JP2003005804A - Plant control equipment - Google Patents

Abstract

(57) [Problem] To provide a plant control device capable of further improving the stability of control using the model parameters while identifying model parameters of a control target model obtained by modeling a plant to be controlled. provide. SOLUTION: A model parameter identifier 22a calculates a model parameter vector θ in a format in which an update vector dθ is added to a reference vector θbase of the model parameter. The update vector dθ is corrected so that the influence of the past value of the identification error ide is reduced, and the corrected update vector dθ is added to the reference vector θbase to calculate the model parameter vector θ. Further, processing is performed to limit the values of the elements a1, a2, b1, and c1 of the model parameter vector to within a predetermined limit range.

Description

Detailed Description of the Invention

[0001]

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to a plant control device, and in particular, while identifying in real time model parameters showing the characteristics of a controlled object model obtained by modeling a plant to be controlled, The present invention relates to a device that is used for control.

[0002]

2. Description of the Related Art A plant to be controlled is modeled,
The air-fuel ratio control device that calculates the model parameter of the controlled object model by the parameter adjustment mechanism and feedback-controls the air-fuel ratio of the air-fuel mixture supplied to the internal combustion engine to the target air-fuel ratio by the adaptive controller using this model parameter compared to the conventional one Known (for example, JP-A-11-73206)
Issue).

In this control device, the model parameter is calculated by adding the update component calculated according to the identification error of the model parameter to the initial value of the model parameter. The update component of the model parameter is modified so as to reduce the influence of the past value of the identification error, and the model parameter is prevented from drifting due to the influence of disturbance and the control is destabilized.

[0004]

In the device described in the above publication, model parameter drift due to the influence of disturbance is prevented. However, since the model parameter calculated by the parameter adjusting mechanism is used as it is in the adaptive controller, there is room for improvement in maintaining the stability of the adaptive controller.

The present invention has been made in view of this point, and further improves the stability of control using the model parameter while identifying the model parameter of the controlled object model that models the controlled object plant. It is an object of the present invention to provide a control device for a plant that can be operated.

[0006]

To achieve the above object, the invention according to claim 1 identifies a model parameter vector of a controlled object model that models a plant based on the input and output of the plant. In a control device for a plant, comprising: means and a control means for controlling the plant using the model parameter vector identified by the identifying means, the identifying means calculates an identification error (ide) of the model parameter vector. Identifying error calculating means and an update vector (d
θ), update vector calculation means, update vector correction means for correcting the update vector so as to reduce the influence of the past value of the identification error, and reference vector (θbase, θ (0)) of the model parameter. To the model parameter vector calculating means for calculating the model parameter vector by adding the modified update vector to the element (a1, a1) of the model parameter vector calculated by the model parameter vector calculating means.
a2, b1, c1) values are limited within a predetermined limit range.

According to this structure, the update vector is calculated according to the identification error of the model parameter vector, the update vector is modified so as to reduce the influence of the past value of the identification error, and the reference vector of the model parameter is modified. The model parameter vector is calculated by adding the updated vectors, and the values of the elements of the model parameter vector are limited within a predetermined limit range. Therefore, it is possible to further improve the control stability while preventing the drift of the model parameter.

According to a second aspect of the present invention, in the plant control apparatus according to the first aspect, the update vector calculating means calculates the update vector using a fixed gain algorithm. According to this configuration, since the update vector is calculated using the fixed gain algorithm, the amount of calculation can be reduced.

According to a third aspect of the present invention, in the plant control apparatus according to the first or second aspect, the update vector correction means has a past value of at least one element of the update vector greater than 0 and greater than 1. Small predetermined value (DE
It is characterized in that the update vector is corrected by multiplying LTAi, EPSi).

According to this configuration, since the update vector is corrected by multiplying the past value of at least one element of the update vector by a predetermined value larger than 0 and smaller than 1, the influence of the past value of the identification error is affected. Can be reduced and the model parameter vector can be prevented from drifting.

According to a fourth aspect of the present invention, in the plant control apparatus according to the third aspect, the update vector correction means is an element related to the input of the update vector of the update vector (element related to the calculation of b1). ) Or an element that is not related to the input / output of the plant (an element related to the calculation of c1), the predetermined value (DELTAi, EPS).
It is characterized by not multiplying i).

According to this configuration, the elements of the update vector related to the input of the plant or the elements not related to the input / output of the plant are not multiplied by the predetermined value larger than 0 and smaller than 1, so that the steady state is obtained by modifying the update vector. It is possible to prevent the occurrence of deviation.

According to a fifth aspect of the present invention, in the plant control apparatus according to the third or fourth aspect, the update vector correction means includes at least one element of the reference vector (θ (0)) as well. It is characterized in that it is multiplied by a predetermined value. According to this configuration, at least one element of the reference vector is also multiplied by the predetermined value larger than 0 and smaller than 1, and the drift of the model parameter vector is prevented.

The invention described in claim 6 is from claim 1 to claim 4.
In the plant control device according to any one of items 1 to 3, the reference vector is calculated according to a parameter (DTH) indicating a change in dynamic characteristics of the plant.
According to this configuration, since the reference vector is calculated according to the parameter indicating the change in the dynamic characteristic of the plant, an appropriate reference vector according to the change in the dynamic characteristic of the plant can be obtained. As a result, the model parameters can be quickly converged even when the plant includes a non-linear element.

According to a seventh aspect of the present invention, an identifying means for identifying a model parameter vector of a controlled object model modeling the plant based on the input and output of the plant, and the model parameter identified by the identifying means. In a control device for a plant, which comprises a control means for controlling the plant by using a vector, the identification means includes an identification error (id
e) and an identification error calculation means for calculating e), and the identification error is within a predetermined range (−EIDNRLMT ≦ ide ≦ EIDNRL).
MT), an identification error correction means for correcting the identification error in a decreasing direction, and a model parameter vector calculation for calculating the model parameter vector using the identification error (idenl) corrected by the identification error correction means Means and the values of the elements of the model parameter vector calculated by the model parameter vector calculation means,
It has a limiting means for limiting within a predetermined limit range.

According to this configuration, when the identification error of the model parameter is within a predetermined range, the identification error is corrected in a decreasing direction, the model parameter vector is calculated using the corrected identification error, and The value of the element of the model parameter vector is limited within a predetermined limit range. Therefore, it is possible to further improve the control stability while preventing the drift of the model parameter.

The invention described in claim 8 is from claim 1 to claim 7.
In the plant control device according to any one of items 1 to 3, the limiting means sets the values of the plurality of elements so that the plurality of elements (a1, a2) of the model parameter vector satisfy a predetermined relationship (FIG. 18). Characterized by limiting.

According to this configuration, the values of the plurality of elements of the model parameter vector are limited so that the plurality of elements of the model parameter vector satisfy a predetermined relationship, so that the stability of control using the model parameter vector is improved. You can
According to a ninth aspect of the present invention, in the plant control apparatus according to the seventh aspect, the identification error correction means has the identification error (ide) within the predetermined range (-EIDNRLMT).
When ≦ ide ≦ EIDNRLMT), the identification error (ide) is set to 0.

According to this configuration, when the identification error is within the predetermined range, the identification error is set to 0. Therefore, the influence of the identification error, which should not be reflected in the value of the model parameter, is eliminated, and the model parameter The drift prevention effect can be enhanced. The invention described in claim 10 is claim 1
In the control device for the plant according to any one of 1 to 9,
The plant includes a throttle valve drive device having a throttle valve of an internal combustion engine and a drive means for driving the throttle valve, and the control means makes the opening of the throttle valve equal to a target opening. A parameter for determining a control input to the throttle valve driving device is calculated.

According to this structure, the throttle valve opening is controlled to match the target opening by using the model parameter identified by the identifying means. Therefore, the controllability of the throttle valve opening to the target opening is controlled. And the stability of throttle valve opening control can be improved.

[0021]

BEST MODE FOR CARRYING OUT THE INVENTION Embodiments of the present invention will be described below with reference to the drawings. (First Embodiment) FIG. 1 is a view showing the arrangement of a throttle valve control apparatus according to the first embodiment of the present invention. In an intake passage 2 of an internal combustion engine (hereinafter referred to as “engine”) 1,
A throttle valve 3 is provided. The throttle valve 3 includes a return spring 4 as a first urging means for urging the throttle valve 3 in the closing direction, and the throttle valve 3
Member 5 as second urging means for urging the valve in the valve opening direction
And are attached. Further, the throttle valve 3 is configured so that it can be driven by a motor 6 as a driving means via a gear (not shown). When the driving force by the motor 6 is not applied to the throttle valve 3, the opening degree TH of the throttle valve 3 is the default opening degree T in which the urging force of the return spring 4 and the urging force of the elastic member 5 are balanced.
It is held at HDEF (for example, 5 degrees).

The motor 6 is an electronic control unit (hereinafter "E").
CU ”) and its operation is the ECU
Controlled by 7. The throttle valve 3 is provided with a throttle valve opening sensor 8 for detecting the throttle valve opening TH, and the detection signal thereof is supplied to the ECU 7.

Further, the ECU 7 is connected with an accelerator sensor 9 for detecting a depression amount ACC of an accelerator pedal for detecting a required output of a driver of a vehicle equipped with the engine 1, and a detection signal thereof is supplied to the ECU 7. It EC
U7 is an input circuit to which the detection signals of the throttle valve opening sensor 8 and the accelerator sensor 9 are supplied, an AD conversion circuit that converts the input signal into a digital signal, a central processing unit (CPU) that executes various calculation processes, and a CPU. It is provided with a memory for storing a program to be executed and maps and tables referred to by the program, and an output circuit for supplying a drive current to the motor 6. The ECU 7 determines the target opening THR of the throttle valve 3 according to the accelerator pedal depression amount ACC, and determines the control amount DUT of the motor 6 so that the detected throttle valve opening TH matches the target opening THR. , An electric signal corresponding to the control amount DUT is supplied to the motor 6.

In the present embodiment, the throttle valve driving device 10 including the throttle valve 3, the return spring 4, the elastic member 5 and the motor 6 is the control target, and the duty ratio of the electric signal for applying the input to the control target to the motor 6 is controlled. Let DUT be the output of the controlled object be the throttle valve opening TH detected by the throttle valve opening sensor 8.

When the response frequency characteristic of the throttle valve driving device 10 is actually measured, the gain characteristic and the phase characteristic shown by the solid line in FIG. 2 are obtained. Therefore, the model defined by the following equation (1) is set as the controlled object model. The response frequency characteristic of this model is shown by the broken line in FIG. 2, and it has been confirmed that it is similar to the characteristic of the throttle valve driving device 10. DTH (k + 1) = a1 * DTH (k) + a2 * DTH (k-1) + b1 * DUT (kd) + c1 (1) Here, k is a parameter representing the discretized time, and DTH (k ) Is the throttle valve opening deviation amount defined by the following equation (2). DTH (k) = TH (k) -THDEF (2) where TH is the detected throttle valve opening, THDEF
Is the default opening. Also, a1 and a of the formula (1)
2, b1 and c1 are model parameters that determine the characteristics of the controlled object model, and d is the dead time.

The model defined by the above equation (1) is a discrete time DARX model (delayed autoregressive model with) adopted to facilitate application of adaptive control.
exogeneous input: Autoregressive model with external input). In the equation (1), the model parameters a1 and a2 related to the output deviation amount DTH and the input duty ratio D
In addition to the model parameter b1 related to UT, a model parameter c1 not related to input / output is set. The model parameter c1 is a parameter indicating the deviation of the default opening THDEF and the disturbance applied to the throttle valve drive device. That is, the model parameter identifier identifies the model parameters c1, at the same time as the model parameters a1, a2, a3, so that the default opening deviation and the disturbance can be identified.

FIG. 3 is a functional block diagram of the throttle valve control device realized by the ECU 7. This control device has an adaptive sliding mode controller 21, a model parameter identifier 22, and a dead time d after a lapse of time. A state predictor 23 that calculates a predicted throttle valve opening deviation amount (hereinafter referred to as “prediction deviation amount”) PREDTH (k) (= DTH (k + d)) and a throttle valve 3 of the throttle valve 3 according to the accelerator pedal depression amount ACC. The target opening degree setting unit 24 sets the target opening degree THR.

Adaptive sliding mode controller 2
1 indicates that the detected throttle valve opening TH is the target opening THR.
In accordance with the adaptive sliding mode control, the duty ratio DUT is calculated, and the calculated duty ratio DUT is output. By using the adaptive sliding mode controller 21, the throttle valve opening TH
It is possible to appropriately change the response characteristic to the target opening THR of using the predetermined parameter (VPOLE), and as a result, the impact (throttle) when moving the throttle valve 3 from the open position to the fully closed position. It is possible to avoid (a collision with the fully closed stopper) and to make the engine response variable to the accelerator operation. In addition, it is possible to ensure the stability against the error of the model parameter.

The model parameter identifier 22 uses the modified model parameter vector θL (θL ^T = [a1, a2, b
1, c1]) is calculated and supplied to the adaptive sliding mode controller 21. More specifically, the model parameter identifier 22 determines the model parameter vector θ based on the throttle valve opening TH and the duty ratio DUT.
To calculate. Furthermore, the model parameter vector θ
The modified model parameter vector θL is calculated by performing a limit process on the corrected model parameter vector θL, and the modified model parameter vector θL is supplied to the adaptive sliding mode controller 21. In this way, the throttle valve opening TH
Optimal model parameters a1, a2, b1 for obtaining the target opening degree THR are obtained, and the model parameter c indicating the deviation of the disturbance and the default opening degree THDEF is obtained.
1 is obtained.

By using the model parameter identifier 22 for identifying model parameters in real time, adaptation to changes in engine operating conditions, compensation of characteristic variations of hardware, compensation of power supply voltage fluctuations, and secular variation of hardware characteristics. Can be adapted to.

The state predictor 23 determines the throttle valve opening TH.
Based on the duty ratio DUT, the throttle valve opening TH (predicted value) after the dead time d, more specifically, the predicted deviation amount PREDTH is calculated and supplied to the adaptive sliding mode controller 21. Predicted deviation amount PREDTH
By using, it is possible to secure the robustness of the control system with respect to the dead time of the controlled object, and to improve the controllability especially in the vicinity of the default opening THDEF where the dead time is large.

Next, the operation principle of the adaptive sliding mode controller 21 will be described. First, the target value DTHR (k) is defined as the deviation amount between the target opening THR (k) and the default opening THDEF by the following equation (3). DTHR (k) = THR (k) -THDEF (3) Here, the throttle valve opening deviation amount DTH and the target value DT
If the deviation e (k) from the HR is defined by the following equation (4), the switching function value σ (k) of the adaptive sliding mode controller
Is set as in the following equation (5). e (k) = DTH (k) −DTHR (k) (4) σ (k) = e (k) + VPOLE × e (k−1) (5) = (DTH (k) −DTHR (k)) + VPOLE × (DTH
(k-1) -DTHR (k-1)) Here, VPOLE is a switching function setting parameter set to a value larger than -1 and smaller than 1.

The vertical axis is the deviation e (k), and the horizontal axis is the previous deviation e.
On the phase plane defined as (k-1), the combination of the deviation e (k) that satisfies σ (k) = 0 and the previous deviation e (k-1) is
Since this is a straight line, this straight line is generally called a switching straight line. The sliding mode control is a control focused on the behavior of the deviation e (k) on the switching straight line, so that the switching function value σ (k) becomes 0, that is, the deviation e (k) and the previous deviation e (k). -1) is controlled so that it is placed on the switching line on the phase plane, and it is robust against disturbances and modeling errors (difference between actual plant characteristics and modeled controlled model characteristics). Such control is realized, and the throttle valve opening deviation amount DTH is made to follow the target value DTHR.

Further, the switching function setting parameter V of the equation (5)
By changing the value of POLE, as shown in FIG. 4, it is possible to change the damping characteristic of the deviation e (k), that is, the tracking characteristic of the throttle valve opening deviation amount DTH to the target value DTHR. Specifically, when VPOLE = −1, the characteristic does not follow at all, and the following speed can be increased as the absolute value of the switching function setting parameter VPOLE is decreased.

The throttle valve control device is required to satisfy the following requirements A1 and A2. A1) Avoid collision with the throttle fully closed stopper when moving the throttle valve 3 to the fully closed position A2) Non-linear characteristics near the default opening THDEF (biasing force of the return spring 4 and biasing force of the elastic member 5). Controllability with respect to changes in the elastic characteristics due to the balance between, the backlash of the gear interposed between the motor 6 and the throttle valve 3, and the dead zone where the throttle valve opening does not change even if the duty ratio DUT changes. Therefore, in the vicinity of the fully closed position of the throttle valve, the deviation e
The convergence speed of (k) is reduced, and the default opening THD
It is necessary to increase the convergence speed in the vicinity of EF.

According to the sliding mode control, by changing the switching function setting parameter VPOLE,
Since the convergence speed can be easily changed, in the present embodiment, the change amount DDT of the throttle valve opening TH and the target value DTHR is changed.
The switching function setting parameter VPOLE is set according to HR (= DTHR (k) -DTHR (k-1)).
As a result, the above requirements A1 and A2 can be satisfied.

As described above, in the sliding mode control, the deviation e (k) is restrained on the switching straight line by restraining the combination of the deviation e (k) and the previous deviation e (k-1) (hereinafter referred to as "deviation state quantity"). k) is converged to 0 at a specified convergence speed and robustly to disturbance and modeling error. Therefore, in the sliding mode control, it is important how to put the deviation state quantity on the switching straight line and restrain it.

From such a viewpoint, the input (output of the controller) DUT (k) (also referred to as Usl (k)) to the controlled object is represented by the equivalent control input U as shown in the following equation (6).
eq (k), reaching law input Urch (k) and adaptive law input Uad
Constructed as the sum of p (k). DUT (k) = Usl (k) = Ueq (k) + Urch (k) + Uadp (k) (6)

The equivalent control input Ueq (k) is an input for restraining the deviation state quantity on the switching straight line, and the reaching law input U
rch (k) is an input for putting the deviation state quantity on the switching straight line, and the adaptive law input Uadp (k) is for suppressing the influence of modeling error and disturbance and putting the deviation state quantity on the switching straight line. Is input. Below each input Ueq (k), Urch (k)
And a method of calculating Uadp (k) will be described.

Since the equivalent control input Ueq (k) is an input for restraining the deviation state quantity on the switching straight line, the condition to be satisfied is given by the following equation (7). σ (k) = σ (k + 1) (7) When the duty ratio DUT (k) satisfying the equation (7) is obtained by using the equation (1) and the equations (4) and (5), the following equation (9) is obtained. ) Is obtained, which becomes the equivalent control input Ueq (k). Furthermore, reaching law input Urch (k) and adaptive law input U
adp (k) is defined by the following equations (10) and (11), respectively.

[Equation 1]

Here, F and G are a reaching law control gain and an adaptive law control gain, respectively, which are set as described below. Further, ΔT is a control cycle. The above equation (9) is calculated by calculating the throttle valve opening deviation amount DTH (k + d) after the dead time d and the corresponding target value DTHR (k + d +
1) is required. 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 dead time d has elapsed, and the target value DTHR (k + d + 1) is used. As a result, the latest target value DTHR is used.

Next, reaching law input Urch and adaptive law input U
Adp determines the reaching law control gain F and the adaptive law control gain G so that the deviation state quantity can be stably placed on the switching straight line. Specifically, the disturbance V (k) is assumed, and the conditions for the switching function value σ (k) to be stable with respect to the disturbance V (k) are calculated to obtain the setting conditions for the gains F and G. As a result, it was obtained as a stable condition that the combination of the gains F and G satisfies the following formulas (12) to (14), in other words, is within the region shown by hatching in FIG.

F> 0 (12) G> 0 (13) F <2- (ΔT / 2) G (14) As described above, the equivalent control input Ueq (k) is obtained from the equations (9) to (11). , Reaching law input Urch (k) and adaptive law input U
It is possible to calculate adp (k) and calculate the duty ratio DUT (k) as the sum of these inputs.

The model parameter identifier 22 receives the input (DUT (k)) and output (TH) of the controlled object as described above.
Based on (k)), the model parameter vector of the controlled object model is calculated. Specifically, the model parameter identifier 22 uses a recursive identification algorithm (generalized recursive least squares algorithm) according to the following equation (15)
The model parameter vector θ (k) is calculated. θ (k) = θ (k−1) + KP (k) ide (k) (15) θ (k) ^T = [a1 ′, a2 ′, b1 ′, c1 ′] (16)

Where a1 ', a2', b1 'and C
1'is a model parameter before performing the limit process described later. Also, ide (k) is the following formula (17),
It is the identification error defined by (18) and (19). DTHHAT (k) is an estimated value of the throttle valve opening deviation amount DTH (k) calculated using the latest model parameter vector θ (k-1) (hereinafter referred to as "estimated throttle valve opening deviation amount"). Is. KP (k) is the following formula (20).
Is a gain coefficient vector defined by Further, P (k) in the equation (20) is a quartic square matrix calculated by the following equation (21).

[Equation 2]

[Equation 3]

By setting the coefficients λ1 and λ2 of the equation (21), the identification algorithm according to the equations (15) to (21) becomes
It is one of the following four identification algorithms. λ1 = 1, λ2 = 0 Fixed gain algorithm λ1 = 1, λ2 = 1 Least square method algorithm λ1 = 1, λ2 = λ Gradual gain algorithm (λ is a predetermined value other than 0 and 1) λ1 = λ, λ2 = 1 Weight With least squares algorithm (λ is a predetermined value other than 0 and 1)

On the other hand, in this embodiment, the following B1), B
It is required to meet the requirements 2) and B3). B1) Adaptation to changes in quasi-static dynamic characteristics and variations in characteristics of hardware "Changes in quasi-static characteristics" mean slow-changing characteristic changes such as fluctuations in power supply voltage and aging of hardware. B2) Adaptation to dynamic changes in dynamic characteristics Specifically, it means adaptation to changes in dynamic characteristics corresponding to changes in the throttle valve opening TH. B3) Model Parameter Drift Prevention It is possible to prevent a problem that an absolute value of a model parameter increases due to an influence of an identification error caused by a non-linear characteristic of a controlled object which should not be reflected in the model parameter.

First, in order to satisfy the requirements of B1) and B2), the weighted least squares algorithm is adopted by setting the coefficients λ1 and λ2 to predetermined values λ and "0", respectively. Next, the drift of model parameters will be described. As shown in FIG. 6, after the model parameters have converged to some extent, when there is a residual identification error caused by a non-linear characteristic such as the frictional characteristic of the throttle valve, or a disturbance whose average value is not zero is constantly added, , Residual identification error accumulates and causes model parameter drift.

Since such a residual identification error should not be reflected in the value of the model parameter, the dead zone function Fnl as shown in FIG. 7A is used to perform the dead zone processing. Specifically, the corrected identification error idenl (k) is calculated by the following equation (23), and the corrected identification error idenl (k) is calculated.
The model parameter vector θ (k) is calculated using (k). That is, instead of the above equation (15), the following equation (15
a) is used. As a result, the above request B3) can be satisfied. idenl (k) = Fnl (ide (k)) (23) θ (k) = θ (k-1) + KP (k) idenl (k) (15a)

The dead zone function Fnl is not limited to the one shown in FIG. 7 (a), and for example, the discontinuous dead zone function shown in FIG. 7 (b) or the dead zone function shown in FIG. 7 (c). A full dead band function may be used. However, if the incomplete dead zone function is used, the drift cannot be completely prevented.

The amplitude of the residual identification error changes according to the amount of change in the throttle valve opening TH. Therefore, in the present embodiment, the dead zone width parameter EIDNRLMT that defines the width of the dead zone shown in FIG. 7 is calculated according to the following equation (24) in accordance with the root mean square value DDTHRSQA of the change amounts of the target throttle valve opening THR. It is set (specifically, the dead band width parameter EIDNRLMT is set to increase as the root mean square value DDTHRSQA increases). This makes it possible to prevent the identification error that should be reflected in the value of the model parameter from being ignored as the residual identification error. Formula (2
4) DDTHR is the amount of change in the target throttle valve opening THR and is calculated by the following equation (25).

[Equation 4]

Since the throttle valve opening deviation amount DTH is controlled by the adaptive sliding mode controller 21 to the target value DTHR, the target value DTHR of the equation (25) is changed to the throttle valve opening deviation amount DTH in the same manner. However, the amount of change DDTH of the throttle valve opening deviation amount DTH may be calculated, and the dead band width parameter EIDNRLMT may be changed by the root mean square value DDTHRSQA obtained by replacing DDTHR in Expression (24) with DDTH.

In order to further enhance the robustness of the control system, it is effective to make the adaptive sliding mode controller 21 more stable. Therefore, in the present embodiment, limit processing is performed on each of the elements a1 ′, a2 ′, b1 ′, and c1 ′ of the model parameter vector θ (k) calculated by the equation (15), and the modified model parameter vector θL (k) (ΘL (k) ^T = [a1, a2, b
1, c1]) is calculated. Then, the adaptive sliding mode controller 21 uses the modified model parameter vector θL (k) to execute the sliding mode control. The details of the limit process will be described later with reference to the flowchart.

Next, the prediction deviation amount PR by the state predictor 23
A method of calculating EDTH will be described. First, the following equation (26)
According to (29), the matrices A and B and the vector X are
Define (k) and U (k).

[Equation 5] By rewriting the above equation (1) that defines the controlled object model using these matrices A and B and the vectors X (k) and U (k), the following equation (30) is obtained. X (k + 1) = AX (k) + BU (kd) (30)

When X (k + d) is obtained from the equation (30), the following equation (31) is obtained.

[Equation 6] Here, model parameters a1 ′, a before limit processing
Defining matrices A ′ and B ′ by the following equations (32) and (33) using 2 ′, b1 ′ and c1 ′,
The prediction vector XHAT (k + d) is given by the following equation (34).

[Equation 7]

DTHHAT (k + d), which is the element in the first row of the prediction vector XHAT (k + d), is the prediction deviation amount PREDT.
H (k), which is given by the following equation (35). PREDTH (k) = DTHHAT (k + d) = α1 × DTH (k) + α2 × DTH (k-1) + β1 × DUT (k-1) + β2 × DUT (k-2) + ... + βd × DUT (kd) + Γ1 + γ2 + ... + γd (35) where α1 is the 1st row and 1st column element of the matrix A ′ ^d , and α2
'One row and two columns elements ^d, .beta.i the matrix A' is a matrix A ^di '1 row and first column elements, .gamma.i the matrix A' B ^di
It is a 1-row, 2-column element of B '.

Prediction deviation amount P calculated by equation (35)
REDTH (k) is applied to the equation (9), and further, target values DTHR (k + d + 1), DTHR (k + d), and DTHR (k + d
-1) to DTHR (k), DTHR (k-1), and DT, respectively.
By replacing with HR (k-2), the following formula (9a) is obtained. From the equation (9a), the equivalent control input Ueq (k)
To calculate.

[Equation 8]

Further, using the prediction deviation amount PREDTH (k) calculated by the equation (35), the prediction switching function value σpre (k) is defined by the following equation (36), and the reaching law input Ur
ch (k) and adaptive law input Uadp (k) are calculated by the following equations (10a) and (11a), respectively. σpre (k) = (PREDTH (k) -DTHR (k-1)) + VPOLE (PREDTH (k-1) -DTHR (k-2)) (36)

[Equation 9]

Next, the model parameter c1 'is a parameter indicating the deviation of the default opening THDEF and the disturbance as described above. Therefore, as shown in FIG.
Although it fluctuates due to disturbance, the default opening deviation can be considered to be almost constant within a relatively short period. Therefore, in the present embodiment, the model parameter c1 ′ is statistically processed, and the center value of the variation is set to the default opening deviation thdefadp.
And is used for calculating the throttle valve opening deviation amount DTH and the target value DTHR.

The least-squares method is generally known as a statistical processing method. In this statistical processing by the least-squares method, data in a certain fixed period, that is, all of the identified model parameters c1 'are usually stored in a memory. It is executed by executing a batch operation at a certain time. However, in this collective operation method, a huge amount of memory is required to store all data, and further inverse matrix operation is required, resulting in an increase in operation amount.

Therefore, in the present embodiment, the above equations (15)-
The recursive least squares method algorithm of adaptive control shown in (21) is applied to statistical processing, and the least square center value of the model parameter c1 is set to the default opening deviation thdefad.
It is calculated as p.

Specifically, in the above equations (15) to (21), θ (k) and θ (k) ^T are replaced with thdefadp, and ζ (k) and ζ (k) ^T are replaced with “1”. Replace ide (k) with ec1 (k), replace KP (k) with KPTH (k), replace P (k) with PTH.
(k) and replace λ1 and λ2 with λ1 'and λ, respectively.
By replacing with 2 ', the following formulas (37) to (40)
To get

[Equation 10]

By setting the coefficients λ1 'and λ2', it is possible to select any of the four algorithms described above.
In Expression (39), the weighted least squares method is adopted by setting the coefficient λ1 ′ to 0 or a predetermined value other than 1 and setting the coefficient λ2 ′ to 1.

In the calculation of the above equations (37) to (40), the values to be stored are thdefadp (k + 1) and PTH.
Only (k + 1) and no inverse matrix operation is required. Therefore, by adopting the recursive least squares method algorithm, the statistical processing of the model parameter c1 by the least squares method can be performed while overcoming the drawbacks of the general least squares method.

The default opening deviation thdefadp obtained as a result of the statistical processing is applied to the equations (2) and (3), and instead of the equations (2) and (3), the following equations (41) and (42) are used. The throttle valve opening deviation amount DTH (k) and the target value DTHR (k) are calculated. DTH (k) = TH (k) -THDEF + thdefadp (41) DTHR (k) = THR (k) -THDEF + thdefadp (42)

By using the equations (41) and (42), even if the default opening THDEF deviates from the design value due to the characteristic variation of hardware or the change over time, the deviation is compensated and accurate. Control can be performed.

Next, the CPU of the ECU 7 for realizing the functions of the adaptive sliding mode controller 21, the model parameter identifier 22 and the state predictor 23 described above.
The calculation processing in will be described.

FIG. 9 is an overall flow chart of the throttle valve opening control.
The ec) is executed by the CPU of the ECU 7. Step S
At 11, the state variable setting process shown in FIG. 10 is executed.
That is, the calculation of the equations (41) and (42) is executed to calculate the throttle valve opening deviation amount DTH (k) and the target value DTHR (k).
Is calculated (FIG. 10, steps S21 and S22). Note that (k) indicating the current value may be omitted in some cases.

In step S12, the calculation of the model parameter identifier shown in FIG. 11, that is, the calculation process of the model parameter vector θ (k) according to the equation (15a) is executed, and further the limit process is executed to execute the modified model parameter vector. Calculate θL (k).

In the following step S13, the calculation of the state predictor shown in FIG. 21 is executed, and the prediction deviation amount PREDTH (k)
To calculate. Next, using the modified model parameter vector θL (k) calculated in step S12, the arithmetic processing of the control input Usl (k) shown in FIG. 22 is executed (step S
14). That is, equivalent control input Ueq, reaching law input U
The rch (k) and the adaptive law input Uadp (k) are calculated, and the control input Usl (k) (= duty ratio DUT (k)) is calculated as the sum of these inputs.

In a succeeding step S16, stability determination processing of the sliding mode controller shown in FIG. 29 is executed. That is, stability determination is performed based on the differential value of the Lyapunov function, and the stability determination flag FSMCSTAB is set. This stability determination flag FSMCSTAB is "1".
When set to, it indicates that the adaptive sliding mode controller 21 is unstable. When the stability determination flag FSMCSTAB is set to "1" and the adaptive sliding mode controller 21 becomes unstable,
Stabilize switching function setting parameter VPOLE Predetermined value XP
Set to OLE STB (FIG. 24, step S231,
(See S232), and set the equivalent control input Ueq to “0”.
Then, the control is stabilized by switching to the control using only the reaching law input Urch and the adaptive law input Uadp (see FIG. 22, steps S206 and S208). When the adaptive sliding mode controller 21 becomes unstable, further reaching law input Urch and adaptive law input U
Change the formula for calculating adp. That is, the values of the reaching law control gain F and the adaptive law control gain G are set to the controller 21.
And the reaching law input Urch and the adaptive law input Uadp are calculated without using the model parameter b1 (see FIGS. 27 and 28). By the stabilization processing as described above, the unstable state of the adaptive sliding mode controller 21 can be terminated early and the stable state can be restored. In step S17, FIG.
The default opening deviation thdefadp is calculated by executing the thdefadp calculation process shown in.

FIG. 11 is a flowchart of the calculation process of the model parameter identifier 22. In step S31, the gain coefficient vector KP (k) is calculated by the expression (20), and then the estimated throttle valve opening deviation amount DTHHAT (k) is calculated by the expression (18) (step S32).
In step S33, the calculation process of idenl (k) shown in FIG. 12 is executed, and the estimated throttle valve opening deviation amount DTHHAT (k) calculated in step S32 is applied to the equation (17) to identify the identification error ide (k ) Is calculated and
The dead zone process using the function shown in (a) is performed to calculate the corrected identification error idenl.

In the following step S34, the model parameter vector θ (k) is calculated by the equation (15a), and then the stabilization processing of the model parameter vector θ (k) is executed (step S35). That is, the limit process of each model parameter is performed to calculate the modified model parameter vector θL (k).

FIG. 12 is a flowchart of the idenl (k) calculation process executed in step S33 of FIG. In step S51, the identification error i
Calculate de (k). Next, the value of the counter CNTIDST incremented in step S53 is the predetermined value XCNTID set according to the dead time d of the control target.
It is determined whether or not ST is larger than ST (eg, set to “3” corresponding to dead time d = 2) (step S5).
2). Since the initial value of the counter CNTIDST is “0”, the process first proceeds to step S53 and the counter CNTI
Increment DST by "1" to identify error ide
(k) is set to "0" (step S54), and the process proceeds to step S55. Immediately after the identification of the model parameter vector θ (k) is started, the correct identification error cannot be obtained by the calculation by the equation (17), so that the steps S52 to S54 are performed without using the calculation result by the equation (17). id
The e (k) is set to "0".

If the answer to step S52 is affirmative (YES), the process immediately proceeds to step S55. In step S55, low-pass filter processing of the identification error ide (k) is performed. Specifically, when identifying the model parameter of the controlled object having the low-pass characteristic, the identification weight for the identification error ide (k) of the least-squares identification algorithm is as shown in FIG.
(A) has a frequency characteristic as shown by the solid line L1,
This is a characteristic in which high-frequency components are attenuated as shown by a broken line L2 by low-pass filter processing. This is for the following reason.

The frequency characteristics of the actual controlled object and the controlled object model obtained by modeling this are as shown by solid lines L3 and L4 in FIG. 13 (b), respectively. That is, when a model parameter is identified by the model parameter identifier 22 with respect to a controlled object having a low-pass characteristic (a characteristic in which a high-frequency component is attenuated), the identified model parameter is greatly influenced by the high-frequency blocking characteristic.
The gain of the controlled object model in the low frequency range becomes lower than the actual characteristic. As a result, the correction of the control input by the sliding mode controller 21 becomes overcorrection.

Therefore, the frequency characteristic of the weight of the identification algorithm is set to the characteristic shown by the broken line L2 in FIG. 13A by the low-pass filter processing, and the frequency characteristic of the controlled object model is shown in FIG. The characteristic shown by the broken line L5 is set to match the actual frequency characteristic, or the gain of the controlled object model is corrected to be slightly higher than the actual gain. As a result, overcorrection by the controller 21 can be prevented, the robustness of the control system can be enhanced, and the control system can be made more stable.

The low-pass filter processing is performed by using the past value of the identification error ide (ki) (for example, 1 corresponding to i = 1 to 10).
0 past values) are stored in the ring buffer, and the past values are multiplied by a weighting factor and added to execute. Further, the identification error ide (k) is calculated by the equation (17),
Since it is calculated using (18) and (19), similar low-pass filter processing is performed on the throttle valve opening deviation amount DTH (k) and the estimated throttle valve opening deviation amount DTHHAT (k). Alternatively, the throttle valve opening deviation amount D
TH (k-1) and DTH (k-2) and duty ratio DUT (k
The same effect can be obtained by performing the same low-pass filter processing on -d-1).

Returning to FIG. 12, in the following step S56,
The dead zone process shown in FIG. 14 is executed. In step S61 of FIG. 14, the root mean square value DDTHRSQA of the change amount of the target throttle valve opening THR is calculated by setting n = 5 in the equation (24), and the root mean square value DDTH is then calculated.
The EIDNRLMT table shown in FIG. 15 is searched according to the RSQA to calculate the dead zone width parameter EIDNRLMT (step S62).

In step S63, the identification error ide (k)
Is greater than the dead zone width parameter EIDNRLMT, and if ide (k)> EIDNRLMT, the corrected identification error idenl is calculated by the following equation (43).
(k) Calculate (step S67). idenl (k) = ide (k) -EIDNRLMT (43)

If the answer to step S63 is negative (NO), it is further determined whether or not the identification error ide (k) is smaller than the dead band width parameter EIDNRLMT minus a value (step S64), and (k) <-EIDN
If it is RLMT, the corrected identification error idenl (k) is calculated by the following equation (44) (step S65). idenl (k) = ide (k) + EIDNRLMT (44) When the identification error ide (k) is within the range of ± EIDNRLMT, the corrected identification error idenl (k) is set to "0" (step S66).

FIG. 16 is a flowchart of the stabilization processing of θ (k) executed in step S35 of FIG. In step S71, the flag FA1 used in this process is set.
STAB, FA2STAB, FB1LMT and FC1L
Initialization is performed by setting MT to "0". Then, in step S72, a shown in FIG.
The limit processing of 1'and a2 'is executed, and step S7
In 3, the limit process of b1 ′ shown in FIG. 19 is executed,
In step S74, the limit process of c1 'shown in FIG. 20 is executed.

FIG. 17 is a flowchart of the limit processing of a1 'and a2' executed in step S72 of FIG. FIG. 18 is a diagram for explaining the process of FIG. 17, and will be referred to together with FIG. In FIG. 18,
Model parameters a1 'and a2' that require limit processing
Is indicated by "x", and the range of stable combinations of model parameters a1 'and a2' is indicated by hatched areas (hereinafter referred to as "stable areas"). The process of FIG. 17 is a process of moving the combination of the model parameters a1 ′ and a2 ′ outside the stable region to within the stable region (position indicated by “◯”).

In step S81, the model parameter a
It is determined whether 2'is equal to or larger than the predetermined a2 lower limit value XIDA2L. The predetermined a2 lower limit value XIDA2L is set to a negative value larger than "-1". Predetermined a2 lower limit value XIDA2L
, The stable modified model parameters a1 and a2 are obtained even when set to “−1”, but the n-th power of the matrix A defined by the equation (26) becomes unstable (this is a1 ′). And a
2 ′ does not diverge but means that it oscillates), so it is set to a value larger than “−1”.

If a2 '<XIDA2L in step S81, the modified model parameter a2 is set to this lower limit value XIDA2L and the a2 stabilization flag F is set.
Set A2STAB to "1". a2 stabilization flag F
When A2STAB is set to "1", it indicates that the modified model parameter a2 is set to the lower limit value XIDA2L. In FIG. 18, the model parameter correction by the limit process P1 in steps S81 and S82 is
1 ”is attached to the arrow (the line with the arrow).

When the answer to step S81 is affirmative (YES), that is, when a2'≥XIDA2L, the modified model parameter a2 is set to the model parameter a2 '(step S83). Steps S84 and S8
5, the model parameter a1 ′ is the predetermined a1 lower limit value X
It is determined whether IDA1L and the predetermined upper limit value XIDA1H are within the range. Predetermined a1 lower limit value XIDA1
L is set to a value greater than or equal to −2 and less than 0, and has a predetermined value a
The 1 upper limit value XIDA1H is set to 2, for example.

When the answers to steps S84 and S85 are both affirmative (YES), that is, XIDA1L≤a
If 1 ′ ≦ XIDA1H, the modified model parameter a1 is set to the model parameter a1 ′ (step S88). On the other hand, when a1 ′ <XIDA1L,
The corrected model parameter a1 is set to the lower limit value XIDA1L and the a1 stabilization flag FA1STAB is set to "1" (steps S84 and S86). Also a
When 1 ′> XIDA1H, the modified model parameter a1 is set to the upper limit value XIDA1H, and a1
The stabilization flag FA1STAB is set to "1" (steps S85, S87). a1 stabilization flag FA1STA
When B is set to "1", the modified model parameter a
1 indicates that the lower limit value XIDA1L or the upper limit value XIDA1H is set. In FIG. 18, step S84
The correction of the model parameter by the limit process P2 of S87 is shown by the arrow line with "P2".

In step S90, it is determined whether or not the sum of the absolute value of the modified model parameter a1 and the modified model parameter a2 is less than or equal to the predetermined stability determination value XA2STAB. The predetermined stability determination value XA2STAB is set to a value close to "1" and smaller than "1" (for example, 0.99).

The straight lines L1 and L2 shown in FIG. 18 are straight lines that satisfy the following equation (45). a2 + | a1 | = XA2STAB (45) Therefore, in step S90, it is determined whether or not the combination of the modified model parameters a1 and a2 is on or below the lines L1 and L2 shown in FIG. If the answer to step S90 is affirmative (YES), the combination of the modified model parameters a1 and a2 is within the stable region in FIG. 18, so this processing is immediately terminated.

On the other hand, when the answer to step S90 is negative (NO), the modified model parameter a1 is the value obtained by subtracting the predetermined a2 lower limit value XIDA2L from the predetermined stability determination value XA2STAB (XIDA2L <0, so XA2ST
It is determined whether or not AB-XIDA2L> XA2STAB is satisfied (step S91). Then, the modified model parameter a1 is (XA2STAB-XIDA2L)
When it is below, the modified model parameter a2 is set to (XA
2STAB- | a1 |) and the a2 stabilization flag FA2STAB is set to "1" (step S92).

In step S91, the modified model parameter a
1 is larger than (XA2STAB-XIDA2L), the modified model parameter a1 is set to (XA2STAB-X
IDA2L), the modified model parameter a2 is set to the predetermined a2 lower limit value XIDA2L, and the a1 stabilization flag FA1STAB and the a2 stabilization flag FA2 are set.
Both STABs are set to "1" (step S9)
3).

In FIG. 18, steps S91 and S are performed.
The modification of the model parameter by the limit process P3 of 92 is shown by the arrow line with "P3", and the modification of the model parameter by the limit process P4 of steps S91 and S93 is shown by the arrow line with "P4". Indicated by. As described above, the limit process is executed by the process of FIG. 17 so that the model parameters a1 ′ and a2 ′ fall within the stable region shown in FIG.
1 and a2 are calculated.

FIG. 19 is a flow chart of the limit process of b1 'executed in step S73 of FIG.
In steps S101 and S102, it is determined whether or not the model parameter b1 ′ is within a range of the predetermined b1 lower limit value XIDB1L and the predetermined b1 upper limit value XIDB1H. The predetermined b1 lower limit value XIDB1L is set to a positive predetermined value (for example, 0.1), and the predetermined b1 upper limit value XIDB1H
Is set to, for example, "1".

When the answers to steps S101 and S102 are both affirmative (YES), that is, XIDB1L
When ≦ b1 ′ ≦ XIDB1H, the modified model parameter b1 is set to the model parameter b1 ′ (step S105). On the other hand, when b1 ′ <XIDB1L, the modified model parameter b1 is set to the lower limit value XIDB1L.
Set to b1 limit flag FB1LMT
Is set to "1" (steps S101 and S104).
If b1 ′> XIDB1H, the modified model parameter b1 is set to the upper limit value XIDB1H and the b1 limit flag FB1LMT is set to “1” (steps S102 and S103). When the b1 limit flag FB1LMT is set to "1", the modified model parameter b1 is set to the lower limit value XIDB1L or the upper limit value XI.
It indicates that it is set to DB1H.

FIG. 20 is a flowchart of the model parameter c1 'limit process executed in step S74 of FIG. In steps S111 and S112,
The model parameter c1 ′ is the predetermined c1 lower limit value XIDC1
It is determined whether or not it is within the range of L and the predetermined c1 upper limit value XIDC1H. The predetermined c1 lower limit value XIDC1L is set to, for example, -60, and the predetermined c1 upper limit value XIDC1H.
Is set to 60, for example.

When the answers to steps S111 and S112 are both affirmative (YES), that is, XIDC1L
When ≦ c1 ′ ≦ XIDC1H, the modified model parameter c1 is set to the model parameter c1 ′ (step S115). On the other hand, when c1 ′ <XIDC1L, the modified model parameter c1 is set to the lower limit value XIDC1L.
Set to c1 limit flag FC1LMT
Is set to "1" (steps S111 and S114).
When c1 ′> XIDC1H, the modified model parameter c1 is set to the upper limit value XIDC1H and the c1 limit flag FC1LMT is set to “1” (steps S112 and S113). When the c1 limit flag FC1LMT is set to "1", the modified model parameter c1 is set to the lower limit value XIDC1L or the upper limit value XI.
Indicates that it is set to DC1H.

FIG. 21 is a flowchart of the arithmetic processing of the state predictor executed in step S13 of FIG. In step S121, a matrix operation is executed to execute the matrix elements α1, α2, β1 to β2, and γ1 of the equation (35).
Calculate γd. In step S122, equation (35)
The predicted deviation amount PREDTH (k) is calculated by

FIG. 22 shows a control input Usl to the throttle valve driving device 10 executed in step S14 of FIG.
9 is a flowchart of a process of calculating (= DUT).
In step S201, the prediction switching function value σ shown in FIG.
The calculation process of pre is executed, and in step S202, the calculation process of the integrated value of the prediction switching function value σpre shown in FIG. 26 is executed. In step S203, the equivalent control input Ueq is calculated by the equation (9). Step S204
Then, the arithmetic processing of the reaching law input Urch shown in FIG. 27 is executed, and in step S205, the arithmetic processing of the adaptive law input Uadp shown in FIG. 28 is executed.

In step S206, it is determined whether or not the stability determination flag FSMCSTAB set in the process of FIG. 29 described later is "1". Stability determination flag FSM
When CSTAB is set to "1", it indicates that the adaptive sliding mode controller 21 is unstable.

In step S206, FSMCSTAB = 0
When the adaptive sliding mode controller 21 is stable, the control input Usl is calculated by adding the control inputs Ueq, Urch, and Uadp calculated in steps S203 to S205 (step S).
207).

On the other hand, when FSMCSTAB = 1 and the adaptive sliding mode controller 21 is unstable, the reaching law input Urch and the adaptive law input Uad are input.
The sum of p is calculated as the control input Usl. That is,
The equivalent control input Ueq is not used for calculating the control input Usl. This makes it possible to prevent the control system from becoming unstable.

In the following steps S209 and S210,
It is determined whether or not the calculated control input Usl is within the range of predetermined upper and lower limits XUSLH and XUSLL, and the control input U
If sl is within the range of the predetermined upper and lower limits, this processing is immediately terminated. On the other hand, the control input Usl is the predetermined lower limit value XU.
When it is equal to or lower than SLL, the control input Usl is set to the predetermined lower limit value XUSLL (steps S209 and S212),
When the control input Usl is greater than or equal to the predetermined upper limit value XUSLH, the control input Usl is set to the predetermined upper limit value XUSLH (steps S210, S211).

FIG. 23 is a flowchart of the calculation process of the prediction switching function value σpre executed in step S201 of FIG. In step S221, the arithmetic processing of the switching function setting parameter VPOLE shown in FIG. 24 is executed,
Then, using the above equation (36), the prediction switching function value σpre
The calculation of (k) is executed (step S222).

In the following steps S223 and S224,
The calculated predictive switching function value σpre (k) is a predetermined upper and lower limit value X.
Whether or not it is within the range of SGMH and XSGML is determined, and when the prediction switching function value σpre (k) is within the range of the predetermined upper and lower limit values, this processing is immediately terminated. On the other hand, when the prediction switching function value σpre (k) is less than or equal to the predetermined lower limit value XSGML, the prediction switching function value σpre (k) is set to the predetermined lower limit value XSGML (steps S223 and S225),
When the prediction switching function value σpre (k) is equal to or larger than the predetermined upper limit value XSGMH, the prediction switching function value σpre (k) is set to the predetermined upper limit value XSGMH (steps S224 and S22).
6).

FIG. 24 is a flowchart of the calculation process of the switching function setting parameter VPOLE executed in step S221 of FIG. In step S231, it is determined whether or not the stability determination flag FSMCSTAB is "1". When FSMCSTAB = 1 and the adaptive sliding mode controller 21 is unstable, the switching function setting parameter VPOLE is stabilized. The predetermined value XPOLESTB is set (step S232). The stabilization predetermined value XPOLESTB is set to a value larger than "-1" and very close to "-1" (for example, -0.999).

When FSMCSTAB = 0 and the adaptive sliding mode controller 21 is stable,
The change amount DD of the target value DTHR (k) is calculated by the following equation (46).
THR (k) is calculated (step S233). DDTHR (k) = DTHR (k) -DTHR (k-1) (46) In step S234, the throttle valve opening deviation amount DTH
And the change amount DD of the target value calculated in step S233
The VPOLE map is searched according to THR, and the switching function setting parameter VPOLE is calculated. As shown in FIG. 25A, the VPOLE map shows that when the throttle valve opening deviation amount DTH takes a value near 0 (the throttle valve opening T
H is set to a value close to the default opening THDEF), and is set to a value other than near 0, the throttle valve opening deviation amount DT
It is set so as to have a substantially constant value with respect to changes in H. Also, the VPOLE map is set such that the VPOLE value increases as the target value change amount DDTHR increases, as shown by the solid line in FIG.
When the throttle valve opening deviation amount DTH takes a value near 0, as shown by the broken line in the figure, the target value change amount DDTH
It is set to increase when R takes a value near 0.

That is, the target value DT of the throttle valve opening
When the change in the decreasing direction of HR is large, the switching function setting parameter VPOLE is set to a relatively small value.
As a result, it is possible to prevent the throttle valve 3 from colliding with the throttle fully closed stopper. Further, the switching function setting parameter VPOLE is set to a relatively large value near the default opening THDEF, and the controllability near the default opening THDEF can be improved.

As shown in FIG. 10C, when the throttle valve opening TH is near the fully closed opening or near the fully opened opening, the switching function setting parameter VPOLE may be set to be decreased. Good. As a result, when the throttle valve opening TH is in the vicinity of the fully closed opening or in the vicinity of the fully opened opening, the follow-up speed with respect to the target opening THR becomes slow, and the fully closed stopper of the throttle valve 3 (functions as a stopper even at the fully opened opening). Can be more reliably prevented.

In the following steps S235 and S236,
It is determined whether or not the calculated switching function setting parameter VPOLE is within the range of predetermined upper and lower limit values XPOLEH and XPOLEL. If the switching function setting parameter VPOLE is within the range of predetermined upper and lower limits, this process is immediately terminated. To do. On the other hand, when the switching function setting parameter VPOLE is equal to or lower than the predetermined lower limit value XPOLEL, the switching function setting parameter VPOLE is set to the predetermined lower limit value XPOLEL (steps S235 and S237), and the switching function setting parameter VPOLE is equal to or higher than the predetermined upper limit value XPOLEH. If there is, the switching function setting parameter VPOLE is set to the predetermined upper limit value XPOLEH (steps S236 and S23).
8).

FIG. 26 shows the integrated value SUMSI of the predictive switching function value σpre, which is executed in step S202 of FIG.
It is a flowchart of the process which calculates GMA. The integrated value SUMSIGMA is used to calculate the adaptive law input Uadp in the process of FIG. 28 described later (see the above formula (11a)).

In step S241, the integrated value SUMSIGMA is calculated by the following equation (47). Δ in the following formula
T is a calculation execution cycle. SUMSIGMA (k) = SUMSIGMA (k-1) + σpre × ΔT (47) In the subsequent steps S242 and S243, the calculated integrated value SUMSIGMA is the predetermined upper and lower limit values XSUMSH and X.
It is determined whether it is within the range of SUMSL, and the integrated value SU
If MSIGMA is within the range of the predetermined upper and lower limits, this process is immediately terminated. On the other hand, the integrated value SUMSIGMA
Is less than or equal to the predetermined lower limit value XSUMSL, the integrated value S
UMSIGMA is set to a predetermined lower limit value XSUMSL (steps S242 and S244), and the integrated value SUMSIGMA is set.
Is greater than or equal to the predetermined upper limit value XSUMSH, the integrated value S
UMSIGMA is set to a predetermined upper limit value XSUMSH (steps S243, S245).

FIG. 27 is a flowchart of the arithmetic processing of the reaching law input Urch executed in step S204 of FIG. In step S261, the stability determination flag FS
It is determined whether MCSTAB is "1". When the stability determination flag FSMCSTAB is "0" and the adaptive sliding mode controller 21 is stable,
The control gain F is set to a predetermined normal gain XKRCH (step S262), and the following formula (48) (formula (10a) above) is set.
The reaching law input Urch is calculated by the same formula (step S263). Urch = −F × σpre / b1 (48)

On the other hand, when the stability determination flag FSMCSTAB is "1" and the adaptive sliding mode controller 21 becomes unstable, the control gain F is set to the predetermined stabilizing gain XKRCHSTB (step S26).
4), the following equation (49) that does not use the model parameter b1
The reaching law input Urch is calculated by (step S26
5). Urch = −F × σpre (49)

In the following steps S266 and S267,
The calculated reaching law input Urch is a predetermined upper and lower limit value XURCH
Whether or not it is within the range of H and XURCHL is determined, and when the reaching law input Urch is within the range of the predetermined upper and lower limit values, this processing is immediately terminated. On the other hand, reaching rule input Urc
When h is equal to or lower than the predetermined lower limit value XURCHL, the reaching law input Urch is set to the predetermined lower limit value XURCHL (steps S266 and S268). When the reaching law input Urch is equal to or higher than the predetermined upper limit value XURCHH, the reaching law input Urch is set.
Set ch to a predetermined upper limit value XURCHH (step S
267, S269).

When the adaptive sliding mode controller 21 becomes unstable in this way, the control gain F is set to the predetermined stabilizing gain XKRCHSTB and the reaching law input U is obtained without using the model parameter b1.
The adaptive model parameter controller 21 can be returned to a stable state by calculating rch. When the identification by the model parameter identifier 22 becomes unstable, the adaptive sliding mode controller 21 becomes unstable, so the unstable model parameter b1
By not using, the adaptive sliding mode controller 21 can be stabilized.

FIG. 28 is a flowchart of the arithmetic processing of the adaptive law input Uadp executed in step S205 of FIG. In step S271, the stability determination flag FS
It is determined whether MCSTAB is "1". When the stability determination flag FSMCSTAB is "0" and the adaptive sliding mode controller 21 is stable,
The control gain G is set to a predetermined normal gain XKADP (step S272), and the following formula (50) (formula (11a) above) is set.
The adaptive law input Uadp is calculated by the equation corresponding to (step S273). Uadp = -G × SUMSIGMA / b1 (50)

On the other hand, when the stability determination flag FSMCSTAB is "1" and the adaptive sliding mode controller 21 becomes unstable, the control gain G is set to the predetermined stabilizing gain XKADPSTB (step S27).
4), the following equation (51) that does not use the model parameter b1
The adaptive law input Uadp is calculated by (step S27)
5). Uadp = -GxSUMSIGMA (51)

When the adaptive sliding mode controller 21 becomes unstable in this way, the control gain G is set to the predetermined stabilizing gain XKADPSTB, and the adaptive law input U is set without using the model parameter b1.
The adaptive model parameter controller 21 can be returned to a stable state by calculating adp.

FIG. 29 is a flowchart of the stability determination process of the sliding mode controller executed in step S16 of FIG. In this processing, stability determination is performed based on the differential term of the Lyapunov function, and the stability determination flag F
Set up SMCSTAB.

In step S281, the switching function change amount Dσpre is calculated by the following equation (52), and then the stability determination parameter SGMTAB is calculated by the following equation (53).
Is calculated (step S282). Dσpre = σpre (k) −σpre (k−1) (52) SGMSTAB = Dσpre × σpre (k) (53) In step S283, the stability determination parameter SGMS
It is determined whether or not TAB is less than or equal to the stability determination threshold value XSGMSTAB. When SGMSTAB> XSGMSTAB, it is determined that the controller 21 may be unstable, and the instability detection counter CNTSMCST is incremented by "1". (Step S285). Also, S
When GMTAB ≦ XSGMSTAB, the controller 21 determines that the controller 21 is stable and holds the count value of the instability detection counter CNTSMCST without incrementing it (step S284).

At step S286, the value of the instability detection counter CNTSMCST is set to the predetermined count value XSSTAB.
It is determined whether or not the following. CNTSMCST ≦ XSSTA
When it is B, the controller 21 determines that it is stable, and sets the first determination flag FSMCSTAB1 to "0" (step S287). On the other hand, CNTSMCST>
If it is XSSTAB, the controller 21 determines that it is unstable, and the first determination flag FSMCSTA
B1 is set to "1" (step S288). In addition,
The instability detection counter CNTSMCST has its count value initialized to "0" when the ignition switch is turned on.

In a succeeding step S289, the stability determination period counter CNTJUDST is decremented by "1", and then the stability determination period counter CNTJUDST is decremented.
It is determined whether the value of is "0" (step S29).
0). The stability determination period counter CNTJUDST displays a predetermined determination count value XCJ when the ignition switch is turned on.
Initialized to UDST. Therefore, the answer to step S290 is negative (NO) at first, and immediately after step S290.
Proceed to 295.

Thereafter, the stability determination period counter CNTJUD
When ST becomes "0", the process proceeds from step S290 to step S291, and it is determined whether or not the first determination flag FSMCSTAB1 is "1". Then, when the first determination flag FSMCSTAB1 is "0", the second determination flag FSMCSTAB2 is set to "0" (step S293), and when the first determination flag FSMCSTAB1 is "1", the second determination flag FSMCSTAB1 is set to "1". Flag FSMCSTAB
2 is set to "1" (step S292).

In the following step S294, the value of the stability judgment period counter CNTJUDST is set to the predetermined judgment count value X.
CJUDST is set, and the value of the instability detection counter CNTSMCST is set to "0", and step S2 is performed.
Proceed to 95. In step S295, the stability determination flag F
SMCSTAB is set to the first determination flag FSMCSTAB1.
And the second judgment flag FSMCSTAB2. The second determination flag FSMCSTAB2 is set in step S
The answer of 286 becomes affirmative (YES), and the first determination flag F
Even if SMCSTAB1 is set to "0", it is maintained at "1" until the value of the stability determination period counter CNTJUDST becomes "0". Therefore, the stability determination flag FSMCSTAB also indicates the stability determination period counter CNTJU.
It is maintained at "1" until the value of DST becomes "0".

FIG. 30 is a flow chart of the calculation processing of the default opening deviation thdefadp executed in step S17 of FIG. In step S251, the gain coefficient KPTH (k) is calculated by the following equation (54). KPTH (k) = PTH (k-1) / (1 + PTH (k-1)) (54)

Here, PTH (k-1) is the gain parameter calculated in step S253 at the previous execution of this processing. In step S252, the model parameter C calculated by the model parameter identifier computing process shown in FIG.
1'and the gain coefficient KPT calculated in step S251
H (k) is applied to the following equation (55) to calculate the default opening deviation thdefadp (k). thdefadp (k) = thdefadp (k-1) + KPTH (k) × (c1′-thdefadp (k-1)) (55)

In step S253, the gain parameter PTH (k) is calculated by the following equation (56). PTH (k) = (1-PTH (k-1) / (XDEFADPW + PTH (k-1))) * PTH (k-1) / XDEFADPW (56) The formula (56) is λ1 ′ in the formula (39). And λ
2'is set to a predetermined value XDEFADPW and "1", respectively.

By the processing of FIG. 30, the model parameter c
1'is statistically processed by the recursive weighted least squares method,
The default opening deviation thdefadp is calculated. In the present embodiment, the throttle valve drive device 10 and the ECU 7
22 corresponds to the plant, the processing of FIG. 22 corresponds to the control means, the processing of FIG. 11 corresponds to the identification means, and the processing of FIG. 12 corresponds to the identification error. 14 corresponds to the identification error correction means, step S34 of FIG. 11 corresponds to the model parameter vector calculation means, and the processing of FIG. 16 corresponds to the limiting means.

(Second Embodiment) In the first embodiment described above, the controlled object model is defined using the equation (1) including the dead time d, and the dead time d elapses using the state predictor 23. By calculating the subsequent predicted deviation amount PREDTH,
It controls the control target model including dead time. Therefore, it is necessary for the CPU to execute the calculation corresponding to the state predictor 23, and the calculation amount of the CPU becomes large. Therefore, in the present embodiment, in order to reduce the calculation load applied to the CPU, the controlled object model is defined by the following equation (1a) in which the dead time d is “0”, and the dead time d is set to “0”. The modeling error caused by is compensated by the robustness of the adaptive sliding mode control. DTH (k + 1) = a1 × DTH (k) + a2 × DTH (k-1) + b1 × DUT (k) + c1 (1a)

In order to further reduce the computational load on the CPU, a fixed gain algorithm is adopted as the model parameter identification algorithm. Further, in order to further stabilize the control, another method instead of the dead zone processing is adopted as a method for preventing the drift of the model parameter. Hereinafter, the present embodiment will be described in detail focusing on the points different from the first embodiment. The third embodiment is the same as the first embodiment except for the points described below.

FIG. 31 is a functional block diagram of a throttle valve control device realized by the ECU 7. This control device is an adaptive sliding mode controller 21a.
, Model parameter identifier 22a, model parameter scheduler 25, accelerator pedal depression amount AC
The target opening degree setting unit 24 sets the target opening degree THR of the throttle valve 3 according to C.

Adaptive sliding mode controller 2
The detected throttle valve opening TH is input to 1a, not the predicted deviation amount PREDTH, and the duty ratio DUT is calculated by the adaptive sliding mode control so that the throttle valve opening TH matches the target opening THR. It

Adaptive sliding mode controller 2
By using 1a, the same effects as those described in the first embodiment can be obtained, and the robustness of the control system with respect to the dead time of the control target can be ensured. Therefore, the modeling error caused by setting the dead time d to "0" can be compensated.

The model parameter identifier 22a calculates a modified model parameter vector θL (θL ^T = [a1, a2, b1, c1]) by a method different from that of the first embodiment, and the adaptive sliding mode controller 21a receives it. Supply. More specifically, the model parameter identifier 2
2a calculates the model parameter vector θ by correcting the reference model parameter vector θbase supplied from the model parameter scheduler 25 based on the throttle valve opening TH and the duty ratio DUT. Further, the modified model parameter vector θL is calculated by performing limit processing on the model parameter vector θ, and the modified model parameter vector θL is adapted to the adaptive sliding mode controller 21a.
Supply to. In this way, the optimum model parameters a1, a2, b1 for making the throttle valve opening TH follow the target opening THR are obtained, and further the model parameter c1 showing the disturbance and the deviation of the default opening THDEF is obtained.

The model parameter scheduler 25 uses the reference model parameter vector θbase (θbase ^T = [a1base,
a2base, b1base, c1base]) is calculated and supplied to the model parameter identifier 22a.

In this embodiment, since the controlled object model is defined by the equation (1a), the adaptive sliding mode controller 21a calculates the equivalent control input Ueq, the reaching law input Urch, and the adaptive law input Uadp by the equation ( Instead of 9a), (10a), and (11a), the following formulas (9b), (10b), and (11b) are used for calculation.

[Equation 11]

Expressions (9b) to (11b) are expressed by the above expression (9).
It is obtained by setting the dead time d of (11) to "0". The model parameter identifier 22a calculates the model parameter vector of the controlled object model based on the input (DUT (k)) and the output (TH (k)) of the controlled object as described above. Specifically, the model parameter identifier 22
For a, the model parameter vector θ (k) is calculated by the following equation (15) (reprinted). θ (k) = θ (k-1) + KP (k) ide (k) (15)

The identification error ide (k) of the equation (15) is calculated by the following equations (17) (repost), (18) (repost) and (19a).
Is defined by Expression (19a) is obtained by setting the dead time d of Expression (19) to “0”. The gain coefficient vector KP (k) is defined by the following equation (20) (reprinted), and the square matrix P (k) of the equation (20) is expressed by the following equation (21).
Calculated by (repost)

[Equation 12]

[Equation 13]

In this embodiment, in addition to satisfying the following requirements B1 to B3 as in the first embodiment, it is required to further satisfy the following requirements B4 and B5. B1) Adaptation to changes in quasi-static dynamic characteristics and variations in characteristics of hardware “Changes in quasi-static dynamic characteristics” are slow-changing characteristic changes such as fluctuations in power supply voltage and aging of hardware. B2) Adaptation to dynamic changes in dynamic characteristics Specifically, it means adaptation to changes in dynamic characteristics corresponding to changes in the throttle valve opening TH. B3) Model Parameter Drift Prevention It is possible to prevent a problem that an absolute value of a model parameter increases due to an influence of an identification error caused by a non-linear characteristic of a controlled object which should not be reflected in the model parameter. B4) Matching with Computation Ability of ECU Specifically, it is required to further reduce the computation amount. B5) Stabilization of model parameters (control performance) Specifically, it is required to suppress variations in identified model parameters as much as possible.

First, in order to satisfy the requirement B4, the fixed gain algorithm is adopted by setting the coefficients λ1 and λ2 to 1 and 0, respectively. As a result, the square matrix P (k) becomes constant, so that the calculation of Expression (21) can be omitted, and the calculation amount can be significantly reduced.

That is, when the fixed gain algorithm is adopted, the equation (20) is simplified as the following equation (20a). In the equation (20a), P is a square matrix whose constants are diagonal elements.

[Equation 14] According to the algorithm thus simplified, the amount of calculation can be reduced. However, the model parameter vector θ
The equation (15) for calculating (k) can be rewritten as the following equation (15b), and since it has the integral structure of the identification error ide (k), the model parameter drift easily occurs. θ (k) = θ (0) + KP (1) ide (1) + KP (2) ide (2) + ... + KP (k) ide (k) (15b) where θ (0) is a model parameter This is an initial value vector whose elements are the initial values of.

Therefore, in the present embodiment, in order to prevent such model parameter drift, the model parameter vector θ (k) is calculated by the following equation (15c) instead of the above equation (15b). did. θ (k) = θ (0) + DELTA ^k-1 × KP (1) ide (1) + DELTA ^k-2 × KP (2) ide (2) + …… + DELTA × KP (k-1) ide (k- 1) + KP (k) ide (k) (15c) where DELTA is the forgetting factor D as shown in the following equation.
It is a forgetting coefficient vector whose elements are ELTAi (i = 1 to 4). DELTA = [Delta1, Delta2, Delta
A3, DELTA4]

The forgetting factor DELTAi is set to a value between 0 and 1 (0 <DELTATAi <1) and has a function of gradually reducing the influence of past identification error. However, the coefficient DELTA3 related to the calculation of the model parameter b1 or the coefficient DELTA related to the calculation of the model parameter c1
Either one of 4 is set to "1" so that the forgetting coefficient is not substantially multiplied. Thus, by setting some of the elements of the forgetting factor vector DELTA to "1",
It is possible to prevent the steady deviation between the target value DTHR and the throttle valve opening deviation amount DTH from occurring. Note that the coefficients DELTA3 and DELTA4 are both "1".
Then, the effect of preventing drift of the model parameter becomes insufficient, so it is desirable to set only one of them to "1".

Rewriting equation (15c) in recurrence form,
The following formulas (15d) and (15e) are obtained. Equation (1)
The method of calculating the model parameter vector θ (k) using the following equations (15d) and (15e) instead of 5) is hereinafter referred to as the δ correction method, and dθ (k) defined by the equation (15e).
Is called "update vector". θ (k) = θ (0) + dθ (k) (15d) dθ (k) = DELTA × dθ (k-1) + KP (k) ide (k) (15e)

According to the algorithm using the δ correction method,
In addition to the drift prevention effect that satisfies the requirement B3, the model parameter stabilizing effect that satisfies the requirement B5 can be obtained. That is, the initial value vector θ (0) is always saved, and the update vector dθ (k) is also the forgetting coefficient vector DELT.
The function of A limits the possible values of the element, so that each model parameter can be stabilized near the initial value.

Further, since the model parameter is calculated while adjusting the update vector dθ (k) by the identification based on the input / output data of the actual controlled object, the model parameter suitable for the actual controlled object can be calculated, and the above request B1 Is also satisfied. Next, in order to satisfy the requirement B2, in the present embodiment, the model parameter vector θ (k) is calculated by the following equation (15f) using the reference model parameter vector θbase instead of the initial value vector θ (0) of the equation (15d). Was calculated. θ (k) = θbase + dθ (k) (15f)

Reference model parameter vector θbase
Is set according to the throttle valve opening deviation amount DTH (= TH-THDEF) by the model parameter scheduler 25, so that it can be adapted to the change in the dynamic characteristic corresponding to the change in the throttle valve opening TH. Request B2
Can meet.

As described above, in this embodiment, the fixed gain algorithm is adopted to reduce the amount of calculation of the ECU (requirement B4), and the algorithm using the δ correction method is adopted. Adapts to changes in dynamic characteristics and variations in hardware characteristics (requirement B1), stabilizes model parameters (control performance) (requirement B5), and prevents drift of model parameters (requirement B3), and adopts the model parameter scheduler 25. By doing so, the adaptation (requirement B2) to the dynamic characteristic change corresponding to the change of the throttle valve opening TH is realized.

The elements a1 ', a2', of the model parameter vector θ (k) calculated by the equation (15f),
Limit processing is performed on b1 ′ and c1 ′, and the modified model parameter vector θL (k) (θL (k) ^T = [a1,
a2, b1, c1]) is the same as in the first embodiment.

Further, the model parameter c1 'is statistically processed, and the center value of the variation is used as the default opening deviation thdef.
Calculated as adp, and the following formulas (41) (42) (repost)
The throttle valve opening deviation amount DTH and the target value DTH
The point of calculating R is also the same as in the first embodiment. DTH (k) = TH (k) -THDEF + thdefadp (41) DTHR (k) = THR (k) -THDEF + thdefadp (42)

Next, the arithmetic processing in 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 described above will be described.

FIG. 32 is an overall flow chart of throttle valve opening control. In this process, step S13 (calculation of the state predictor) of the throttle valve opening control process shown in FIG. 9 is deleted, and steps S12, S14 and S16 are executed.
These are changed to steps S12a, 14a and 16a, respectively.

In step S12a, the calculation of the model parameter identifier shown in FIG. 33, that is, the above equation (15f)
The calculation process of the model parameter vector θ (k) is executed, and the limit process is further executed to calculate the modified model parameter vector θL (k).

In step S14a, the processing of the control input Usl (k) shown in FIG. 36 is executed using the modified model parameter vector θL (k). That is, the equivalent control input U is calculated by the equations (9b), (10b) and (11b).
eq, reaching law input Urch (k) and adaptive law input Uadp
(k) is calculated, and the control input U is calculated as the sum of those inputs.
sl (k) (= duty ratio DUT (k)) is calculated.

In step S16a, the stability determination process of the sliding mode controller shown in FIG. 41 is executed. That is, the stability determination of the sliding mode controller is performed by using the switching function value σ instead of the predicted switching function value σpre, and the stability determination flag FSMCSTAB is set. This stability determination flag FSMCSTAB is
The process when set to "1" is the same as that in the first embodiment.

FIG. 33 shows the model parameter identifier 22a.
3 is a flowchart of the arithmetic processing of FIG. This process is shown in FIG.
Steps S31 to S34 of the calculation process of the model parameter identifier shown in FIG.
It is changed to a, and steps S33b and S33c are added.

In step S31a, the gain coefficient vector KP (k) is calculated by the equation (20a), and then the equation (1)
The estimated throttle valve opening deviation amount DTHHAT (k) is calculated from 8) and (19a) (step S32a). In step S33a, the calculation processing of ide (k) shown in FIG. 35 is executed to calculate the identification error ide (k). In step S33b, the update vector dθ is calculated by the equation (15e).
(k) is calculated, and then the θbase table shown in FIG. 34 is searched according to the throttle valve opening deviation amount DTH to calculate the reference model parameter vector θbase (step S33c). In the θbase table, reference model parameters a1base, a2base, and b1bas are included.
e is set. When the throttle valve opening deviation amount DTH takes a value near “0” (the throttle valve opening TH is near the default opening THDEF), the reference model parameters a1base and b1base decrease and the reference model parameter a2base increases. Is set to. In addition, the reference model parameter c1base
Is set to "0".

In step S34a, the model parameter vector θ (k) is calculated by the equation (15f), and then the model parameter vector θ is calculated as in the first embodiment.
The stabilization process of (k) is executed (step S35). That is, the limit process of each model parameter is performed to calculate the modified model parameter vector θL (k).

FIG. 35 is a flowchart of the side (k) calculation process executed in step S33a of FIG.
This process is step S of the ide (k) calculation process of FIG.
56 (dead zone process) is deleted and step S51 is changed to step S51a. That is, in the present embodiment, the delta correction method prevents the drift of the model parameter, so the dead zone process is not executed.

Further, in step S51a, the estimated throttle valve opening deviation amount DTHHAT (k) is calculated by the equations (18) and (19a), and the identification error is calculated using the estimated throttle valve opening deviation amount DTHHAT (k). Calculate ide (k). In this embodiment, since the dead time d of the controlled object model is set to "0", the predetermined value XCN of step S52 is set.
TIDST is set to “2”, for example.

FIG. 36 shows the control input Us to the throttle valve drive system 10 executed in step S14a of FIG.
It is a flowchart of the process which calculates 1 (= DUT). In this process, steps S201 to S205 of the Usl calculation process shown in FIG.
Up to S205a.

In step S201a, the switching function value σ shown in FIG. 37 is executed, and in step S202a, the integrated value of the switching function value σ shown in FIG. 38 is executed. In step S203a, the equivalent control input Ueq is calculated by the equation (9b). Step S204
In a, the arithmetic processing of the reaching law input Urch shown in FIG. 39 is executed, and in step S205a, the arithmetic processing of the adaptive law input Uadp shown in FIG. 40 is executed.

FIG. 37 is a flow chart of the calculation process of the switching function value σ executed in step S201a of FIG. This process is performed by using the prediction switching function value σpr shown in FIG.
Steps S222 to S226 of the arithmetic processing of e are changed to steps S222a to 226a, respectively.

In step S222a, the switching function value σ (k) is calculated by the equation (5). Continuing step S2
23a to S226a correspond to steps S223 to S in FIG.
In this example, “σpre” in 226 is replaced with “σ”, and the same limit processing as the processing in FIG. 23 is performed on the switching function value σ (k).

FIG. 38 shows an integrated value SUMSIGMAa of the switching function value σ executed in step S202a of FIG.
It is a flowchart of the process which calculates. This process
Step S2 of the integrated value calculation process of σpre shown in FIG.
41 to S245 through steps S241a to S2, respectively.
It has been changed to 45a. Integrated value SUMSIGMA
a is used to calculate the adaptive law input Uadp in the process of FIG. 40 described later (see the above equation (11b)).

In step S241a, the following equation (47)
The integrated value SUMSIGMAa is calculated according to a). S
UMSIGMAa (k) = SUMSIGMAa (k-1) + σ ×
ΔT (47a) Subsequent steps S242a to S245a
Then, limit processing similar to the processing of FIG. 26 is performed on the calculated integrated value SUMSIGMAa.

FIG. 39 is a flowchart of the arithmetic processing of the reaching law input Urch executed in step S204a of FIG. This process is performed by the reaching law input Ur shown in FIG.
The steps S263 and S265 of the ch arithmetic processing are changed to steps S263a and S265a, respectively.

That is, in the present embodiment, the reaching law input Urch when the adaptive sliding mode controller 21a is stable is calculated by using the switching function value σ instead of the predicted switching function value σpre (step S263a),
And the calculation of the reaching law input Urch when the adaptive sliding mode controller 21a is unstable (step S265a).

FIG. 40 is a flowchart of the arithmetic processing of the adaptive law input Uadp executed in step S205a of FIG. This process corresponds to the adaptive law input Ua shown in FIG.
The steps S273 and S275 of the dp calculation process are changed to steps S273a and S275a, respectively.

That is, in the present embodiment, the switching function value σ
Of the adaptive law input Uadp when the adaptive sliding mode controller 21a is stable (step S273a) and the adaptive law input Uadp when the adaptive sliding mode controller 21a is unstable ( Step S2
75a) is executed.

FIG. 41 is a flowchart of the stability determination process of the sliding mode controller executed in step S16a of FIG. This process corresponds to steps S281 and S281 of FIG.
1a and S282a.

In step S281a, the following equation (52a)
Then, the switching function change amount Dσ is calculated, and step S28
In 2a, the stability determination parameter SGMSTAB is calculated by the following equation (53a). That is, the stability determination is performed based on the switching function value σ instead of the predicted switching function value σpre. Dσ = σ (k) −σ (k−1) (52a) SGMSTAB = Dσ × σ (k) (53a)

In this embodiment, the throttle valve drive device 1
0 and a part of the ECU 7 (an output circuit for supplying a drive current to the motor 6) correspond to the plant, the processing of FIG. 36 corresponds to the control means, the processing of FIG. 33 corresponds to the identification means, and the processing of FIG.
33 corresponds to the identification error calculating means, step S33b of FIG. 33 corresponds to the update vector calculating means and update vector correcting means, step S34a of FIG. 33 corresponds to the model parameter vector calculating means, and step S of FIG.
35 corresponds to the limiting means.

(Third Embodiment) FIG. 42 is a block diagram showing the configuration of a control system according to the third embodiment of the present invention. This control system is a plant 101 to be controlled.
And a pH sensor 102 for detecting the pH (pH) of the mixed liquid, which is the output of the plant, and the pH sensor output V1OU
Subtractor 103 for subtracting the first reference value V1BASE from T
And a target value generator 104 that generates a control target value V1TARGET, and a manipulated variable determiner 1 that determines the first manipulated variable U1.
05, a first manipulated variable U1 and a second reference value V2BASE, and an adder 106 that outputs a second manipulated variable U2.

The subtractor 103, the target value generator 104, the manipulated variable determiner 105 and the adder 106 are, specifically, CP.
It is composed of an electronic control unit including a U, a memory, an input / output circuit, and the like. The plant 101 includes a flow rate control valve 111 that controls the flow rate of the alkaline liquid according to the second manipulated variable U2, and an agitator 112 that agitates the alkaline liquid supplied via the flow rate control valve 111 and the acidic liquid. Become. The plant 101 outputs a mixed liquid having a desired pH value by stirring an alkaline liquid and an acidic liquid.

The manipulated variable determiner 105 comprises an identifier 121 for identifying a model parameter vector of a controlled object model that models the plant 101, an adaptive sliding mode controller 122, and a predictor 123. Identifier 121, adaptive sliding mode controller 1
22 and the predictor 123 correspond to the model parameter identifier 22, the adaptive sliding mode controller 21, and the state predictor 23 in the first embodiment, respectively, and have the same functions as these.

Correspondence between the components and parameters of this embodiment and the components and parameters of the first embodiment will be described below. The pH sensor 101 corresponds to the throttle valve opening sensor 8, and the output V1OUT of the pH sensor 101 corresponds to the throttle valve opening TH.
The first target value V1BASE is the default opening THDEF
In the present embodiment, for example, the pH value corresponds to neutrality. Therefore, the deviation amount DV1 corresponds to the throttle valve opening deviation amount DTH. Further, the target value generation unit 104 corresponds to the target opening setting unit 24, and the control target value V1TARGET corresponds to the target value DTHR of the throttle valve opening deviation amount. Note that in the first embodiment, the function of the subtractor 103 is that the model parameter identifier 2
2 and the state predictor 23.

The second reference value V2BASE is added to bias the central value of the first manipulated variable U1, 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 therefore the second reference value V2BASE is substantially “0” (that is, U1 = U2 = Us).
1). In the present embodiment, the second reference value V2BASE
Is set to a value such that the opening of the flow control valve 111 is 50%.

The flow control valve 111 has a duty ratio DUT.
Corresponds to a switching element (which is included in the output circuit of the ECU 7 and whose illustration and description are omitted) whose on / off is controlled by the pulse signal of 1. and the alkaline solution corresponds to the power supply voltage.
Further, the output flow rate V2 of the flow rate control valve 111 corresponds to the drive current of the motor 6, the agitator 112 corresponds to the valve body of the motor 6 and the throttle valve 3, and the acidic liquid is the intake air added to the valve body of the throttle valve 3. It corresponds to the tube negative pressure and the biasing force of the return spring 4 and the elastic member 5. The pH value V1 of the mixed liquid output from the stirrer 112 corresponds to the actual opening degree of the throttle valve.

Because of the above correspondence, the plant 101 can be modeled as in the first embodiment and the same control method can be applied. That is, the identifier 1
21 is based on the first operation amount U1 and the deviation amount DV1.
The modified model parameter parameter vector θL is calculated by the same arithmetic processing as that of the first embodiment, and the predictor 12
3 calculates a predicted deviation amount PREDV1 based on the first operation amount U1, the deviation amount DV1, and the modified model parameter vector θL by the same calculation process as in the first embodiment, and the adaptive sliding mode controller 122
Based on the predicted deviation amount PREDV1 and the modified model parameter vector θL, the predicted deviation amount PREDV1 is set to the control target value V1TA by the same calculation processing as that of the first embodiment.
The first manipulated variable U1 is calculated so as to match RGET. Therefore, as the control target value V1TARGET,
By setting a desired relative pH value (deviation amount from the first reference value V1BASE), the plant output V1 can be made to match the desired pH value. In this embodiment,
The identifier 121 corresponds to the identification means, and the identification error calculation means,
It includes identification error correction means, model parameter vector calculation means and restriction means.

(Modification of Third Embodiment) FIG. 43 shows a modification of the configuration shown in FIG. In this modification, the plant 101 is not the plant 101 of FIG.
a is a control target. The plant 101a has a flow rate sensor 113 that detects the output flow rate V2 of the flow rate control valve 111 and a flow rate sensor output V2OUT that is the second in the plant 101a.
A feedback controller 114 for controlling the flow rate control valve 111 is added so as to match the flow rate value corresponding to the manipulated variable U2.

As described above, the same modeling and the same control method as those of the third embodiment can be applied to the plant including the local feedback loop. In the first embodiment, since the motor drive circuit is known, a detailed description will not be given. However, a current sensor for detecting the output current of the switching element controlled to be turned on / off is provided to detect the detected current value ID. However, the feedback control may be performed so as to match the current value IR corresponding to the manipulated variable Usl, and this modification corresponds to the case where such a circuit configuration is adopted in the first embodiment.

(Fourth Embodiment) FIG. 44 is a block diagram showing the structure of a control system according to the fourth embodiment of the present invention. This control system replaces the manipulated variable determiner 105 of FIG. 42 with the manipulated variable determiner 105a, and corresponds to the control system shown as the second embodiment. The third embodiment is the same as the third embodiment except for the following description.

The manipulated variable determiner 105a includes an identifier 121a.
And an adaptive sliding mode controller 122a
And a parameter scheduler 124. Identifier 121a, adaptive sliding mode controller 12
2a and the parameter scheduler 124 are the model parameter identifiers 22a and 22a in the second embodiment, respectively.
It corresponds to the adaptive sliding mode controller 21a and the model parameter scheduler 25, and has the same functions as these.

That is, the parameter scheduler 124
Is the reference model parameter vector θbas based on the deviation amount DV1 by the same calculation processing as that of the second embodiment.
Then, the identifier 121a calculates the first manipulated variable U1, the deviation amount DV1, and the reference model parameter vector θbase.
Based on the above, the modified model parameter vector θL is calculated by the same calculation processing as that of the second embodiment, and the adaptive sliding mode controller 122a determines the deviation amount DV1.
Also, based on the modified model parameter vector θL, the first manipulated variable U1 is calculated by the same calculation process as in the second embodiment so that the deviation amount DV1 matches the control target value V1TARGET. Therefore, the control target value V1TA
As RGET, a desired relative pH value (first reference value V1B
By setting the deviation amount from ASE), the plant output V1 can be made to match the desired pH value.

In the present embodiment, the identifier 121a corresponds to the identifying means, and includes the identification error calculating means, the update vector calculating means, the update vector correcting means, the model parameter vector calculating means and the limiting means.

(Modification of Fourth Embodiment) FIG. 45 shows a modification of the configuration shown in FIG. In this modification, the plant 101 is not the plant 101 of FIG.
a is a control target. The plant 101a is the same as the plant 101a of FIG. As described above, the modeling and the control method similar to those of the fourth embodiment can be applied to the plant including the local feedback loop.

(Other Embodiments) As a calculation method of the model parameter identification error ide (k), the ε correction method described below may be adopted instead of the δ correction method. That is, the model parameter vector θ (k) may be calculated by the following equation (15g) instead of the equation (15c). θ (k) = EPS ^k θ (0) + EPS ^k-1 × KP (1) ide (1) + EPS ^k-2 × KP (2) ide (2) + …… + EPS × KP (k-1) ide ( k-1) + KP (k) ide (k) (15g) Here, EPS is a forgetting factor EPS as shown by the following equation.
It is a forgetting coefficient vector having i (i = 1 to 4) as an element. EPS = [EPS1, EPS2, EPS3, EPS4]

Forgetting factors EPS1, EPS2 and EPS4
Is set to a value between 0 and 1 (0 <EPSi <1) similarly to the forgetting factor DELTAi, and has a function of gradually reducing the influence of past identification error.

However, in the case of the ε correction method, the coefficient EPS3 relating to the calculation of the model parameter b1 must be set to “1”. This is for the following reason. In the case of the ε correction method, when the identification error ide (k) becomes small, the model parameters are all values near zero. However, the model parameter b1 is expressed by the equations (9b), (10b), (11b).
Since it is applied to the denominator of, the input Usl to the controlled object diverges when the model parameter b1 approaches “0”.

Expression (15g) differs from expression (15c) in that the initial value vector θ (0) is also multiplied by the forgetting coefficient vector EPS. Rewriting the formula (15g) in the recurrence form, the following formula (15h) is obtained. Formula (15)
Instead of, the method of calculating the model parameter vector θ (k) using the following equation (15h) is called the ε correction method. θ (k) = EPS × θ (k-1) + KP (k) ide (k) (15h)

Even with the ε correction method, the past identification error ei
Since the influence of d is reduced, it is possible to prevent drift of model parameters. In addition, in the second embodiment,
Although the model parameter drift is prevented by the δ correction method, the correction identification error idenl (k) is calculated by the dead zone process (FIG. 14) as in the first embodiment, and this is used to calculate the model parameter vector. It is also possible to calculate θ (k).

In the first embodiment, the δ correction method or the ε correction method may be adopted instead of the dead zone processing. Further, when the δ correction method is adopted in the first embodiment, a model parameter scheduler is introduced as in the second embodiment, and the update vector is added to the reference model parameter vector θbase calculated by the model parameter scheduler. It is desirable to calculate the model parameter vector θ in the following format.

[0194]

As described above in detail, according to the invention described in claim 1, the update vector is calculated according to the identification error of the model parameter vector, and the update is performed so as to reduce the influence of the past value of the identification error. The vector is modified, and the modified update vector is added to the reference vector of the model parameter to calculate the model parameter vector, and the value of the element of the model parameter vector is limited within a predetermined limit range. Therefore, it is possible to further improve the control stability while preventing the drift of the model parameter.

According to the invention described in claim 2, since the update vector is calculated using the fixed gain algorithm, the amount of calculation can be reduced.

According to the third aspect of the invention, the past value of at least one element of the update vector is greater than 0 and 1 or more.
Since the update vector is corrected by multiplying it by a smaller predetermined value, the influence of the past value of the identification error is reduced, and the drift of the model parameter vector can be prevented.

According to the invention as set forth in claim 4, since the update vector is not multiplied by a predetermined value larger than 0 and smaller than 1 with respect to the element related to the input of the plant or the element not related to the input / output of the plant, It is possible to prevent the occurrence of steady deviation due to the correction of the vector.

According to the fifth aspect of the present invention, at least one element of the reference vector is also multiplied by a predetermined value larger than 0 and smaller than 1 to prevent the model parameter vector from drifting.

According to the sixth aspect of the present invention, the reference vector is calculated according to the parameter indicating the change in the dynamic characteristics of the plant, so that an appropriate reference vector according to the change in the dynamic characteristics of the plant can be obtained. . As a result, the model parameters can be quickly converged even when the plant includes a non-linear element.

According to the invention described in claim 7, when the identification error of the model parameter is within a predetermined range, the identification error is corrected in a decreasing direction, and the model parameter vector is corrected using the corrected identification error. Is calculated, and the values of the elements of the model parameter vector are further limited within a predetermined limit range. Therefore, it is possible to further improve the control stability while preventing the drift of the model parameter.

According to the eighth aspect of the present invention, the values of the plurality of elements of the model parameter vector are limited so that the plurality of elements of the model parameter vector satisfy a predetermined relationship. It is possible to improve the sex.

According to the ninth aspect of the present invention, when the identification error is within the predetermined range, the identification error is set to 0. Therefore, the influence of the identification error that should not be reflected in the value of the model parameter is reduced. It is possible to improve the effect of preventing the drift of the model parameter by eliminating it.

According to the tenth aspect of the present invention, the model parameter identified by the identifying means is used to control the throttle valve opening to match the target opening, so that the target opening of the throttle valve opening is controlled. The controllability of the throttle valve opening can be improved and the stability of the throttle valve opening control can be improved.

[Brief description of drawings]

FIG. 1 is a diagram showing a throttle valve drive system for an internal combustion engine and a control system therefor according to an embodiment of the present invention.

FIG. 2 is a diagram showing frequency characteristics of the throttle valve driving device shown in FIG.

FIG. 3 is a functional block diagram showing functions realized by the electronic control unit (ECU) of FIG.

FIG. 4 is a diagram showing a relationship between a control characteristic of a sliding mode controller and a value of a switching function setting parameter (VPOLE).

FIG. 5 is a diagram showing a setting range of control gains (F, G) of a sliding mode controller.

FIG. 6 is a diagram for explaining drift of model parameters.

FIG. 7 is a diagram showing a function for correcting an identification error.

FIG. 8 is a diagram for explaining that the default opening deviation of the throttle valve is reflected in the model parameter (c1 ′).

FIG. 9 is a flowchart of throttle valve opening control processing.

10 is a flowchart of a process of setting a state variable in the process of FIG.

11 is a flowchart of a process of executing a calculation of a model parameter identifier in the process of FIG.

12 is a flowchart of a process of executing an identification error (ide) operation in the process of FIG.

FIG. 13 is a diagram for explaining low-pass filter processing of an identification error (ide).

FIG. 14 is a flowchart of a dead zone process in the process of FIG.

15 is a diagram showing a table used in the processing of FIG.

16 is a flowchart of a stabilization process of a model parameter vector (θ) in the process of FIG.

FIG. 17 is a model parameter (a in the process of FIG.
It is a flowchart of the limit process of 1 ', a2').

FIG. 18 is a diagram for explaining changes in the values of model parameters by the processing of FIG.

FIG. 19 is a model parameter (b in the process of FIG.
It is a flowchart of the limit process of 1 ').

FIG. 20 is a diagram showing model parameters (c
It is a flowchart of the limit process of 1 ').

21 is a flowchart of a process of executing a calculation of a state predictor in the process of FIG.

22 is a flowchart of a process of executing a control input (Usl) operation in the process of FIG. 9.

FIG. 23 is a prediction switching function value (σp in the process of FIG. 22;
5 is a flowchart of a process for executing the calculation of re).

FIG. 24 is a flowchart of a process for executing a calculation of a switching function setting parameter (VPOLE) in the process of FIG.

FIG. 25 is a diagram showing a map used in the processing of FIG. 24.

FIG. 26 is a prediction switching function value (σp
5 is a flowchart of a process for executing the calculation of the integrated value of (re).

FIG. 27 is a diagram showing a reaching rule input (Urc) in the process of FIG.
It is a flowchart of the process which performs the calculation of h).

FIG. 28 is a diagram illustrating an adaptive law input (Uad
It is a flowchart of the process which performs the calculation of p).

FIG. 29 is a flowchart of a process of executing stability determination of the sliding mode controller in the process of FIG. 9.

30 is a diagram illustrating a default opening deviation (t
6 is a flowchart of a process for executing a calculation of (hdefadp).

FIG. 31 is a functional block diagram showing functions realized by the electronic control unit (ECU) of FIG. 1 (second embodiment).

FIG. 32 is a flowchart of throttle valve opening control processing (second embodiment).

FIG. 33 is a flowchart of a process of executing a calculation of a model parameter identifier in the process of FIG. 32.

FIG. 34 is a diagram showing a table used in the processing of FIG. 33.

FIG. 35 is a flowchart of a process for executing an identification error (ide) operation in the process of FIG. 33.

FIG. 36 is a flowchart of a process for executing a calculation of a control input (Usl) in the process of FIG. 32.

FIG. 37 is a flowchart of a process of executing a calculation of a switching function value (σ) in the process of FIG. 36.

FIG. 38 is a flowchart of a process of performing an integrated value of a switching function value (σ) in the process of FIG. 36.

39 is a reaching law input (Urc) in the process of FIG.
It is a flowchart of the process which performs the calculation of h).

40 is a diagram showing an adaptive law input (Uad
It is a flowchart of the process which performs the calculation of p).

41 is a flowchart of a process for executing stability determination of the sliding mode controller in the process of FIG. 32.

FIG. 42 is a block diagram showing a configuration of a control system according to a third embodiment of the present invention.

43 is a block diagram showing a modified example of the configuration shown in FIG. 42.

FIG. 44 is a block diagram showing a configuration of a control system according to a fourth embodiment of the present invention.

45 is a block diagram showing a modified example of the configuration shown in FIG. 44.

[Explanation of symbols]

DESCRIPTION OF SYMBOLS 1 Internal combustion engine 3 Throttle valve 7 Electronic control unit 10 Throttle valve drive device 21, 21a Adaptive sliding mode controller (control means) 22, 22a Model parameter identifier (identification means, identification error calculation means, update vector calculation means, update vector correction) Means, identification error correcting means, model parameter vector calculating means, limiting means) 24 target opening setting section 25 model parameter scheduler

─────────────────────────────────────────────────── ─── Continuation of front page (51) Int.Cl. ⁷ Identification code FI theme code (reference) // G05B 13/00 G05B 13/00 A (72) Inventor Jun Takahashi 1-chome, Wako-shi, Saitama Prefecture No. 1 F-term within Honda R & D Co., Ltd. (reference) 3G065 CA00 CA11 DA05 DA06 DA15 FA12 GA41 GA46 HA21 HA22 JA04 JA09 JA11 KA02 KA15 KA16 KA21 3G084 BA05 DA25 EA11 EA12 EB13 EC04 EC06 FA10 3G301 LA03 NA08 ND02 NB02 NC02 ND02 NB02 NC02 ND02 ND45 NE17 NE19 NE25 PA11A PA11Z PF03Z 5H004 GA07 GA08 GA10 GA14 GA17 GB02 GB12 HA04 HA13 HB02 HB04 HB07 KA22 KA32 KA45 KA54 KA74 KC12 KC26 KC28 KC43 KC45 LA03 LA13

Claims (10)

[Claims] 1. An identifying unit for identifying a model parameter vector of a controlled object model that models a plant based on an input and an output of the plant, and a model parameter vector identified by the identifying unit for identifying the plant. In a plant control device including control means for controlling, the identifying means includes an identification error calculating means for calculating an identification error of the model parameter vector, and an update vector calculating means for calculating an update vector according to the identification error. And an update vector correction means for correcting the update vector so as to reduce the influence of the past value of the identification error, and the model parameter vector by adding the corrected update vector to the reference vector of the model parameter. Model parameter vector calculating means for calculating, and the model The value of the element of the model parameter vector calculated by the parameter vector-calculating means, the plant control system and having a limiting means for limiting within a predetermined limit range. 2. The plant control device according to claim 1, wherein the update vector calculation means calculates the update vector using a fixed gain algorithm. 3. The update vector correction means corrects the update vector by multiplying a past value of at least one element of the update vector by a predetermined value larger than 0 and smaller than 1. The control device for the plant according to claim 1. 4. The update vector correction means does not multiply the predetermined value for an element related to the input of the plant or an element not related to the input / output of the plant of the update vector. 3. The plant control device according to item 3. 5. The plant control device according to claim 3, wherein the update vector correction means multiplies at least one element of the reference vector by the predetermined value. 6. The plant control apparatus according to claim 1, wherein the reference vector is calculated according to a parameter indicating a change in dynamic characteristics of the plant. 7. An identifying unit that identifies a model parameter vector of a controlled object model that models the plant based on an input and an output of the plant, and a model parameter vector identified by the identifying unit to identify the plant. In a control device of a plant provided with a controlling means for controlling, the identifying means is an identifying error calculating means for calculating an identifying error of the model parameter vector, and when the identifying error is within a predetermined range, the identifying Identification error correction means for correcting the error in a decreasing direction, model parameter vector calculation means for calculating the model parameter vector using the identification error corrected by the identification error correction means, and calculation by the model parameter vector calculation means The values of the elements of the model parameter vector The plant control system and having a limiting means for limiting the. 8. The limiting means limits the values of the plurality of elements so that the plurality of elements of the model parameter vector satisfy a predetermined relationship.
7. The plant control device according to any one of 1 to 7. 9. The plant control apparatus according to claim 7, wherein the identification error correction means sets the identification error to 0 when the identification error is within the predetermined range. 10. The plant includes a throttle valve drive device having a throttle valve of an internal combustion engine and a drive means for driving the throttle valve, and the control means sets the opening degree of the throttle valve to a target opening degree. The plant control device according to any one of claims 1 to 9, wherein a parameter that determines a control input to the throttle valve drive device is calculated so as to match.