JP2002339784A - Control device for throttle valve drive - Google Patents

Abstract

(57) [Problem] To provide a control device of a throttle valve drive device capable of improving a control response speed by expanding a control stability margin. SOLUTION: A state predictor 23 opens a throttle valve after a dead time d of a throttle valve driving device 10 according to a detected throttle valve opening TH and a duty ratio DUT output from an adaptive sliding mode controller 21. The degree TH is predicted, and a predicted throttle valve opening deviation amount PREDTH is calculated. The adaptive sliding mode controller 21 calculates the predicted throttle valve opening deviation amount PRE.
A duty ratio DUT is calculated according to DTH.

Description

DETAILED DESCRIPTION OF THE INVENTION

[0001]

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to a control device for a throttle valve driving device including a throttle valve of an internal combustion engine and a motor for driving the throttle valve.

[0002]

2. Description of the Related Art A throttle valve driving device provided with a motor for driving a throttle valve and a spring for biasing the throttle valve is provided with a PID (proportional to digital pressure) according to a throttle valve opening detected by a throttle valve opening sensor. , Integral,
2. Description of the Related Art A control device controlled by differential control has been conventionally known (Japanese Patent Application Laid-Open No. 8-261050).

[0003]

However, since the throttle valve drive usually includes a dead time element,
In the PID control according to the throttle valve opening detected by the throttle valve opening sensor, if the control gain is set high, the control tends to be unstable. Therefore, it is necessary to set the control gain low enough to prevent the control from becoming unstable, and there is a problem that the control response speed is reduced.

The present invention has been made in view of this point, and it is an object of the present invention to provide a control device for a throttle valve driving device capable of improving a control response speed by expanding a control stability margin. Aim.

[0005]

According to one aspect of the present invention, a throttle valve for an internal combustion engine is provided.
A control device for controlling a throttle valve driving device including a driving device for driving the throttle valve, and a control device for controlling the opening of the throttle valve to a target opening, wherein a predicting device for predicting a future throttle valve opening is provided. The throttle valve driving device is controlled according to the throttle valve opening predicted by the predicting means.

According to this configuration, the throttle valve driving device is controlled according to the future opening degree of the throttle valve predicted by the predicting means. Therefore, even if the throttle valve driving device includes a dead time element, the control is performed. , And the control response speed can be improved by increasing the control gain.

According to a second aspect of the present invention, in the control device for a throttle valve driving device according to the first aspect, the throttle valve driving device is modeled including a dead time element, and the predicting means includes: The throttle valve opening after the dead time elapses is predicted based on a control target model obtained by modeling.

[0008] According to this configuration, the throttle valve driving device is modeled including the dead time element, and the throttle valve opening after the dead time elapses is predicted based on the control target model obtained by the modeling. Therefore, compensation for the dead time existing in the throttle valve driving device can be performed with higher accuracy.

According to a third aspect of the present invention, in the control device for a throttle valve driving device according to the second aspect, an identification unit for identifying a model parameter of the model to be controlled is provided, and the prediction unit is provided with the identification unit. The throttle valve opening is predicted using the model parameters identified by

According to this configuration, since the throttle valve opening is predicted using the model parameters identified by the identification means, the dynamic characteristics of the throttle valve driving device change with time or change due to environmental conditions and the like. Even in this case, it is possible to calculate an accurate predicted throttle valve opening.

[0011] The invention described in claim 4 is the first to third aspects of the present invention.
The control device for a throttle valve driving device according to any one of the preceding claims, further comprising a sliding mode controller that controls the throttle valve driving device by sliding mode control according to the throttle valve opening predicted by the prediction means. And

According to this configuration, since the throttle valve driving device is controlled by the sliding mode control having high robustness, modeling is performed by a difference between the actual dead time of the throttle valve driving device and the dead time of the control target model. Even when an error (difference between the characteristics of the actual plant and the characteristics of the modeled control target model) occurs, high controllability can be stably maintained.

According to a fifth aspect of the present invention, in the control device for a throttle valve driving device according to the fourth aspect, there is provided identification means for identifying a model parameter of the model to be controlled, and the sliding mode controller comprises The throttle valve driving device is controlled using the model parameters identified by the means.

According to this configuration, the sliding mode controller controls the throttle valve driving device using the model parameters identified by the identification means, so that a modeling error can be reduced and controllability is improved. be able to.

According to a sixth aspect of the present invention, in the control device for a throttle valve driving device according to the fourth or fifth aspect,
The control input to the throttle valve driving device by the sliding mode controller includes an adaptive law input. According to this configuration, since the control input to the throttle valve driving device includes the adaptive law input, good controllability can be realized even if there is a disturbance or a modeling error.

[0016]

Embodiments of the present invention will be described below with reference to the drawings. FIG. 1 is a diagram showing a configuration of a throttle valve control device according to one embodiment of the present invention. A throttle valve 3 is provided in an intake passage 2 of an internal combustion engine (hereinafter, referred to as “engine”) 1. The throttle valve 3 has a return spring 4 as a first urging means for urging the throttle valve 3 in the valve closing direction, and an elasticity as a second urging means for urging the throttle valve 3 in the valve opening direction. The member 5 is attached. The throttle valve 3 is configured to be driven via a gear (not shown) by a motor 6 as driving means. When the driving force of the motor 6 is not applied to the throttle valve 3, the throttle valve 3
Is the default opening THDEF at which the urging force of the return spring 4 and the urging force of the elastic member 5 are balanced.
(For example, 5 degrees).

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

The ECU 7 is connected to 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 is supplied to the ECU 7. You. EC
U7 is an input circuit to which detection signals of the throttle valve opening sensor 8 and the accelerator sensor 9 are supplied, an AD conversion circuit for converting the input signal into a digital signal, a central processing unit (CPU) for executing various arithmetic processes, and a CPU. A memory for storing a program to be executed, a map and a table referred to by the program, and an output circuit for supplying a drive current to the motor 6 are provided. 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 such 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 this embodiment, a throttle valve driving device 10 including a throttle valve 3, a return spring 4, an elastic member 5, and a motor 6 is a control target, and a duty ratio of an electric signal for applying an input to the control target to the motor 6 is provided. The output of the control target is a throttle valve opening TH detected by the throttle valve opening sensor 8.

When the response frequency characteristics of the throttle valve driving device 10 are actually measured, gain characteristics and phase characteristics indicated by solid lines in FIG. 2 are obtained. Therefore, a model defined by the following equation (1) was set as a control target model. The response frequency characteristic of this model is shown by a broken line in FIG. 2, and it has been confirmed that the response frequency characteristic is close to the characteristic of the throttle valve driving device 10. DTH (k + 1) = a1.times.DTH (k) + a2.times.DTH (k-1) + b1.times.DUT (kd) + c1 (1) where k is a parameter representing a 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, a of the formula (1)
2, b1 and c1 are model parameters that determine the characteristics of the control target model, and d is the dead time.

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

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

Adaptive sliding mode controller 2
1 indicates that the detected throttle valve opening TH is equal to the target opening THR.
The duty ratio DUT is calculated by the adaptive sliding mode control so as to coincide with the above, and the calculated duty ratio DUT is output. By using the adaptive sliding mode controller 21, the throttle valve opening TH
Can be changed appropriately using a predetermined parameter (VPOLE), and as a result, the impact (throttle) when the throttle valve 3 is moved from the open position to the fully closed position can be changed. This makes it possible to avoid collision with the fully closed stopper) and to vary the engine response to the accelerator operation. In addition, it is possible to ensure stability against errors in model parameters.

The model parameter identifier 22, the corrected model parameter vector ^{θL (θL T = [a1,} a2, b
1, c1]) and supplies the calculated value to the adaptive sliding mode controller 21. More specifically, the model parameter identifier 22 calculates the model parameter vector θ based on the throttle valve opening TH and the duty ratio DUT.
Is calculated. Further, the model parameter vector θ
To calculate the modified model parameter vector θL, and supplies the modified model parameter vector θL to the adaptive sliding mode controller 21. Thus, the throttle valve opening TH
Model parameters a1, a2, and b1 are obtained to make the model follow the target opening THR, and further, a model parameter c indicating a disturbance and a deviation of the default opening THDEF.
1 is obtained.

By using the model parameter identifier 22 for identifying model parameters in real time, it is possible to adapt to changes in engine operating conditions, compensate for variations in hardware characteristics, compensate for variations in power supply voltage, and aging of hardware characteristics. Adaptation to is possible.

The state predictor 23 calculates the throttle valve opening TH
The throttle valve opening TH (predicted value) after the dead time d, more specifically, the predicted deviation amount PREDTH is calculated based on the duty ratio DUT and supplied to the adaptive sliding mode controller 21. Predicted deviation PREDTH
Is used, it is possible to ensure the robustness of the control system with respect to the dead time of the control target, and to improve the controllability especially near 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 a deviation amount between the target opening THR (k) and the default opening THDEF by the following equation (3). DTHR (k) = THR (k) -THDEF (3) Here, the throttle valve opening deviation amount DTH and the target value DT
If the deviation e (k) from 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 (k).
On the phase plane defined as (k-1), the combination of the deviation e (k) satisfying 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 focuses 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 the combination is on the switching line on the phase plane, and robust against disturbances and modeling errors (differences between the characteristics of the actual plant and the characteristics of the model to be controlled). In this case, the throttle valve opening deviation amount DTH follows the target value DTHR.

The switching function setting parameter V in equation (5)
By changing the value of POLE, it is possible to change the attenuation characteristic of the deviation e (k), that is, the characteristic of the throttle valve opening deviation amount DTH following the target value DTHR, as shown in FIG. Specifically, if 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 decreases.

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 (the biasing force of the return spring 4 and the biasing force of the elastic member 5). The controllability is improved for the change of the elastic characteristic caused by the balance of the motor, 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
In the vicinity of the EF, it is necessary to increase the convergence speed.

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

As described above, in the sliding mode control, the combination of the deviation e (k) and the previous deviation e (k-1) (hereinafter referred to as "deviation state quantity") is constrained on the switching straight line, thereby obtaining the deviation e ( k) is converged to 0 at the designated convergence speed and robustly against disturbances and modeling errors. Therefore, in the sliding mode control, it is important how to put the deviation state quantity on the switching straight line and restrict it there.

From such a viewpoint, the input (output of the controller) DUT (k) (also referred to as Usl (k)) to the control object is expressed 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
It is configured 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 constraining the deviation state quantity on the switching line.
rch (k) is an input for placing the deviation state quantity on the switching straight line, and the adaptive law input Uadp (k) is for suppressing the effects of modeling errors and disturbances and for placing the deviation state quantity on the switching straight line. Is input. Hereinafter, 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 restricting 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) that satisfies Expression (7) is obtained using Expression (1) and Expressions (4) and (5), the following expression (9) is obtained. ), Which is the equivalent control input Ueq (k). Further, the reaching law input Urch (k) and the 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, and are set as described below. ΔT is a control cycle. In the calculation of the above equation (9), the throttle valve opening deviation amount DTH (k + d) after the elapse of the dead time d and the corresponding target value DTHR (k + d +
1) is necessary. Therefore, the target deviation DTHR (k + d + 1) is calculated by using the predicted deviation PREDTH (k) calculated by the state predictor 23 as the throttle valve opening deviation DTH (k + d) after the elapse of the dead time d. , The latest target value DTHR is used.

Next, the reaching law input Urch and the adaptive law input U
The reaching law control gain F and the adaptive law control gain G are determined by adp such that the deviation state quantity is stably placed on the switching straight line. Specifically, a condition for the switching function value σ (k) to be stable with respect to the disturbance V (k) is obtained by assuming the disturbance V (k), thereby obtaining the setting conditions of 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 expressions (12) to (14), in other words, that the combination is within the region indicated 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 by the equations (9) to (11). , Reaching law input Urch (k) and adaptive law input U
adp (k) is calculated, and the duty ratio DUT (k) can be calculated as the sum of those inputs.

As described above, the model parameter identifier 22 inputs (DUT (k)) and outputs (TH
(k)), a model parameter vector of the control target model is calculated. Specifically, the model parameter identifier 22 uses a sequential identification algorithm (generalized sequential least squares algorithm) according to the following equation (15).
Calculate the model parameter vector θ (k). θ (k) = θ (k−1) + KP (k) ide (k) (15) θ (k) ^T = [a1 ′, a2 ′, b1 ′, c1 ′] (16)

Here, a1 ', a2', b1 'and C
1 ′ is a model parameter before performing a limit process described later. Also, ide (k) is given by the following equation (17):
This 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”). It is. KP (k) is calculated by the following equation (20).
Is a gain coefficient vector defined by Further, P (k) in Expression (20) is a fourth-order square matrix calculated by Expression (21) below.

(Equation 2)

(Equation 3)

By setting the coefficients λ1 and λ2 in the equation (21), the identification algorithm based on the equations (15) to (21)
It will be one of the following four identification algorithms: λ1 = 1, λ2 = 0 Fixed gain algorithm λ1 = 1, λ2 = 1 Least squares algorithm λ1 = 1, λ2 = λ Gradual decreasing gain algorithm (λ is a predetermined value other than 0, 1) λ1 = λ, λ2 = 1 Weight Least-squares algorithm (λ is a predetermined value other than 0 and 1)

On the other hand, in the present embodiment, the following B1), B
It is required to satisfy the requirements of 2) and B3). B1) Adaptation to Quasi-Static Dynamic Characteristics Change and Hardware Characteristics Variation “Quasi-static dynamic characteristics change” means a characteristic change with a slow change speed such as a power supply voltage fluctuation or hardware aging. 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) Prevention of Drift of Model Parameter A problem that the absolute value of the model parameter increases due to the influence of the identification error caused by the non-linear characteristic of the controlled object that should not be reflected in the model parameter is prevented.

First, in order to satisfy the above requirements B1) and B2), a weighted least squares algorithm is adopted by setting the coefficients λ1 and λ2 to predetermined values λ and “0”, respectively. Next, the drift of the model parameters will be described. As shown in FIG. 6, when the model parameters converge to some extent, there is a residual identification error caused by non-linear characteristics such as the friction characteristics of the throttle valve, or when a disturbance having a non-zero average value is constantly applied. , Residual identification errors accumulate, causing model parameter drift.

Since such a residual identification error should not be reflected in the value of the model parameter, the dead zone processing is performed using a dead zone function Fnl as shown in FIG. Specifically, the corrected identification error idenl (k) is calculated by the following equation (23), and the corrected identification error idenl (k) is calculated.
(k) is used to calculate the model parameter vector θ (k). That is, the following equation (15) is used instead of the above equation (15).
Use a). Thereby, the above requirement 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. 7A, but may be, for example, a discontinuous dead zone function as shown in FIG. A complete dead band function may be used. However, when an incomplete dead zone function is used, drift cannot be completely prevented.

The amplitude of the residual identification error changes in accordance with the variation of the throttle valve opening TH. Therefore, in the present embodiment, the dead zone width parameter EIDNLLMT defining the dead zone width shown in FIG. 7 is changed according to the root mean square value DDTHRSQA of the amount of change in the target throttle valve opening THR calculated by the following equation (24). (Specifically, as the root mean square value DDTHRSQA increases, the dead zone width parameter EIDNRLMT is set to increase). Thus, it is possible to prevent the identification error to be reflected in the value of the model parameter from being ignored as the residual identification error. Equation (2
DDTHR of 4) is a change amount of the target throttle valve opening THR, and is calculated by the following equation (25).

(Equation 4)

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

To further enhance the robustness of the control system, it is effective to further stabilize the adaptive sliding mode controller 21. Therefore, in the present embodiment, a limit process 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 corrected model parameter vector θL (k) (ΘL (k) ^T = [a1, a2, b
1, c1]). Then, the adaptive sliding mode controller 21 executes the sliding mode control using the modified model parameter vector θL (k). The details of the limit process will be described later with reference to a flowchart.

Next, the prediction deviation 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
(k) and U (k) are defined.

(Equation 5) Using the matrices A and B and the vectors X (k) and U (k), the above equation (1) defining the control target model is rewritten to obtain the following equation (30). X (k + 1) = AX (k) + BU (kd) (30)

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

(Equation 6) Here, the model parameters a1 ′, a
When matrices A ′ and B ′ are defined 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 an element of the first row of the predicted vector XHAT (k + d), is calculated using the predicted 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) here, α1 is one row and one column element of the matrix a ^'d, 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
This is a 1-line, 2-column element of B '.

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

(Equation 8)

Using the predicted deviation amount PREDTH (k) calculated by the equation (35), a prediction switching function value σpre (k) is defined by the following equation (36), and the reaching law input Ur
The ch (k) and the 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, as described above, the model parameter c1 'is a parameter indicating a deviation of the default opening THDEF and a disturbance. Therefore, as shown in FIG.
Although it fluctuates due to disturbance, the default opening deviation can be regarded as substantially constant within a relatively short period. Therefore, in the present embodiment, the model parameter c1 ′ is statistically processed, and the central value of the variation is set to the default opening deviation thdefadp.
And used for calculating the throttle valve opening deviation amount DTH and the target value DTHR.

As a method of statistical processing, the least square method is generally known. In the statistical processing based on the least square method, data within a certain period, that is, all identified model parameters c1 'are usually stored in a memory. Is executed by performing a batch operation at a certain point in time. However, this batch operation method requires an enormous amount of memory to store all data, and further requires an inverse matrix operation, resulting in an increase in the amount of operation.

Therefore, in this embodiment, the above equations (15) to (15)
The adaptive least-squares 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 formulas (15) to (21), θ (k) and θ (k) ^T are replaced by thdefadp, and ζ (k) and ζ (k) ^T are replaced by “1”. And replace ide (k) with ec1 (k), replace KP (k) with KPTH (k), and replace P (k) with PTH
(k), and λ1 and λ2 are respectively λ1 ′ and λ
By substituting 2 ′, the following formulas (37) to (40)
Get.

(Equation 10)

By setting the coefficients λ1 ′ and λ2 ′, any one of the above four algorithms can be selected.
In Equation (39), the weighted least squares method is adopted by setting the coefficient λ1 ′ to a predetermined value other than 0 or 1, and setting the coefficient λ2 ′ to 1.

In the calculations 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 employing the recursive least squares algorithm, the statistical processing of the model parameter c1 can be performed by the least squares method 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 above equations (2) and (3), and is replaced by the following equations (41) and (42) instead of the equations (2) and (3). , 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 equations (41) and (42), even if the default opening THDEF deviates from the design value due to hardware characteristic variations or changes over time, the deviation is compensated for 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 flowchart of the throttle valve opening control. This processing is performed for a predetermined time (for example, 2 ms
This is executed by the CPU of the ECU 7 every ec). Step S
At 11, the state variable setting process shown in FIG. 10 is executed.
That is, the calculations of the equations (41) and (42) are executed, and the throttle valve opening deviation amount DTH (k) and the target value DTHR (k) are calculated.
Is calculated (FIG. 10, steps S21 and S22). Note that (k) indicating the current value may be omitted.

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

In the following step S13, the operation of the state predictor shown in FIG. 21 is executed, and the predicted deviation amount PREDTH (k) is calculated.
Is calculated. Next, using the corrected 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 S12).
14). That is, the equivalent control input Ueq and the reaching law input U
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 those inputs.

In the following step S16, a stability determination process of the sliding mode controller shown in FIG. 29 is executed. That is, the 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".
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,
The switching function setting parameter VPOLE is stabilized to a predetermined value XP.
OLESTB (FIG. 24, step S231, step S231).
At the same time, the equivalent control input Ueq is set to “0”.
By switching to control using only the reaching law input Urch and the adaptive law input Uadp, the control is stabilized (see FIG. 22, steps S206 and S208). When the adaptive sliding mode controller 21 becomes unstable, the reaching law input Urch and the 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
Is changed to a value for stabilizing, 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 above stabilization processing, the unstable state of the adaptive sliding mode controller 21 can be ended early and returned to the stable state.

In step S17, the thd shown in FIG.
efadp calculation processing is performed, and the default opening deviation th
Calculate defadp.

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

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, a limit process of each model parameter is performed to calculate a corrected 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 is calculated according to equation (17).
Calculate de (k). Next, the value of the counter CNTIDST that is incremented in step S53 is set to a predetermined value XCNTID set according to the dead time d of the control object.
It is determined whether or not it is larger than ST (for example, set to “3” corresponding to the 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 CNTIST
DST is incremented by “1” and the identification 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, a correct identification error cannot be obtained by the calculation using Expression (17). id
e (k) is set to “0”.

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

The frequency characteristics of the actual control target and the control target model obtained by modeling the control target are indicated by solid lines L3 and L4 in FIG. 13B, respectively. In other words, when the model parameters are identified by the model parameter identifier 22 for the controlled object having low-pass characteristics (characteristics in which high-frequency components are attenuated), the identified model parameters are greatly affected by the high-frequency rejection characteristics.
The gain of the control target 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 overcorrected.

Therefore, the frequency characteristics of the weight of the identification algorithm are set to the characteristics shown by the broken line L2 in FIG. 13A by the low-pass filter processing, so that the frequency characteristics of the control target model are changed to those shown in FIG. The characteristic is indicated by a broken line L5, and is made to match the actual frequency characteristic, or is corrected so that the gain of the control target model is slightly higher than the actual gain. Thus, overcorrection by the controller 21 can be prevented, the robustness of the control system can be increased, and the control system can be further stabilized.

In the low-pass filter processing, the past value ide (ki) of the identification error (for example, 1 corresponding to i = 1 to 10)
This is performed by storing zero past values in a ring buffer, multiplying those past values by a weighting coefficient, and adding them. Further, the identification error ide (k) is calculated by the equation (17),
(18) and (19), the same low-pass filter processing is performed on the throttle valve opening deviation DTH (k) and the estimated throttle valve opening deviation DTHHAT (k). Alternatively, the throttle valve opening deviation amount D
TH (k-1) and DTH (k-2), and the duty ratio DUT (k
The same effect can be obtained by performing the same low-pass filter processing as in -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 amount of change in the target throttle valve opening THR is calculated with, for example, n = 5 in the equation (24), and then the root-mean-square value DDTH is calculated.
The EIDNRLMT table shown in FIG. 15 is searched according to the RSQA to calculate a dead zone width parameter EIDNRLMT (step S62).

In step S63, the identification error ide (k)
Is larger than the dead band width parameter EIDNRLMT. When ide (k)> EIDNRLMT, the modified identification error idenl is calculated by the following equation (43).
(k) Calculate (step S67). idlen (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 a value obtained by adding a minus sign to the dead band width parameter EIDNRLMT (step S64). (k) <-EIDN
If it is RLMT, the modified identification error idenl (k) is calculated by the following equation (44) (step S65). idlen (k) = ide (k) + EIDNRLMT (44)

The identification error ide (k) is ± EIDNRL
When within the range of MT, the modified 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 a step S71, a flag FA1 used in this processing is set.
STAB, FA2STAB, FB1LMT and FC1L
Initialization is performed by setting MT to “0”. Then, in step S72, a shown in FIG.
1 ′ and a2 ′ limit processing is executed, and step S7
In 3, the limit processing of b1 'shown in FIG.
In step S74, a 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 is referred to together with FIG. In FIG.
Model parameters a1 'and a2' requiring limit processing
Are indicated by "x", and the range of the stable combination of the model parameters a1 'and a2' is indicated by a hatched area (hereinafter referred to as "stable area"). The process of FIG. 17 is a process of moving the combination of the model parameters a1 ′ and a2 ′ outside the stable region to the inside of the stable region (the position indicated by “○”).

In step S81, model parameters a
It is determined whether or not 2 ′ is greater than or equal to a predetermined a2 lower limit value XIDA2L. The predetermined a2 lower limit value XIDA2L is set to a negative value larger than “−1”. Predetermined a2 lower limit value XIDA2L
Can be set to “−1”, stable modified model parameters a1 and a2 can be obtained, but the nth power of the matrix A defined by the equation (26) becomes unstable (this is because a1 ′ And a
2 ′ does not diverge, but vibrates), so that the value is set to a value larger than “−1”.

If it is determined in step S81 that a2 '<XIDA2L, the modified model parameter a2 is set to this lower limit value XIDA2L, and the a2 stabilization flag F2 is set.
Set A2STAB to “1”. a2 stabilization flag F
When A2STAB is set to “1”, it indicates that the modified model parameter a2 has been set to the lower limit XIDA2L. In FIG. 18, the modification of the model parameters by the limit processing P1 in steps S81 and S82 is "P
This is indicated by an arrow line with a “1” (a line with an arrow).

If the answer to step S81 is affirmative (YES), that is, if a2 '≧ XIDA2L, the corrected model parameter a2 is set to the model parameter a2' (step S83). Step S84 and step S8
5, the model parameter a1 ′ is set to a predetermined a1 lower limit value X
It is determined whether or not it is within a range defined by IDA1L and a predetermined a1 upper limit value XIDA1H. Predetermined a1 lower limit value XIDA1
L is set to a value equal to or greater than −2 and smaller than 0,
One upper limit value XIDA1H is set to 2, for example.

When the answers of 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 modified 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). And 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 and 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 has been set. In FIG. 18, step S84
The correction of the model parameters by the limit processing P2 of S87 to S87 is indicated by an arrow with “P2”.

In step S90, it is determined whether or not the sum of the absolute value of the corrected model parameter a1 and the corrected model parameter a2 is equal to or smaller than a 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 satisfying the following equation (45). a2 + | a1 | = XA2STAB (45) Accordingly, in step S90, it is determined whether or not the combination of the modified model parameters a1 and a2 is on or below the straight 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 of FIG.

On the other hand, if the answer to step S90 is negative (NO), the modified model parameter a1 is a value obtained by subtracting the predetermined a2 lower limit value XIDA2L from the predetermined stability determination value XA2STAB (XDA2L <0, so XA2ST
AB-XIDA2L> XA2STAB holds) or not (step S91). And the corrected model parameter a1 is (XA2STAB-XIDA2L)
If the following, the modified model parameter a2 is set to (XA
2STAB- | a1 |) and the a2 stabilization flag FA2STAB is set to "1" (step S92).

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

In FIG. 18, steps S91 and S91
The correction of model parameters by the limit processing P3 of 92 is indicated by an arrow with “P3”, and the correction of model parameters by the limit processing P4 of steps S91 and S93 is indicated by an arrow with “P4”. Indicated by

As described above, the limit processing is executed by the processing of FIG. 17 so that the model parameters a1 'and a2' fall within the stable region shown in FIG. 18, and the corrected model parameters a1 and a2 are calculated.

FIG. 19 is a flowchart 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 defined by a predetermined b1 lower limit XIDB1L and a predetermined b1 upper limit 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.
Is set to, for example, “1”.

When the answers in steps S101 and S102 are both affirmative (YES), ie, XIDB1L
If ≦ 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.
And the b1 limit flag FB1LMT
Is set to "1" (steps S101, S104).
If b1 ′> XIDB1H, the correction 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.
DB1H is set.

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

When the answers of steps S111 and S112 are both affirmative (YES), that is, XIDC1L
If ≦ 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.
And the c1 limit flag FC1LMT
Is set to "1" (steps S111, S114).
If c1 ′> XIDC1H, the correction 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 DC1H has been set.

FIG. 21 is a flowchart of the operation of the state predictor executed in step S13 of FIG. In step S121, a matrix operation is performed to calculate the matrix elements α1, α2, β1 to β2, and γ1 of the equation (35).
Γγd is calculated. In step S122, equation (35)
To calculate the predicted deviation amount PREDTH (k).

FIG. 22 shows a control input Usl to the throttle valve driving device 10 executed in step S14 of FIG.
It is a flowchart of a process of calculating (= DUT).
In step S201, the prediction switching function value σ shown in FIG.
The arithmetic processing of pre is performed, and in step S202, the arithmetic processing of the integrated value of the prediction switching function value σpre illustrated 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 a stability determination flag FSMCSTAB set in a process of FIG. 29 described later is "1". Stability determination flag FSM
CSTAB, when set to “1”, 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 S20).
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
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 can 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
When sl is within the range of the predetermined upper and lower limit values, the present process is immediately terminated. On the other hand, when the control input Usl is
If it is not more than SLL, the control input Usl is set to a predetermined lower limit value XUSLL (steps S209 and S212),
When the control input Usl is equal to or larger than the predetermined upper limit XUSLH, the control input Usl is set to the predetermined upper limit XUSLH (steps S210 and S211).

FIG. 23 is a flowchart of the calculation process of the predicted switching function value σpre executed in step S201 of FIG. In step S221, a calculation process of the switching function setting parameter VPOLE shown in FIG.
Next, according to the above equation (36), the prediction switching function value σpre
The calculation of (k) is performed (step S222).

In the following steps S223 and S224,
The calculated predicted switching function value σpre (k) is equal to a predetermined upper / lower limit X
It is determined whether or not it is within the range of SGMH and XSGML. If the predicted switching function value σpre (k) is within the range of the predetermined upper and lower limits, the present process is immediately terminated. On the other hand, when the predicted switching function value σpre (k) is equal to or smaller than the predetermined lower limit value XSGML, the predicted switching function value σpre (k) is set to the predetermined lower limit value XSGML (steps S223 and S225).
When the predicted switching function value σpre (k) is equal to or larger than the predetermined upper limit value XSGMH, the predicted 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 a 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". If FSMCSTAB = 1 and the adaptive sliding mode controller 21 is unstable, the switching function setting parameter VPOLE is stabilized. It is set to a predetermined value XPOLESTB (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
A VPOLE map is searched according to the THR, and a switching function setting parameter VPOLE is calculated. As shown in FIG. 25A, the VPOLE map indicates that the throttle valve opening deviation amount DTH takes a value near 0 (the throttle valve opening T
When H takes a value near the default opening THDEF), the throttle valve opening deviation DT increases at values other than near 0.
The value is set to be substantially constant with respect to the change of H. The VPOLE map is set such that the VPOLE value increases as the target value change amount DDTHR increases, as indicated by the solid line in FIG.
When the throttle valve opening deviation amount DTH takes a value near 0, the target value change amount DDTH is indicated by a broken line in FIG.
It is set to increase when R takes a value near 0.

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

As shown in FIG. 11C, when the throttle valve opening TH is in the vicinity of the fully closed position or in the vicinity of the fully opened position, the switching function setting parameter VPOLE is set to be decreased. Good. Thus, when the throttle valve opening TH is near the full-closed opening or near the fully-opened position, the follow-up speed to the target opening THR becomes slow, and the fully-closed stopper of the throttle valve 3 (even at the fully-opened position functions as a stopper). ) 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 the predetermined upper and lower limit values XPOLEH and XPOLEL. I do. On the other hand, when the switching function setting parameter VPOLE is equal to or smaller than the predetermined lower limit XPOLEL, the switching function setting parameter VPOLE is set to the predetermined lower limit XPOLEL (steps S235 and S237). If there is, the switching function setting parameter VPOLE is set to a predetermined upper limit XPOLEH (steps S236 and S23).
8).

FIG. 26 shows the sum SUMSI of the predicted switching function value σpre executed in step S202 of FIG.
It is a flowchart of a process of calculating GMA. The integrated value SUMSIGMA is used to calculate the adaptive law input Uadp in the processing of FIG. 28 described later (see the above equation (11a)).

In step S241, an integrated value SUMSIGMA is calculated by the following equation (47). Δ in the following equation
T is the execution cycle of the operation. SUMSIGMA (k) = SUMSIGMA (k-1) + σpre × ΔT (47) In the following steps S242 and S243, the calculated integrated value SUMSIGMA is set to the predetermined upper and lower limit values XSUMSH and X
It is determined whether or not it is within the range of SUMSL, and the integrated value SU is determined.
When MSIGMA is within the range of the predetermined upper and lower limit values, the present process is immediately terminated. On the other hand, the integrated value SUMSIGMA
Is less than or equal to a 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 a 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 calculation process of the reaching law input Urch executed in step S204 of FIG. In step S261, the stability determination flag FS
It is determined whether or not 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 equation (48) (the above equation (10a)) is used.
The reaching law input Urch is calculated by the same equation as in (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 a predetermined stabilization gain XKRCHSTB (step S26).
4), the following equation (49) without using the model parameter b1
Is used to calculate the reaching law input Urch (Step S26)
5). Urch = −F × σpre (49)

In the following steps S266 and S267,
The calculated reaching law input Urch is equal to a predetermined upper / lower limit value XURCH
It is determined whether or not it is within the range of H and XURCHL. If the reaching law input Urch is within the range of the predetermined upper and lower limit values, the process is immediately terminated. On the other hand, reaching rule input Urc
If h is equal to or smaller 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). If the reaching law input Urch is equal to or larger than the predetermined upper limit value XURCHH, the reaching law input Ur is set.
is set to a predetermined upper limit value XURCHH (step S
267, S269).

When the adaptive sliding mode controller 21 becomes unstable as described above, the control gain F is set to the predetermined stabilization gain XKRCHSTB, and the reaching law input U is set without using the model parameter b1.
By calculating rch, the adaptive model parameter controller 21 can be returned to a stable state. When the identification by the model parameter identifier 22 becomes unstable, the adaptive sliding mode controller 21 becomes unstable.
, The adaptive sliding mode controller 21 can be stabilized.

FIG. 28 is a flowchart of the adaptive law input Uadp calculation process executed in step S205 of FIG. In step S271, the stability determination flag FS
It is determined whether or not 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 equation (50) (the above equation (11a)) is used.
Then, the adaptive law input Uadp is calculated by the following equation (step S273). Uadp = −G × SUMSIGMA / b1 (50)

On the other hand, when the stability judgment flag FSMCSTAB is "1" and the adaptive sliding mode controller 21 becomes unstable, the control gain G is set to the predetermined stabilization gain XKADPSTB (step S27).
4), the following equation (51) without using the model parameter b1
Is used to calculate the adaptive law input Uadp (step S27)
5). Uadp = −G × SUMSIGMA (51)

As described above, when the adaptive sliding mode controller 21 becomes unstable, the control gain G is set to the predetermined stabilization gain XKADPSTB, and the adaptive law input U is used without using the model parameter b1.
By calculating adp, the adaptive model parameter controller 21 can be returned to a stable state.

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

In step S281, the switching function change amount Dσpre is calculated by the following equation (52), and then the stability determination parameter SGMSTAB is calculated by the following equation (53).
Is calculated (step S282). Dσpre = σpre (k) −σpre (k−1) (52) SGSTAB = Dσpre × σpre (k) (53) In step S283, the stability determination parameter SGMS
It is determined whether or not TAB is equal to or smaller than a stability determination threshold value XSGMSTAB. If 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
If GMSTAB≤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).

In step S286, the value of the instability detection counter CNTSMCST is set to the predetermined count value XSSTAB.
It is determined whether or not: CNTSMCST ≦ XSSTA
If 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, it is determined that the controller 21 is unstable, and the first determination flag FSMCSTA
B1 is set to "1" (step S288). In addition,
The count value of the instability detection counter CNTSMCST is initialized to “0” when the ignition switch is turned on.

In the following step S289, the stability determination period counter CNTJUDST is decremented by "1", and then the stability determination period counter CNTJUDST is decremented.
Is determined whether or not the value of is “0” (step S29)
0). The stability determination period counter CNTJUDST has a predetermined determination count value XCJ when the ignition switch is turned on.
Initialized to UDST. Therefore, initially, the answer to step S290 is negative (NO), and
Proceed to 295.

Thereafter, a stability determination period counter CNTJUD
When ST becomes “0”, the process proceeds from step S290 to step S291, and it is determined whether the first determination flag FSMCSTAB1 is “1”. 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 is set. Flag FSMCSTAB
2 is set to "1" (step S292).

In the following step S294, the value of the stability determination period counter CNTJUDST is set to the predetermined determination count value X.
In addition to setting CJUDST, the value of the instability detection counter CNTSMCST is set to “0”, and step S2
Go to 95. In step S295, the stability determination flag F
SMCSTAB is set to a first determination flag FSMCSTAB1.
And the second determination flag FSMCSTAB2. The second determination flag FSMCSTAB2 is determined in step S
The answer to 286 is 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 is also set to the stability determination period counter CNTJU.
Until the value of DST becomes "0", it is maintained at "1".

FIG. 30 is a flowchart of the default opening deviation thdefadp calculation process executed in step S17 of FIG. In step S251, a 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 a gain parameter calculated in step S253 during the previous execution of this process. In step S252, the model parameter C calculated in the model parameter identifier calculation 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 a default opening deviation thdefadp (k). thdefadp (k) = thdefadp (k−1) + KPTH (k) × (c1′−thdefadp (k−1)) (55)

In step S253, a 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 equation (56) is obtained by calculating λ1 ′ in the equation (39). And λ
2 ′ is set to a predetermined value XDEFADPW and “1”, respectively.

By the processing in FIG. 30, the model parameter c
1 ′ is statistically processed by the sequential weighted least squares method,
The default opening deviation thdefadp is calculated. In the present embodiment, the model parameter identifier 22 in FIG. 3 corresponds to an identification unit, and the state predictor 23 in FIG. 3 corresponds to a prediction unit.

[0127]

As described in detail above, according to the first aspect of the present invention, the throttle valve driving device is controlled in accordance with the future throttle valve opening predicted by the predicting means. Even if the device includes a dead time element, the stability margin of control can be expanded, and the control gain can be increased to improve the control response speed.

According to the second aspect of the present invention, the throttle valve driving device is modeled including a dead time element,
Since the throttle valve opening after the lapse of the dead time is predicted based on the control target model obtained by the modeling, the dead time existing in the throttle valve driving device can be compensated with higher accuracy.

According to the third aspect of the present invention, since the throttle valve opening is predicted using the model parameters identified by the identification means, the dynamic characteristics of the throttle valve driving device may change over time. Even when it changes due to environmental conditions or the like, it is possible to calculate an accurate predicted throttle valve opening.

According to the fourth aspect of the present invention, since the throttle valve driving device is controlled by the sliding mode control having high robustness, the actual dead time of the throttle valve driving device and the dead time of the model to be controlled are reduced. Even if a modeling error occurs due to the deviation, a high controllability can be stably maintained.

According to the fifth aspect of the present invention, the sliding mode controller controls the throttle valve driving device using the model parameters identified by the identification means, so that a modeling error can be reduced. Controllability can be improved.

According to the sixth aspect of the invention, since the control input to the throttle valve driving device includes an adaptive law input, good controllability can be realized even if there is a disturbance or a modeling error. .

[Brief description of the drawings]

FIG. 1 is a diagram showing a throttle valve driving device for an internal combustion engine and a control device thereof according to an embodiment of the present invention.

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

FIG. 3 is a functional block diagram showing functions realized by an electronic control unit (ECU) in FIG. 1;

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

FIG. 5 is a diagram showing a setting range of a control gain (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 a default opening deviation of a throttle valve is reflected on a model parameter (c1 ′).

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

FIG. 10 is a flowchart of a process for setting a state variable in the process of FIG. 9;

FIG. 11 is a flowchart of a process for executing a calculation of a model parameter identifier in the process of FIG. 9;

FIG. 12 is a flowchart of a process of executing an operation of an identification error (ide) in the process of FIG. 11;

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

FIG. 14 is a flowchart of dead zone processing in the processing of FIG. 12;

FIG. 15 is a diagram showing a table used in the processing of FIG. 14;

FIG. 16 is a flowchart of a process of stabilizing a model parameter vector (θ) in the process of FIG. 11;

FIG. 17 shows a model parameter (a) in the processing of FIG.
It is a flowchart of the limit processing of 1 ′, a2 ′).

FIG. 18 is a diagram for explaining a change in a value of a model parameter due to the processing in FIG. 16;

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

FIG. 20 shows a model parameter (c) in the processing of FIG.
It is a flowchart of the limit process of 1 ').

FIG. 21 is a flowchart of a process of executing an operation of a state predictor in the process of FIG. 9;

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

FIG. 23 shows a prediction switching function value (σp
It is a flowchart of the process which performs the calculation of re).

FIG. 24 is a flowchart of a process of executing a calculation of a switching function setting parameter (VPOLE) in the process of FIG. 23;

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

FIG. 26 shows a prediction switching function value (σp
It is a flowchart of the process which performs the calculation of the integrated value of re).

FIG. 27 shows a reaching law input (Urc) in the processing of FIG. 22;
It is a flowchart of the process which performs the calculation of h).

FIG. 28 shows an adaptive law input (Uad) in the processing of FIG. 22;
It is a flowchart of the process which performs the calculation of p).

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

30 is a diagram showing a default opening deviation (t
9 is a flowchart of a process for executing the calculation of (hdefadp).

[Explanation of symbols]

 DESCRIPTION OF SYMBOLS 1 Internal combustion engine 3 Throttle valve 7 Electronic control unit 10 Throttle valve drive 21 Adaptive sliding mode controller 22 Model parameter identifier (identification means) 23 State predictor (prediction means) 24 Target opening setting part

 ────────────────────────────────────────────────── ─── Continued on front page (72) Inventor Jun Takahashi 1-4-1 Chuo, Wako-shi, Saitama F-term in Honda R & D Co., Ltd. (Reference) 3G065 CA00 DA05 DA06 DA15 FA06 FA07 FA12 GA41 GA46 HA06 HA21 HA22 JA04 JA09 JA11 KA02 KA13 KA15 KA16 3G301 JA11 LA03 LB01 LC03 NA06 NA08 NB02 NB03 NB15 NB18 NC02 ND02 ND05 ND41 ND45 NE17 NE19 NE25 PA11A PA11Z PF03Z

Claims (6)

[Claims] 1. A control device for controlling a throttle valve driving device including a throttle valve of an internal combustion engine and driving means for driving the throttle valve to control the opening of the throttle valve to a target opening. A control device for a throttle valve driving device, comprising a predicting means for predicting a throttle valve opening, and controlling the throttle valve driving device according to the throttle valve opening predicted by the predicting means. 2. The throttle valve driving device is modeled including a dead time element, and the prediction means predicts a throttle valve opening after a dead time has elapsed based on a control target model obtained by the modeling. The control device for a throttle valve driving device according to claim 1, wherein 3. An identification means for identifying a model parameter of the control target model, wherein the prediction means predicts the throttle valve opening using the model parameter identified by the identification means. A control device for a throttle valve driving device according to claim 2. 4. A sliding mode controller according to claim 1, further comprising a sliding mode controller for controlling said throttle valve driving device by sliding mode control according to a throttle valve opening predicted by said predicting means. 3. The control device for a throttle valve drive device according to claim 1. 5. An identification means for identifying a model parameter of the controlled object model, wherein the sliding mode controller controls the throttle valve driving device using the model parameter identified by the identification means. The control device for a throttle valve driving device according to claim 4. 6. The control device according to claim 4, wherein the control input to the throttle valve driving device by the sliding mode controller includes an adaptive law input.