has a substantially constant value regardless of changes of the throttle valve opening deviation amount DTH, when the throttle valve opening deviation amount DTH has values which are not in the vicinity of "0". The VPOLE map is also set so that the switching function setting parameter VPOLE increases as the amount of change DDTHR in target value increases as indicated by the solid line in FIG. 25B, and the switching function setting parameter VPOLE increases as the amount of change DDTHR in the target value has a value in the vicinity of "0" as indicated by the broken line in FIG. 25B, when the throttle valve opening deviation amount DTH has a value in the vicinity of"O".
Specifically, when the target value DTHR for the throttle valve opening changes greatly in the decreasing direction, the switching

function setting parameter VPOLE is set to a relatively small value. This makes it possible to prevent the throttle valve 3 from colliding with the stopper for stopping the throttle valve 3 in the fully closed position. In the vicinity of the default opening THDEF, the switching function setting parameter VPOLE is set to a relatively large value, which improves the controllability in the vicinity of the default opening THDEF.
As shown in FIG. 25C, the VPOLE map may be set so that the switching function setting parameter VPOLE decreases when the throttle valve opening TH is in the vicinity of the fully closed opening or the fully open opening. Therefore, when the throttle valve opening TH is in the vicinity of the fully closed opening or the fully open opening, the speed for the throttle valve opening TH to follow up the target opening THR is reduced. As a result, collision of the throttle valve 3 with the stopper can more positively be avoided (the stopper also stops the throttle valve 3 in the fully open position).
In steps S235 and S236, it is determined whether or not the calculated switching function setting parameter VPOLE is in a range defined between a predetermined upper limit value XPOLEH and a predetermined lower limit XPOLEL. If the switching function setting parameter VPOLE is in the range between XPOLEH and XPOLEL, the process shown immediately ends. If the switching function setting parameter VPOLE is less than or equal to the predetermined lower limit value XPOLEL in step S236, the switching function setting parameter VPOLE is set to the predetermined lower limit value XPOLEL in step S23S. If the switching function setting parameter VPOLE is greater than or equal to the predetermined upper limit value XPOLEH in step S235, the switching function setting parameter VPOLE is set to the predetermined upper limit value XPOLEH in step S237.
FIG. 26 is a flowchart showing a process of calculating an integrated value of opre, SUMSIGMA, of the predicted switching function value opre. This process is carried out in step S202 shown in

FIG. 22. The integrated value SUMSIGMA is used for calculating the adaptive law input Uadp in the process shown in FIG. 28 which will be described later (see the equation (lla)).
In step S241, the integrated value SUMSIGMA is calculated from the following equation (47) where A T represents a calculation period.
SUMSIGMA(k) = SUMSIGMA(k-l) + opre x AT (47)
In steps S242 and S243, it is determined whether or not the calculated integrated value SUMSIGMA is in a range defined between a predetermined upper limit value XSUMSH and a predetermined lower limit value XSUMSL. If the integrated value SUMSIGMA is in the range between XSUMSH and XSUMSL, the process immediately ends. If the integrated value SUMSIGMA is less than or equal to the predetermined lower limit value XSUMSL in step S242, the integrated value SUMSIGMA is set to the predetermined lower limit value XSUMSL in step S244. If the integrated value SUMSIGMA is greater than or equal to the predetermined upper limit value XSUMSH in step S243, the integrated value SUMSIGMA is set to the predetermined upper limit value XSUMSH in step S245.
FIG. 27 is a flowchart showing a process of calculating the reaching law input Urch, which is carried out in step S204 shown in FIG. 22.
In step S261, it is determined whether or not the stability determination flag FSMCSTAB is "1". If the stability determination flag FSMCSTAB is "0", indicating that the adaptive sliding mode controller 21 is stable, the control gain F is set to a normal gain XKRCH in step S262, and the reaching law input Urch is calculated from the following equation (48), which is the same as the equation (10a), in step S263.
Urch=-Fxopre/bl (48)
If the stability determination flag FSMCSTAB is "1", indicating that the adaptive sliding mode controller 21 is unstable, the control gain

F is set to a predetermined stabilizing gain XKRCHSTB in step S264, and the reaching law input Urch is calculated according to the following equation (49), which does not include the model parameter bl, in step S265.
Urch = -F x opre (49)
In steps S266 and S267, it is determined whether the calculated reaching law input Urch is in a range defined between a predetermined upper limit value XURCHH and a predetermined lower limit value XURCHL. If the reaching law input Urch is in the range between XURCHH and XURCHL, the process immediately ends. If the reaching law input Urch is less than or equal to the predetermined lower limit value XURCHL in step S266, the reaching law input Urch is set to the predetermined lower limit value XURCHL in step S268. If the reaching law input Urch is greater than or equal to the predetermined upper limit value XURCHH in step S267, the reaching law input Urch is set to the predetermined upper limit value XURCHH in step S269.
As described above, when the adaptive sliding mode controller 21 becomes unstable, the control gain F is set to the predetermined stabilizing gain XKRCHSTB, and the reaching law input Urch is calculated without using the model parameter bl, which brings the adaptive sliding mode controller 21 back to its stable state. When the identifying process carried out by the model parameter identifier 22 becomes unstable, the adaptive sliding mode controller 21 becomes unstable. Therefore, by using the equation (49) that does not include the model parameter bl which has become unstable, the adaptive sliding mode controller 21 can be stabilized.
FIG. 28 is a flowchart showing a process of calculating the adaptive law input Uadp, which is carried out in step S205 shown in FIG. 22.
In step S271, it is determined whether or not the stability determination flag FSMCSTAB is "1". If the stability determination flag FSMCSTAB is "0", indicating that the adaptive sliding mode

controller 21 is stable, the control gain G is set to a normal gain XKADP in step S272, and the adaptive law input Uadp is calculated from the following equation (50), which corresponds to the equation (lla), in step S273.
Uadp = -G x SUMSIGMA/bl (50)
If the stability determination flag FSMCSTAB is "1", indicating that the adaptive sliding mode controller 21 is unstable, the control gain G is set to a predetermined stabilizing gain XKADPSTB in step S274, and the adaptive law input Uadp is calculated according to the following equation (51), which does not include the model parameter bl, in step S275.
Uadp = ■ G x SUMSIGMA (51)
As described above, when the adaptive sliding mode controller 21 becomes unstable, the control gain G is set to the predetermined stabilizing gain XKADPSTB, and the adaptive law input Uadp is calculated without using the model parameter bl, which brings the adaptive sliding mode controller 21 back to its stable state.
FIG. 29 is a flowchart showing a process of determining the stability of the sliding mode controller, which is carried out in step S16 shown in FIG. 9. In this process, the stability is determined based on a differential value of the Lyapunov function, and the stability determination flag FSMCSTAB is set according to the result of the stability determination.
In step S281, a switching function change amount Dopre is calculated from the following equation (52). Then, a stability determining parameter SGMSTAB is calculated from the following equation (53) in step S282.
Dopre = opretk) ■ opre(k-l) (52)
SGMSTAB = Dopre x opre(k) (53)
In step S283, it is determined whether or not the stability determination parameter SGMSTAB is less than or equal to a stability determining threshold XSGMSTAB. If SGMSTAB is greater than

XSGMSTAB, it is determined that the adaptive sliding mode controller 21 may possibly be unstable, and an unstability detecting counter CNTSMCST is incremented by "1" in step S285. If SGMSTAB is less than or equal to XSGMSTAB, the adaptive sliding mode controller 21 is determined to be stable, and the count of the unstability detecting counter CNTSMCST is not incremented but maintained in step S284.
In step S286, it is determined whether or not the value of the unstability detecting counter CNTSMCST is less than or equal to a predetermined count XSSTAB. If CNTSMCST is less than or equal to XSSTAB, the adaptive sliding mode controller 21 is determined to be stable, and a first determination flag FSMCSTABl is set to "0" in step S287. If CNTSMCST is greater than XSSTAB, the adaptive sliding mode controller 21 is determined to be unstable, and the first determination flag FSMCSTABl is set to "1" in step S288. The value of the unstability detecting counter CNTSMCST is initialized to "0", when the ignition switch is turned on.
In step S289, a stability determining period counter CNTJUDST is decremented by "1". It is determined whether or not the value of the stability determining period counter CNTJUDST is "0" in step S290. The value of the stability determining period counter CNTJUDST is initialized to a predetermined determining count XCJUDST, when the ignition switch is turned on. Initially, therefore, the answer to step S290 is negative (NO), and the process immediately goes to step S295.
If the count of the stability determining period counter CNTJUDST subsequently becomes "0", the process goes from step S290 to step S291, in which it is determined whether or not the first determination flag FSMCSTABl is "1". If the flrst determination flag FSMCSTABl is "0", a second determination flag FSMCSTAB2 is set to "0" in step S293. If the first determination flag FSMCSTABl is "1", the second determination flag FSMCSTAB2 is set to "1" in step S292.
In step S294, the value of the stability determining period counter CNTJUDST is set to the predetermined determining count

XCJUDST, and the unstability detecting counter CNTSMCST is set to "0". Thereafter, the process goes to step S295.
In step S295, the stability determination flag FSMCSTAB is set to the logical sum of the first determination flag FSMCSTAB1 and the second determination flag FSMCSTAB2. The second determination flag FSMCSTAB2 is maintained at "1" until the value of the stability determining period counter CNTJUDST becomes "0", even if the answer to step S286 becomes affirmative (YES) and the first determination flag FSMCSTAB1 is set to "0". Therefore, the stability determination flag FSMCSTAB is also maintained at "1" until the value of the stability determining period counter CNTJUDST becomes "0".
FIG. 30 is a flowchart showing a process of calculating the default opening deviation thdefadp, which is carried out in step S17 shown in FIG. 9.
In step S251 shown FIG. 30, a gain coefficient KPTH(k) is calculated according to the following equation (54).
KPTH(k) = PTH(k-l)/(l + PTH(k-l)) (54)
where PTH(k-l) represents a gain parameter calculated in step S253 when the present process was carried out in the preceding cycle.
In step S252, the model parameter cl" calculated in the process of calculations of the model parameter identifier as shown in FIG. 11 and the gain coefficient KPTH(k) calculated in step S251 are applied to the following equation (55) to calculate a default opening deviation thdefadp(k).
thdefadp(k) = thdefadp(k-l)
+ KPTH(k) x (cl" ■ thdefadp(k-D)
(55)
In step S253, a gain parameter PTH(k) is calculated from the following equation (56):
PTH(k) = {1 - PTH(k-l)}/(XDEFADPW + PTH(k-l))}
x PTH(k-l)/XDEFADPW (56)
The equation (56) is obtained bv settine XI" and X2" in the

equation (39) respectively to a predetermined value XDEFADP and "1".
According to the process shown in FIG. 30, the model parameter cl" is statistically processed by the sequential method-of-weighted-least-squares algorithm to calculate the default opening deviation thdefadp.
In the present embodiment, the throttle valve actuating device 10 and a portion of the ECU 7 (i.e., an output circuit for supplying an energizing current to the motor 6) correspond to a plant. The process shown in FIG. 22 corresponds to a sliding mode controller. The process shown in FIG. 11 corresponds to an identifying means. The process shown in FIG. 12 corresponds to an identifying error calculating means. The process shown in FIG. 14 corresponds to an identifying error correcting means. The process shown in FIG. 21 corresponds to a predicting means.
Second Embodiment
In the first embodiment described above, the controlled object model is defined by the equation (l) including the dead time d, and the predicted deviation amount PREDTH after the elapse of the dead time d is calculated with the state predictor 23, to thereby control the controlled object model which includes the dead time. Accordingly, it is necessary to execute calculations corresponding to the state predictor 23 in the CPU, and the amount of calculations executed by the CPU becomes large. In the second embodiment, in order to reduce the calculation load on the CPU, the controlled object model is defined by the following equation (la) where the dead time d is set to "0", and the modeling error caused by setting the dead time d to "0" is compensated by the robustness of the adaptive sliding mode control.
DTH(k+l) = al x DTH(k) + a2 x DTH(k-l)
+ blxDUT(k) + cl (la)
In order to further reduce the calculation load on the CPU, the fixed gain algorithm is employed as the algorithm for identifying the model parameters.

process of the model parameter vector 6 to calculate a corrected model parameter vector 6L, and supplies the corrected model parameter vector 6L to the adaptive sliding mode controller 21a. In this manner, model parameters al, a2, and bl which are optimum for making the throttle valve opening TH follow up the target opening THR are obtained, and a model parameter cl indicative of disturbance and a deviation of the default opening THDEF is obtained.
The model parameter scheduler 25 calculates a reference model parameter vector 9base (0baseT = [albase, a2base, blbase, clbase]) based on the throttle valve opening TH, and supplies the calculated reference model parameter vector 8base to the model parameter identifier 22a.
In the present embodiment, since the controlled object model is defined by the above equation (la), the adaptive sliding mode controller 21a calculates the equivalent control input Ueq(k), the reaching law input Urch(k), and the adaptive law input Uadp(k) from the following equations (9b), (10b), and (lib) instead of the above equations (9a), (10a), and (11a).

The equations (9b), (10b), and (lib) are obtained by setting the dead time d to "0" in the equations (9), (10), and (11).
The model parameter identifier 22a calculates a model parameter vector of the controlled object model based on the input (DUT(k)) and output (TH(k)) of the controlled object, as described above.

Specifically, the model parameter identifier 22a calculates a model parameter vector 9(k) from the following equation (15).
e(k) = 6(k-l) + KP(k)ide(k) (15)
The identifying error ide(k) in the equation (15) is defined by the following equations (17), (18), and (19a). The gain coefficient vector KP(k) is defined by the following equation (20), and the square matrix P(k) in the equation (20) is calculated from the following equation (21).

In the present embodiment, the following requirements B4 and B5 are required to be satisfied, in addition to the requirements Bl through B3 which should be satisfied like the first embodiment should be satisfied.
Bl) Adaptation to quasi-static dynamic characteristic changes and hardware characteristics variations
"Quasi-static dynamic characteristics changes" mean slow-rate characteristics changes such as power supply voltage fluctuations or hardware degradations due to aging.
B2) Adaptation to high-rate dynamic characteristics changes
Specifically, this means adaptation to dynamic characteristics changes depending on changes in the throttle valve opening TH.
B3) Prevention of a drift of model parameters

The drift, which is an excessive increase of the absolute values of model parameters, should be prevented. The drift of model parameters is caused by the effect of the identifying error, which should not be reflected to the model parameters, due to nonlinear characteristics of the controlled object.
B4) Matching with the calculating capability of the ECU
Specifically, the amount of calculations is required to be further reduced.
B5) The stabilization of model parameters (control performance)
Specifically, variations of identified model parameters should be minimized.
In order to satisfy the requirement B4, the coefficients kl and \2 are set respectively to "1" and "0", to thereby employ the fixed gain algorithm. Accordingly, the square matrix P(k) is made constant, and the calculation of the equation (21) can be omitted. As a result, the amount of calculations can greatly be reduced.
Specifically, when the fixed gain algorithm is employed, the equation (20) is simplified into the following equation (20a) where P represents a square matrix having constants as diagonal elements.

According to the algorithm thus simplified, the amount of calculations can be reduced. However, the identifying ability is also slightly reduced. Further, the equation (15) for calculating the model parameter vector 6(k) can be rewritten to the following equation (15b) and has an integral structure of the identifying error ide(k). Therefore, the identifying error ide(k) is likely to be integrated to the model parameters to cause the drift of the model parameters.
6(k) = 6(0) + KP(l)ide(l) + KP(2)ide(2)
+ ■ - • +KP(k)ide(k) (15b)
where 6(0) represents an initial vector having elements of initial values

of the model parameters.
In the present embodiment, in order to prevent the drift of the model parameters, the model parameter vector 8(k) is calculated from the following equation (15c) instead of the above equation (15b).
6(k) = 6(0) + DELTAS x KP(l)ide(l)
+ DELTA*2 x KP(2)ide(2) + • • ■
+ DELTA x KP(k-l)ide(k-l)
+ KP(k)ide(k) (15c)
where DELTA represents a forgetting coefficient vector having forgetting coefficients DELTAi (i = 1 through 4) as elements, as indicated by the following equation.
DELTA = [DELTAI, DELTA2, DELTA3, DELTA4]
The forgetting coefficients DELTAi are set to a value between 0 and 1 (0 If the equation (15) is rewritten into a recursive form, the following equations (15d) and (15e) are obtained. A process of calculating the model parameter vector 6(k) from the equations (l5d) and (I5e) instead of the equation. (15) is hereinafter referred to as a 6 correcting method, and d6(k) defined by the equation (l5e) is referred to as "updating vector".

8(k) = 8(0) + dB(k) (15d)
d6(k) = DELTAx dG(k-l) + KPCk)ide(k) (I5e)
According to the algorithm using the 5 correcting method, the drift preventing effect which satisfies the requirement B3 and the model parameter stabilizing effect which satisfies the requirement Bo are obtained. Specifically, the initial vector 6(0) is maintained at all times, and the values which can be taken by the elements of the updating vector d9(k) are limited by the effect of the coefficient vector DELTA. Therefore, the model parameters can be stabilized in the vicinity of their initial values.
Furthermore, since the model parameters are calculated while adjusting the updating vector d9(k) according to the identifying process based on the input and output data of the actual controlled object, the model parameters matching the actual controlled object can be obtained, and hence the above requirement Bl is satisfied.
In order to satisfy the requirement B2, the model parameter vector 6(k) is calculated from the following equation (l5f) which uses the reference model parameter vector 6base, instead of the initial vector 8(0) in the equation (l5d).
8(k) = Sbase + deCk) (I5f)
The reference model parameter vector 6base is set according to the throttle valve opening deviation amount DTH (TH ■ THDEF) by the model parameter scheduler 25. Consequently, the reference model parameter vector 6base can be adapted to changes in the dynamic characteristics which correspond to changes in the throttle valve opening TH, and hence the above requirement B2 is satisfied.
As described above, according to the present embodiment, the amount of calculations by the ECU is reduced by employing the fixed gain algorithm (the requirement B4). The adaptation to quasrstatic dynamic characteristics changes and hardware characteristics variations (the requirement Bl), the stabilization of the model parameters (control performance) (the requirement Bo), and the prevention of the drift of the

model parameter (the requirement B3) are achieved by employing the algorithm using the 6 correcting method. Further, the adaptation to dynamic characteristics changes corresponding to changes in the throttle opening TH is achieved by employing the model parameter scheduler 25 (the requirement B2).
The elements al", a2", bl", and cl" of the model parameter vector 6(k) calculated from the equation (l5f) are subjected to the limit process so that the corrected model parameter vector 0L(k) (8L(k)T = [al, a2, bl, cl]) is calculated like the first embodiment.
In addition, the model parameter cl" is statistically processed, and the central value of its variations is calculated as the default opening deviation thdefadp and used to calculate the throttle valve opening deviation amount DTH and the target value DTHR from the equations (41) and (42) like the first embodiment.
DTH(k) = THCk) ■ THDEF + thdefadp (41)
DTHR(k) = THR(k) - THDEF + thdefadp (42)
Operations of the CPU of the ECU 7 for realizing the functions of the adaptive sliding mode controller 21a, the model parameter identifier 22a, and the model parameter scheduler 25 will be described below.
FIG. 32 is a flowchart showing a throttle valve opening control process. The throttle valve opening control process differs from the throttle valve opening control process shown in FIG. 9 in that step S13 (the calculations of the state predictor) in the latter process is omitted, and steps S12, S14, and S16 in the latter process are changed respectively to steps S12a, S14a, and S16a.
In step Sl2a, a process of performing calculations of the model parameter identifier as shown in FIG. 33. Specifically, a process of calculating the model parameter vector 6(k) from the equation (15:0 is carried out, and the model parameter vector 6(k) is subjected to the limit process so that the corrected model parameter vector 8L(k) is calculated.
In step S14a, the control input Usl(k) shown in FIG. 36 is

calculated using the corrected model parameter vector 6L(k), Specifically, the equivalent control input Ueq, the reaching law input Urch{k), and the adaptive law input Uadp(k) are calculated, and the sum of these inputs is calculated as the control input Usl(k) (duty ratio DUT(k)).
In step Sl6a, a process of determining the stability of the sliding mode controller as shown in FIG. 41 is carried out. Specifically, the stability of the sliding mode controller is determined using the switching function value a instead of the predicted switching function value opre, and the stability determination flag FSMCSTAB is set. The process performed when the stability determination flag FSMCSTAB is set to "1" is the same as that the first embodiment.
FIG. 33 is a flowchart showing a process of calculations of the model parameter identifier 22a. The process shown in FIG. 33 is different from the process of calculations of the model parameter identifier as shown in FIG. 11 in that steps S31 through S34 in the process shown in FIG. 11 are changed to steps S31a through S34a, and steps S33b and S33c are added.
In step S31a, the gain coefficient vector KP(k) is calculated from the equation (20a). Then, the estimated throttle valve opening deviation amount DTHHAT(k) is calculated from the equations (18) and (19a) in step S32a.
In step S33a, a process of calculating ide(k) as shown in FIG. 35 is carried out to calculate the identifying error ide(k). In step S33b. the updating vector d8(k) is calculated from the equation (l5e). Then, a 8base table shown in FIG. 34 is retrieved according to the throttle valve opening deviation amount DTH to calculate the reference model parameter vector 6base in step S33c. In the 6base table, reference model parameters albase, a2base, and blbase are set. When the throttle valve opening deviation amount DTH has a value close to "0" (i.e.. when the throttle opening TH is in the vicinity of the default opening THDEF), the reference model parameters albase and blbase decrease

and the reference model parameter a2base increases. The reference model parameter clbase is set to "0".
In step S34a, the model parameter vector 8(k) is calculated from the equation (l5f). Then, the process of stabilizing the model parameter vector 6(k) is executed like the first embodiment (step S35). That is, the model parameters are subjected to the limit process so that the corrected model parameter vector 6L(k) is calculated.
FIG. 35 is a flowchart showing a process of calculating the identifying error ide(k) which is carried out in step S33a shown in FIG. 33. The process shown in FIG. 35 is different from the process shown in FIG. 12 in that the dead zone process in step S56 shown in FIG. 12 is omitted and step Sol shown in FIG. 12 is changed to step S51a. In the present embodiment, the dead zone process is not performed, since the drift of the model parameters is prevented by the & correcting method.
In step Sola, the estimated throttle valve opening deviation amount DTHHAT(k) is calculated from the equations (18) and (19a), and the identifying error ide(k) is calculated using the estimated throttle valve opening deviation amount DTHHAT(k).
In the present embodiment, the predetermined value XCNTIDST in step S52 is set to "2", for example, because the dead time d of the controlled object model is set to "0".
FIG. 36 is a flowchart showing a process of calculating the control input Usl (DUT) applied to the throttle valve actuating device 10, which is carried out in step Sl4a shown in FIG. 32. The process shown in FIG. 36 is different from the process shown in FIG. 22 in that steps S201 through S205 shown in FIG. 22 are changed respectively to steps S201a through S205a.
In step S201a, the switching function value a is calculated as shown in FIG. 37. In step S202a, the integrated value of the switching function value a is calculated as shown in FIG. 38. In step S203a, the equivalent control input Ueq is calculated from the equation (9b). In step S204a, the reaching law input Urch is calculated as shown in FIG.

39. In step S205a, the adaptive law input Uadp is calculated as shown in FIG. 40.
FIG. 37 is a flowchart showing a process of calculating the switching function value a which is carried out in step S201a shown in FIG. 36. The process shown in FIG. 37 is different from the process shown in FIG. 23 in that steps S222 through S226 shown in FIG. 23 are changed respectively to steps S222a through S226a.
In step S222a, the switching function value oik) is calculated from the equation (5). Steps S223a through S226a are the steps obtained by replacing "opre" in steps S223 through S226 shown in FIG. 23 with "a". Accordingly, the switching function value o(k) is subjected to the limit process in the same manner as the process shown in FIG. 23.
FIG. 38 is a flowchart showing a process of calculating an integrated value SUMSIGMAa of the switching function value a which is carried out in step S202a shown in FIG. 36. The process shown in FIG. 38 is different from the process shown in FIG. 26 in that steps S241 through S245 shown in FIG. 26 are changed respectively to steps S241a through S245a. The integrated value SUMSIGMAa is used for the calculation of the adaptive law input Uadp in a process shown in FIG. 40 described below (see the equation (lib)).
In step S241a, the integrated value SUMSIGMAa is calculated according to the following equation (47a):
SUMSIGMAa(k) = SUMSIGMAa(k-l) + o x AT (47a)
In steps S242a through S245a, the calculated integrated value SUMSIGMAa is subjected to the limit process in the same manner as the process shown in FIG. 26.
FIG. 39 is a flowchart showing a process of calculating the reaching law input Urch, which is carried out in step S204a shown in FIG. 36. The process shown in FIG. 39 is different from the process shown in FIG. 27 in that steps S263 and S265 shown in FIG. 27 are changed respectively to steps S263a and S265a.
In the present embodiment, using the switching function value a

instead of the predicted switching function value opre, the reaching law input Urch is calculated in step S263a, when the adaptive sliding mode controller 21a is stable, and the reaching law input Urch is calculated in step S265a when the adaptive sliding mode controller 21a is unstable.
FIG. 40 is a flowchart showing a process of calculating the adaptive law input Uadp which is carried out in step S205a shown in FIG. 36. The process shown in FIG. 40 is different from the process shown in FIG. 28 in that steps S273 and S275 shown in FIG. 28 are changed respectively to steps S273a and S275a.
In the present embodiment, using the integrated value SUMSIGMAa of the switching function value a, the adaptive law input Uadp is calculated in step S273a, when the adaptive sliding mode controller 21a is stable, and the adaptive law input Uadp is calculated in step S275a when the adaptive sliding mode controller 21a is unstable.
FIG. 41 is a flowchart showing a process of determining the stability of the sliding mode controller which is carried out in step S16a shown in FIG. 32. The process shown in FIG. 41 is different from the process shown in FIG. 29 in that steps S281 and S282 shown in FIG. 29 are changed respectively to steps S281a and S282a.
In step S281a, the switching function change amount Da is calculated according to the following equation (52a). The stability determining parameter SGMSTAB is calculated according to the following equation (53a) in step S282a. That is, the stability is determined on the basis of the switching function value a instead of the predicted switching function value opre.
Da=a(k)-o(k-l) (52a)
SGMSTAB = Da x a(k) (53a)
In the present embodiment, the throttle valve actuating device 10 and a portion of the ECU 7, i.e., an output circuit for supplying an energizing current to the motor 6, correspond to a plant. The process shown in FIG. 36 corresponds to a sliding mode controller. The process shown in FIG. 33 corresponds to an identifying means. The process

shown in FIG. 35 corresponds to an identifying error calculating means. The process shown in FIG. 35 corresponds to an identifying error correcting means. Step S33b shown FIG. 33 corresponds to an updating vector calculating means and an updating vector correcting means.
Third Embodiment
FIG. 42 is a block diagram showing a control system according to the third embodiment of the present invention.
As shown in FIG. 42, the control system comprises a plant 101 as a controlled object, a pH sensor 102 for detecting a pH of a mixed liquid which is an output from the plant, a subtractor 103 for subtracting a first reference value V1BASE from an output VIOUT from the pH sensor 102, a target value generator 104 for generating a control target value VITARGET, a control quantity determining unit 105 for determining a first control quantity Ul, and an adder 106 for adding the first control quantity Ul and a second reference value V2BASE and outputting a second control quantity U2.
The subtractor 103, the target value generator 104, the control quantity determining unit 105, and the adder 106 are specifically implemented by an electronic control unit having a CPU, a memory, and an input/output circuit.
The plant 101 includes a flow rate control valve 111 for controlling the flow rate of an alkaline liquid according to the second control quantity U2, and a stirrer 112 for stirring an alkaline liquid supplied via the flow rate control valve 111 and an acid liquid. The plant 101 stirs the alkaline liquid and the acid liquid and outputs a mixed liquid having a desired pH value.
The control quantity determining unit 105 comprises an identifier 121 for identifying a model parameter vector of a controlled object model which is obtained by modeling the plant 101, an adaptive sliding mode controller 122, and a predictor 123. The identifier 121, the adaptive sliding mode controller 122, and the predictor 123 correspond

respectively to the model parameter identifier 22, the adaptive sliding mode controller 21, and the state predictor 23 according to the first embodiment, and have the same functions as those of the model parameter identifier 22, the adaptive sliding mode controller 21, and the state predictor 23.
The relations between the components and parameters of the third embodiment and the components and parameters of the first embodiment will be described below.
The pH sensor 102 corresponds to the throttle valve opening sensor 8, and the output VIOUT of the pH sensor 102 corresponds to the throttle valve opening TH. The first target value V1BASE corresponds to the default opening THDEF. In the present preferred embodiment, the first target value VlBASE is set to a pH value corresponding to the neutral value. Therefore, a deviation amount DV1 corresponds to the throttle valve opening deviation amount DTH. The target value generator 104 corresponds to the target opening setting unit 24, and the control target value VlTARGET corresponds to the target value DTHR of the throttle valve opening deviation amount. In the first embodiment, the function of the subtractor 103 is included in the model parameter identifier 22 and the state predictor 23.
The second reference value V2BASE is added to bias the central value of the first control quantity Ul which is the output of the adaptive sliding mode controller 122. In the first embodiment, there is no component corresponding to the adder 106, and hence the second reference value V2BASE substantially equals "0" (i.e., Ul = U2 = Usl). In the present embodiment, the second reference value V2BASE is set to such a value that the opening of the flow rate control valve 111 is 50 %, for example.
The flow rate control valve 111 corresponds to a switching element (which is included in the output circuit of the ECU 7, but not shown in the drawings and not explained the description) that is turned on and off by a pulse signal having the duty ratio DUT. The alkaline

liquid corresponds to the supply voltage. An output flow rate V2 of the flow rate control valve 111 corresponds to an energizing current of the motor 6. The stirrer 112 corresponds to the motor 6 and the valve body of the throttle valve 3. The acid liquid corresponds to the intake pipe negative pressure acting on the valve body of the throttle valve 3 and the energizing forces of the return spring 4 and the resilient member 5. A pH value VI of the mixed liquid outputted from the stirrer 112 corresponds to the actual throttle valve opening.
Because of the above relations, the plant 101 can be modeled in the same manner as the first embodiment, and the same control process as the control process according to the first embodiment can be applied to the plant 101. Specifically, the identifier 121 performs the same operation as that of the first embodiment to calculate the corrected model parameter vector 6L, based on the first control quantity Ul and the deviation amount DV1. The predictor 123 performs the same operation as that of the first embodiment to calculate the predicted deviation PREDVl, based on the first control quantity Ul, the deviation amount DV1, and the corrected model parameter vector 8L. The adaptive sliding mode controller 122 performs the same operation as that of the first embodiment to calculate the first control quantity Ul in order to make the predicted deviation PREDVl coincide with the control target value V1TARGET, based on the predicted deviation amount PREDVl and the corrected model parameter vector 6L. Therefore, the output VI of the plant 101 can be made to coincide with a desired pH value by setting the control target value V1TARGET to a desired relative pH value (a desired deviation amount from the first reference value V1BASE).
In the present embodiment, the identifier 121 corresponds to an identifying means, and includes an identifying error calculating means and an identifying error correcting means. The predictor 123 corresponds to a predicting means.

Modification of Third Embodiment
FIG. 43 shows a modification of the control system shown in FIG. 42. In the modification, a plant 101a, instead of the plant 101 shown in FIG. 42, is a controlled object. The plant 101a is constituted by adding a flow rate sensor 113 and a feedback controller 114 to the plant 101 shown in FIG. 42. The flow rate sensor 113 detects an output flow rate VI of the flow control valve 111, and the feedback controller 114 controls the flow control valve 111 to make an output V20UT of the flow rate sensor 113 coincide with a flow rate corresponding to the second control quantity U2.
The modeling and the control process which are the same as those of the third embodiment are also applicable to the plant including the local feedback loop as shown in FIG. 43.
Since the circuit for energizing the motor in the first embodiment is already known, this circuit has not been described in detail. The circuit for energizing the motor may include a current sensor for detecting an output current of the switching element that is turned on and off, and a feedback control process may be carried out to make a detected current value ID coincide with a current value IR corresponding to the control quantity Usl. The present modification corresponds to a structure where the above circuit arrangement is applied to the first embodiment.
Fourth Embodiment
FIG. 44 is a block diagram of a control system according to the fourth embodiment of the present invention. The control system shown in FIG. 44 corresponds to the control system according to the second embodiment, and is similar to the control system shown in FIG. 42 except that the control quantity determining unit 105 shown in FIG. 42 is replaced with a control quantity determining unit 105a. The details of the control system shown in FIG. 44 which will not be described below are identical to those of the control system according to the third

embodiment.
The control quantity determining unit 105a comprises an identifier 121a, an adaptive sliding mode controller 122a, and a parameter scheduler 124.
The identifier 121a, the adaptive sliding mode controller 122a, and the parameter scheduler 124 correspond respectively to, and have the same functions as, the model parameter identifier 22a, the adaptive sliding mode controller 21a, and the model parameter scheduler 25 according to the second embodiment.
The parameter scheduler 124 performs the same operation as that of the second embodiment to calculate the reference model parameter vector 0base, based on the deviation amount DVl. The identifier 121a performs the same operation as that of the second embodiment to calculate the corrected model parameter vector 6L based on the first control quantity Ul, the deviation amount DVl, and the reference model parameter vector Bbase. The adaptive sliding mode controller 122a performs the same operation as that of the second embodiment to calculate the first control quantity Ul based on the deviation amount DVl and the corrected model parameter vector 6L, to make the deviation amount DVl coincide with the control target value V1TARGET. Therefore, the output VI of the plant 101 can be made to coincide with a desired pH value by setting the control target value VlTARGET to a desired relative pH value (a deviation amount from the first reference value V1BASE).
In the present embodiment, the identifier 121a corresponds to an identifying means, and includes an identifying error calculating means, an updating vector calculating means, and an updating vector correcting means.
Modification of Fourth Embodiment
FIG. 45 shows a modification of the control system shown in FIG. 44. In the modification, a plant 101a, instead of the plant 101 shown in

FIG. 44, is a controlled object. The plant 101a is identical to the plant 101a shown in FIG. 43.
The modeling and the control process which are the same as those of the fourth embodiment are also applicable to the plant including the local feedback loop as shown in FIG. 45.
Other Embodiments
The 6 correcting method may be replaced with a E correcting method described below, as a method of calculating the identifying error ide(k) of the model parameters. Specifically, the following equation (I5g) may be used instead of the equation (15c) to calculate the model parameter vector 9(k).
e(k) = EPSke(o)
+ EPS^i x KP(l)ide(l)
+ EPS"-2 x KP(2)ide(2) + • ■ ■
+ EPS x KP(k-l)ide(k-l) + KP(k)ide(k)
(15g) where EPS represents a forgetting coefficient vector having forgetting coefficients EPSi (i = 1 through 4) as its elements, as indicated by the following equation.
EPS = [EPSI, EPS2, EPS3, EPS4]
Like the forgetting coefficients DELTAi, the forgetting coefficients EPSI, EPS2, and EPS4 are set to a value between "0" and "1" (0 In the £ correcting method, the coefficient EPS3 which is relevant to the calculation of the model parameter bl must be set to "1" for the following reasons. In the E correcting method, the all values of the model parameters becomes closer to zero, as the identifying error ide(k) becomes less. Since the model parameter bl is applied to the denominator of the equations (9b), (10b), and (lib), the input Usl applied to the controlled object diverges as the model parameter bl becomes

closer to "0".
The equation (l5g) is different from the equation (15c) in that the initial vector 6(G) is also multiplied by the forgetting coefficient vector EPS.
If the equation (l5g) is rewritten into a recursive form, the following equation (I5h) is obtained. The method of calculating the model parameter vector 6(k) from the equation (15h) instead of the equation (15) is referred to as the e correcting method.
6(k) = EPS x eCk-1) + KP(k)ide(k) (I5h)
The e correcting method also has a function to gradually reduce the influence of past identifying errors eid. Accordingly , the drift of the model parameters are prevented by the E correcting method.
In the second embodiment, the drift of the model parameters is prevented by the 8 correcting method. However, like the first embodiment, the corrected identifying error idenl(k) may be calculated according to the dead zone process (FIG. 14), and the model parameter vector 8(k) may be calculated using the corrected identifying error idenl(k).
In the first embodiment, the dead zone process may be replaced with the 6 correcting method or the E correcting method.
Although certain preferred embodiments of the present invention have been shown and described in detail, it should be understood that various changes and modifications may be made therein without departing from the scope of the appended claims.


WE CLAIM :
1. A control system for a plant, comprising: identifying means for identifying at least one model parameter of a controlled object model which is obtained by modeling said plant, based on an input and an output of said plant; and control means for controlling said plant using the at least one model parameter identified by said identifying means; said identifying means comprising: reference model parameter calculating means for calculating at least one reference model parameter according to a state quantity of said plant; identifying error calculating means for calculating an identifying error of the at least one model parameter; and updating component calculating means for calculating at least one updating component according to the identifying error, wherein said identifying means calculates the at least one model parameter by adding the at least one updating component to the at least one reference model parameter.
2. The control system according to claim 1, wherein the state quantity of said plant is a parameter indicative of a change in the dynamic characteristic of said plant.
3. The control system according to claim 2, wherein the parameter indicative of a change in the dynamic characteristic of said plant is an output of said plant.
4. The control system according to claim 2, wherein the parameter indicative of a change in the dynamic characteristic of said plant is a control target value of the output of said plant.

The control system according to claim 1 or 2, wherein said control means controls said plant with a sliding mode control.
The control system according to claim 5, wherein an control input applied from said control means to said plant has an adaptive law input.
The control system according to any one of claims 1 to 6, wherein said plant comprises a throttle valve actuating device having a throttle valve of an internal combustion engine and actuating means for actuating said throttle valve, and said control means calculates a parameter for determining a control input to be applied to said throttle valve actuating device to make the opening of said throttle valve coincide with a target opening.
The control system according to claim 5, wherein said control means comprises a sliding mode controller for controlling said plant with the sliding mode control, and said sliding mode controller comprises switching function value calculating means for calculating a value of a switching function defined as a linear function of a deviation between an output of said plant and a control target value; equivalent control input calculating means for calculating an equivalent control input that contributes to constraining a deviation state quantity which is defined based on the deviation between the output of said plant and the control target value, on a switching straight line on which the value of the switching function becomes zero; reaching law input calculating means for calculating a reaching law input that contributes to placing the
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deviation state quantity onto the switching straight line; control input calculating means for calculating a control input to said plant by adding at least the equivalent control input and the reaching law input; stability determining means for determining whether or not the sliding mode control is stable; and stabilizing means for reducing an absolute value of the equivalent control input when the sliding mode control is determined to be unstable.
The control system according to claim 5, wherein said control means comprises a sliding mode controller for controlling said plant with the sliding mode control, and said sliding mode controller comprises switching function value calculating means for calculating a value of a switching function defined as a linear function of a deviation between an output of said plant and a control target value; equivalent control input calculating means for calculating an equivalent control input that contributes to constraining a deviation state quantity which is defined based on the deviation between the output of said plant and the control target value, on a switching straight line on which the value of the switching function becomes zero; reaching law input calculating means for calculating a reaching law input that contributes to placing the deviation state quantity onto the switching straight line; control input calculating means for calculating a control input to said plant by adding at least the equivalent control input and the reaching law input; stability determining means for determining whether or not the sliding mode control is stable; and stabilizing means for changing a control gain that is used in calculating the reaching law input, to a value that makes
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the sliding mode control more stable, when the sliding mode control is determined to be unstable.
The control system according to claim 8 or 9, wherein said sliding mode controller further comprises adaptive law input calculating means for calculating an adaptive law input that contributes to placing the deviation state quantity onto the switching straight line, and said control input calculating means calculates the control input to said plant by adding the equivalent control input, the reaching law input, and the adaptive law input.
The control system according to claim 5, wherein said control means comprises a sliding mode controller for controlling said plant with the sliding mode control, and said sliding mode controller comprises switching function value calculating means for calculating a v?lue of a switching function defined as a linear function of a deviation between an output of said plant and a control target value; equivalent control input calculating means for calculating an equivalent control input that contributes to constraining a deviation state quantity which is defined based on the deviation between the output of said plant and the control target value, on a switching straight line on which the value of the switching function becomes zero; reaching law input calculating means for calculating a reaching law input that contributes to placing the deviation state quantity onto the switching straight line; adaptive law input calculating means for calculating an adaptive law input that contributes to placing the deviation state quantity onto the switching
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straight line; control input calculating means for calculating a control input to said plant by adding the equivalent control input, the reaching law input, and the adaptive law input; stability determining means for determining whether or not the sliding mode control is stable; and stabilizing means for changing a control gain that is used in calculating the adaptive law input, to a value that makes the sliding mode control more stable, when the sliding mode control is determined to be unstable.
The control system according to claim 5, wherein said control means comprises a sliding mode controller for controlling said plant with the sliding mode control, and said sliding mode controller comprises switching function value calculating means for calculating a value of a switching function defined as a linear function of a deviation between an output of said plant and a control target value; stability determining means for determining whether or not the sliding mode control is stable; and stabilizing means for changing the switching function so that a response speed of the sliding mode control may decrease, when the sliding mode control is determined to be unstable.
The control system according to any one of claims 8 to 12, wherein said stability determining means performs the stability determination, based on a value of the switching function.
The control system according to claim 5, wherein said control means comprises a sliding mode controller for controlling said plant with the sliding mode control, and said sliding mode controller comprises stability determining means for determining whether or not the sliding
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mode control is stable; and stabilizing means for inhibiting the use of a predetermined model parameter, when the sliding mode control is determined to be unstable.
The control system according to any one of claims 8 to 14, wherein said plant comprises a throttle valve actuating device having a throttle valve of an internal combustion engine and actuating means for actuating said throttle valve, and said sliding mode controller calculates a parameter for determining the control input to be applied to said throttle valve actuating device to make the opening of said throttle valve coincide with a target opening.
The control system according to claim 5, wherein said control means comprises a sliding mode controller for controlling said plant with the sliding mode control, and said identifying means calculates the at least one model parameter and the at least one updating component respectively as a model parameter vector and an updating vector, said identifying means further having updating vector correcting means for correcting the updating vector by multiplying a past value of at least one element of said updating vector by a predetermined value which is greater than 0 and less than 1, wherein said identifying means calculates the model parameter vector by adding the corrected updating vector to a reference vector consisting of the at least one reference parameter.
The control system according to claim 16, wherein said updating vector correcting means does not multiply one of an element of the updating vector which is relevant to the input of said plant and an element of the
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updating vector which is irrelevant to the input and the output of said plant, by the predetermined value.
The control system according to claim 16 or 17, wherein said updating vector correcting means multiplies at least one element of the reference vector by the predetermined value.
The control system according to any one of claims 16 to 18, wherein said identifying means identifies the model parameter vector using a fixed gain algorithm.
The control system according to any one of claims 16 to 19, wherein said identifying error calculating means performs a low-pass filtering of the identifying error and outputs the identifying error after the low-pass filtering.
The control system according to any one of claims 16 to 20, further comprising predicting means for calculating a predicted value of the output of said plant.
The control system according to claim 21, wherein said predicting means calculates the predicted value using the model parameter vector identified by said identifying means.
The control system according to any one of claims 16 to 21, wherein a control input applied from the sliding mode controller to the plant includes an adaptive law input.
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The control system according to any one of claims 16 to 23, wherein said plant comprises a throttle valve actuating device having a throttle valve of an internal combustion engine and actuating means for actuating said throttle valve, and said sliding mode controller calculates a parameter for determining a control input to be applied to said throttle valve actuating device to make the opening of said throttle valve coincide with a target opening.
The control system according to any one of claims 16 to 23, wherein said plant comprises an engine system having an internal combustion engine and fuel supply means for supplying fuel to said engine, and said sliding mode controller calculates a parameter for determining a control input to be applied to said engine system to make an air-fuel rafio of an air-fuel mixture to be supplied to said engine coincide with a target air-fuel ratio.
The control system according to claim 5, wherein said control means comprises a sliding mode controller for controlling said plant with the sliding mode control, and said identifying means calculates the at least one model parameter and the at least one updating component respectively as a model parameter vector and an updating vector, said identifying means further having updating vector correcting means for correcting the updating vector so that an influence of the past value of the identifying error may decrease; model parameter vector calculating means for calculating the model parameter vector by adding the corrected updating vector to a reference vector consisting of the at
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least one reference model parameter; and limiting means for limiting a value of at least one element of the model parameter vector calculated by said model parameter vector calculating means, within a predetermined limit range.
The control system according to claim 26, wherein said updating vector calculating means calculates the updating vector using a fixed gain algorithm.
The control system according to claim 26 or 27, wherein said updating vector correcting means corrects the updating vector by multiplying a past value of at least one element of said updating vector by a predetermined value which is greater than 0 and less than 1.
The control system according to claim 28, wherein said updating vector correcting means does not multiply one of an element of the updating vector which is relevant to the input of said plant and an element of the updating vector which is irrelevant to the input and the output of said plant, by the predetermined value.
The control system according to claim 28 or 29, wherein said updating vector correcting means multiplies at least one element of the reference vector by the predetermined value.
The control system according to any one of claims 26 to 29, wherein the reference vector is calculated according to a parameter indicative of a change in a dynamic characteristic of said plant.
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The control system according to claim 1, wherein the control object model is obtained by modeling said plant using a predetermined reference value, and said identifying means identifies the plurality of model parameters of said controlled object model, the plurality of model parameters including a model parameter which is irrelevant to an input and an output of said plant, said control system further having correcting means for correcting the predetermined reference value according to the model parameter which is irrelevant to an input and an output of said plant.
The control system according to claim 32, wherein said correcting means calculates a correction value by statistically processing the model parameter which is irrelevant to an input and an output of said plant, and corrects the predetermined reference value with the correction value.
The control system according to claim 32 or 33, wherein said controlled object model is defined by at least one first model parameter which is relevant to an output of said plant, a second model parameter which is relevant to a control input of said plant, and a third model parameter which is irrelevant to the output and the control input of said plant.
The control system according to any one of claims 32 to 34, wherein said plant comprises a throttle valve actuating device having a throttle valve of an internal combustion engine and actuating means for actuating said throttle valve, and said control means calculates a parameter for determining a control input to be applied to said throttle
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valve actuating device to make the opening of said throttle valve coincide with a target opening.
The control system according to claim 35, wherein said throttle valve actuating device comprises a first energizing means for energizing said throttle valve in a closing direction, and a second energizing means for energizing said throttle valve in an opening direction, and maintains said throttle valve at a default opening by means of said first and second energizing means, when said throttle valve is not actuated by said actuating means, said predetermined reference value being set to the default opening.
A control system for a plant substantially as herein described with reference to the accompanying drawings.

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Documents:

in-pct-2002-2073-che abstract duplicate.pdf

in-pct-2002-2073-che abstract.pdf

in-pct-2002-2073-che claims duplicate.pdf

in-pct-2002-2073-che claims.pdf

in-pct-2002-2073-che correspondence others.pdf

in-pct-2002-2073-che correspondence po.pdf

in-pct-2002-2073-che description (complete) duplicate-1.pdf

in-pct-2002-2073-che description (complete) duplicate.pdf

in-pct-2002-2073-che description (complete)-1.pdf

in-pct-2002-2073-che description (complete).pdf

in-pct-2002-2073-che drawings.pdf

in-pct-2002-2073-che form-1.pdf

in-pct-2002-2073-che form-19.pdf

in-pct-2002-2073-che form-26.pdf

in-pct-2002-2073-che form-3.pdf

in-pct-2002-2073-che form-5.pdf

in-pct-2002-2073-che pct.pdf


Patent Number 202032
Indian Patent Application Number IN/PCT/2002/2073/CHE
PG Journal Number 05/2007
Publication Date 02-Feb-2007
Grant Date 14-Sep-2006
Date of Filing 13-Dec-2002
Name of Patentee M/S. HONDA GIKEN KOGYO KABUSHIKI KAISHA
Applicant Address 1-1, Minami-Aoyama 2-chome Minato-ku, Tokyo 107-0062
Inventors:
# Inventor's Name Inventor's Address
1 YASUI, Yuji c/o HONDA R&D CO., NO. 4-1, Chuo 1-chome Wako-shi, Saitama
2 TAKAHASHI, Jun c/o HONDA R&D CO., NO. 4-1, Chuo 1-chome Wako-shi, Saitama
3 IWAKI, Yoshihisa c/o HONDA R&D CO., NO. 4-1, Chuo 1-chome Wako-shi, Saitama
PCT International Classification Number F02D 35/00
PCT International Application Number PCT/JP2002/003895
PCT International Filing date 2002-04-19
PCT Conventions:
# PCT Application Number Date of Convention Priority Country
1 2001-343998 2001-11-09 Japan
2 2001-123344 2001-04-20 Japan
3 2001-146144 2001-05-16 Japan
4 2001-184540 2001-06-19 Japan
5 2001-125648 2001-04-24 Japan