JPH0656120B2 - Internal combustion engine learning control device - Google Patents

Description

TECHNICAL FIELD The present invention relates to a learning control device for a feedback control system such as an air-fuel ratio (fuel injection amount) of an internal combustion engine, an ignition timing, an idle speed and the like.

<Prior Art> A conventional learning control device for an internal combustion engine is disclosed in Japanese Patent Laid-Open No. 59-
No. 203828, No. 59-21138, No. 60-90944, No. 61-19.
There is one disclosed in Japanese Patent No. 0141.

These are set by proportional / integral control, etc. while comparing the control target value and the actual value with the basic control amount that is set corresponding to the control target value such as the air-fuel ratio based on the physical quantity related to the operating state of the engine. In which the control amount is corrected and the control amount is calculated, and the control amount is controlled to feedback-control the air-fuel ratio to the control target value.
The deviation from the reference value of the feedback correction value during the feedback control is learned for each area of the engine operating state to determine the learning value for each area, and in calculating the control amount, the basic control amount is corrected by the learning value for each area, What is obtained by the control amount calculated without correction by the feedback correction value is made to match the control target value, and during feedback control, this is further corrected by the feedback correction value to calculate the control amount.

According to this, the follow-up delay of the feedback control during the transient operation can be eliminated during the feedback control, and the desired control output can be accurately obtained when the feedback control is stopped.

Therefore, variations in components such as the electronically controlled fuel injection device are absorbed, and secular changes in the charging efficiency of the engine and atmospheric pressure,
It is used to maintain maximum engine performance over a long period of time by compensating for changes in operating environment conditions such as temperature and humidity.

<Problems to be Solved by the Invention> However, such a conventional learning control device is a so-called iterative learning method using a data map, that is, a data map grid section is set according to an engine operating state, and feedback control in each learning area is performed. Since the deviation amount is updated by the repeated learning experience, if each learning area segment is set finely in order to improve the learning correction accuracy, the learning update speed becomes slow. In other words, the learning correction accuracy and the learning speed are conditions that conflict with each other.

In view of such conventional problems, an object of the present invention is to provide a learning control device for an internal combustion engine that can improve learning correction accuracy and significantly improve learning speed.

<Means for Solving Problems> In order to achieve the above object, the present invention configures a learning control device for an internal combustion engine including the following means A to J as shown in FIG.

(A) a physical quantity detection means for detecting a physical quantity related to the operating state of the internal combustion engine (B) a rewritable learning value-by-factor storage means for storing a plurality of learning values for each factor (C) a plurality of learning values for each factor Physical quantity correction means for correcting the physical quantity by dividing into a multiplication term and an addition term for the physical quantity (D) Based on the corrected physical quantity, a basic control quantity corresponding to the control target value of the control target of the internal combustion engine is calculated. Basic control amount calculation means (E) Feedback correction value setting means for comparing the control target value with the actual value and increasing or decreasing the feedback correction value by a predetermined amount in the direction to bring the actual value closer to the control target value (F) Control amount calculating means for calculating the control amount by correcting the basic control amount with the feedback correction value (G) Control means for controlling the control target of the internal combustion engine which operates according to the control amount (H) of the feedback correction value Is it a reference value? Deviation detecting means for detecting the deviation (1) The factor of the deviation is analyzed based on at least one of the information on the engine operating state at the time of deviation detection and the information on the deviation, and the deviation is factored at a rate determined by the information. Factor analysis means for separating into a plurality of other parameters (J) Factor-based learning value updating means for modifying and rewriting a plurality of factor-based learning values in the storage means based on each of the plurality of parameters <Action> Physical quantity detection means A detects a physical quantity related to the operating state of the internal combustion engine, and the physical quantity correction means C uses a plurality of factor-based learning values stored in the factor-based learning value storage means B as a multiplication term and an addition term for the physical quantity. Separately, the physical quantity is corrected by these.

The basic control amount calculation means D calculates a basic control amount corresponding to a control target value of a control target (for example, air-fuel ratio, ignition timing, idle speed, etc.) of the internal combustion engine based on the corrected physical amount.

The feedback correction value setting means E compares the control target value with the actual value and sets the feedback correction value in a direction of bringing the actual value closer to the control target value, for example, by increasing or decreasing a predetermined amount based on proportional / integral control. To do.

Then, the control amount calculation means F calculates the control amount by correcting the basic control amount with the feedback correction value. Then, the control means G operates according to the control amount to control the control target of the internal combustion engine.

On the other hand, the deviation detecting means H detects the deviation of the feedback correction value from the reference value. Then, the factor analysis means I
Is a predetermined factor based on at least one of information about the engine operating state at the time of deviation detection and information about deviation (information about deviation amount, deviation direction, deviation speed, deviation change direction, etc.). The analysis is performed speculatively according to the analysis rule, and the deviation is separated into a plurality of parameters for each factor at a rate determined by the information. Then, the factor-based learning value updating means J corrects and rewrites the plurality of factor-based learning values in the storage means B based on each of the separated plurality of parameters.

In this way, the deviation (error amount) of the feedback control is detected, this is inferred using various information and the database, factor analysis is performed, and it is separated into a plurality of parameters, and the optimum arithmetic expression for each factor is used. That is, the learning correction accuracy and the learning speed are compatible with each other by accurately correcting by dividing into a multiplication term and an addition term for the physical quantity.

<Example> An example in which the learning control device according to the present invention is applied to a feedback control system of an air-fuel ratio of an internal combustion engine having an electronically controlled fuel injection device will be described below.

In FIG. 2, air is sucked into the engine 1 from an air cleaner 2 through an intake duct 3, a throttle valve 4 and an intake manifold 5. At the branch portion of the intake manifold 5, a fuel injection valve 6 as a control means is provided for each cylinder. The fuel injection valve 6 is an electromagnetic fuel injection valve that is energized by a solenoid to open and stop energized to be closed. The fuel injection valve 6 is energized and opened by a drive pulse signal from a control unit 12 described later, and a fuel not shown is shown. Fuel that is pumped and adjusted to a predetermined pressure by a pressure regulator is injected and supplied. Although this example is a multi-point injection system, it may be a single-point injection system in which a single fuel injection valve is provided in common to all cylinders upstream of the throttle valve.

A spark plug 7 is provided in the combustion chamber of the engine 1 to ignite sparks to ignite and burn the air-fuel mixture.

Exhaust gas is discharged from the engine 1 through the exhaust manifold 8, the exhaust duct 9, the three-way catalyst 10, and the muffler 11. The three-way catalyst 10 is an exhaust purification device that oxidizes CO and HC in the exhaust components and reduces NO _x to convert them into other harmless substances, and burns the air-fuel mixture at the stoichiometric air-fuel ratio. Sometimes both conversion efficiencies are the best.

The control unit 12 includes a CPU, ROM, RAM,
The microcomputer is provided with an A / D converter and an input / output interface, receives input signals from various sensors, performs arithmetic processing as described later, and controls the operation of the fuel injection valve 6.

As the various sensors, a hot wire type or flap type air flow meter 13 is provided in the intake duct 3 and outputs a signal U according to the intake air flow rate Q.

Further, the crank angle sensor 14 is provided, and in the case of four cylinders, it outputs a reference signal for each crank angle of 180 ° and a unit signal for each crank angle of 1 ° or 2 °. Here, the engine speed N can be calculated by measuring the cycle of the reference signal or the number of generated unit signals within a predetermined time.

A water temperature sensor 15 for detecting the cooling water temperature Tw of the water jacket of the engine 1 is also provided.

Further, an O ₂ sensor 16 is provided at the collecting portion of the exhaust manifold 8 to detect the air-fuel ratio of the air-fuel mixture sucked into the engine 1 via the O ₂ concentration in the exhaust. If the O ₂ sensor 16 with the NO _x reduction catalyst layer proposed in Japanese Patent Application No. 62-65844 is used, more accurate detection becomes possible.

Here, the CPU of the microcomputer incorporated in the control unit 12 is a program (a fuel injection amount calculation routine, an air-fuel ratio feedback control routine, a program on a ROM shown as a flowchart in FIGS. 3 to 5).
The fuel injection is controlled by performing arithmetic processing according to the optimum learning routine).

The functions as physical quantity detection means, physical quantity correction means, basic control amount calculation means, feedback correction value setting means, control amount calculation means, deviation detection means, factor analysis means, and factor-based learning value update means are achieved by the above program. To be done. A RAM is used as the factor-based learning value storage means, and the stored contents are retained by the backup power source even after the engine key switch is turned off.

Next, with reference to the flow charts of FIGS. 3 to 5, the state of the arithmetic processing of the microcomputer in the control unit 12 will be described.

FIG. 3 is a fuel injection amount calculation routine, which is executed every predetermined time.

In step 1 (denoted as S1 in the figure. The same applies hereinafter), an air flow meter is used as a physical quantity related to the engine operating state.
The intake air flow rate Q detected based on the signal from 13 is input. This portion corresponds to the physical quantity detecting means. At the same time, the engine speed N calculated based on the signal from the crank angle sensor 14 and the water temperature Tw detected based on the signal from the water temperature sensor 15 are input.

In step 2, the factor-based learning value X ₁ , stored at a predetermined address of the RAM as the factor-based learning value storage means
Reads _X 2, _{X 3} of X _{2, X 3,} these by correcting the intake air flow rate Q. The correction formula here is Q · X
₂ + X ₃ , and one of the factor-based learning values X ₂ is a multiplication term for the intake air flow rate Q and the other factor-based learning value X 2.
No. ₃ is an addition term for the intake air flow rate Q. Then, from the intake air flow rate (Q · X ₂ + X ₃ ) and the engine speed N, the basic fuel injection amount Tp corresponding to the intake air amount per unit rotation is calculated by the following equation. The step 2 corresponds to the physical quantity correction means and the basic control quantity calculation means.

T _p = K · (Q · X ₂ + X ₃ ) / N [K is a constant] In step 3, the water temperature correction coefficient K _TW corresponding to the water temperature Tw,
Various correction factors including the air-fuel ratio correction factor K _MR according to the engine speed N and the basic fuel injection amount Tp COEF = 1 + K
Set _TW + K _MR + ...

In step 4, the latest air-fuel ratio feedback correction coefficient α (reference value 1) set by the air-fuel ratio feedback control routine of FIG. 4 described later is read.

In step 5, the voltage correction amount Ts is calculated based on the battery voltage.
To set. This is to correct the change in the injection flow rate of the fuel injection valve 6 due to the change in the battery voltage.

In step 8, the fuel injection amount Ti is calculated according to the following equation. Here, X ₁ is RA as a learning value storage means for each factor.
It is one of the factor-based learning values stored in a predetermined address of M, and when learning is not started, X ₁ = 0 is set as an initial value. The part of step 6 corresponds to the control amount calculation means.

Ti = Tp · COEF · α + (Ts + X ₁ ) In step 7, the calculated Ti is set in the output register. As a result, a predetermined engine rotation synchronization (for example, 1
At each fuel injection timing (every rotation), the drive pulse signal having the pulse width of Ti which is set latest is given to the fuel injection valve 6 to perform fuel injection.

FIG. 4 is an air-fuel ratio feedback control routine, which is executed in rotation synchronization or time synchronization, whereby the air-fuel ratio feedback correction coefficient α is set. Therefore, this routine corresponds to the feedback correction value setting means.

In step 11, it is determined whether or not a predetermined air-fuel ratio feedback control condition is satisfied. Here, the predetermined air-fuel ratio feedback control condition is a condition that the engine speed N is a predetermined value or less and the basic fuel injection amount Tp representing the load is a predetermined value or less. If this condition is not satisfied, this routine ends. In this case, the air-fuel ratio feedback correction coefficient α is clamped to the previous value (or the reference value 1), and the air-fuel ratio feedback control is stopped. This is because the air-fuel ratio feedback control is stopped in the high rotation or high load region, a rich output air-fuel ratio is obtained by the air-fuel ratio correction coefficient K _MR , and an increase in exhaust temperature is suppressed.
This is to prevent seizure of the engine 1 and burnout of the three-way catalyst 10.

If the air-fuel ratio feedback control condition is satisfied, step 12
Proceed to the following.

At step 12, the output voltage V _O2 of the O ₂ sensor 16 is read, and at next step 13, it is compared with the slice level voltage V _ref corresponding to the stoichiometric air-fuel ratio to determine the rich lean of the air-fuel ratio.

When the air-fuel ratio is lean (V ₀₂ <V _ref ), the _routine proceeds from step 13 to step 14, and it is judged whether or not it is during the reversal from rich to lean (immediately after the reversal), and at the time of reversal, the routine proceeds to step 15. For the optimum learning routine shown in FIG. 5, which will be described later, the deviation of the previous air-fuel ratio feedback correction coefficient α from the reference value 1 is stored as a = α−1, and then the routine proceeds to step 16, where the air-fuel ratio feedback correction coefficient α is set to the previous time. The value is increased by a predetermined proportional constant P. Step except when flipping
Proceeding to 17, the air-fuel ratio feedback correction coefficient α is increased by a predetermined integration constant I with respect to the previous value, and thus the air-fuel ratio feedback correction coefficient α is increased at a constant slope.
Note that P >> I.

When the air-fuel ratio is rich (V ₀₂ > V _ref , step
From 13 to step 18, it is determined whether or not the lean-to-rich reversal is being performed (immediately after the reversal), and at the time of reversal, the process proceeds to step 19 and the previous air-fuel ratio feedback for the optimum learning routine of FIG. After the deviation of the correction coefficient α from the reference value I is stored as b = α−1, the routine proceeds to step 20, where the air-fuel ratio feedback correction coefficient α is decreased by a predetermined proportional constant P with respect to the previous value. Step except when flipping
The routine proceeds to step 21, where the air-fuel ratio feedback correction coefficient α is decreased by a predetermined integration constant I with respect to the previous value, and thus the air-fuel ratio feedback correction coefficient α is decreased with a constant inclination.

FIG. 5 shows an optimal learning routine, which is executed at predetermined time intervals.
As a result, the factor-based learning values X ₁ , X ₂ , and X ₃ are set and updated.

In step 31, it is determined whether or not a predetermined learning condition is satisfied. Here, the predetermined learning condition is that the air-fuel ratio feedback control is being performed, and the rich / lean signal of the O ₂ sensor 16 is inverted at an appropriate cycle. If this condition is not satisfied, this routine ends.

When the predetermined learning condition is satisfied, the routine proceeds to step 32, where it is judged whether or not the output voltage V ₀₂ of the O ₂ sensor 16 is reversed, and when not being reversed, the routine proceeds to step 33 and the engine operating state at that time is judged. Engine speed N and basic fuel injection amount T as data
Sample p and.

When the output voltage V ₀₂ of the O ₂ sensor 16 is reversed, the process proceeds to step 34 to obtain the average value of the above-mentioned a and b for optimum learning. At this time, a and b are peak values above and below the deviation from the reference value I of the air-fuel ratio feedback correction coefficient α from the reverse of the increasing / decreasing direction of the air-fuel ratio feedback correction coefficient α to the inversion as shown in FIG. 6. The average deviation Δα from the reference value 1 of the air-fuel ratio feedback correction coefficient α is detected by obtaining the average value of these.

Therefore, steps 15 and 19 in FIG. 4 and step 34 in FIG.
The part of corresponds to the deviation detecting means.

Next, in step 35, the output voltage V ₀₂ of the O ₂ sensor 16
The engine speed N and the movement (N ₁ , N ₂ ..., Tp ₁ , Tp ₂ ...) Of the basic fuel injection amount Tp during the reversal are read to specify the engine operating state (N, Tp).

Next, in step 36, the learning weighting parameters K ₁ , K ₂ and K ₃ assigned to each area are searched from the area of the engine operating state (N, Tp) with reference to the factor analysis map. However, K ₁ + K ₂ + K ₃ is 1 or less.

Here, the factors leading to the deviation Δα are mainly caused by the fuel injection valve 6 (hereinafter referred to as F / I factor) and those caused by the air flow meter 13 including the air density change (hereinafter referred to as AF / (M factor)) is divided into an inclination part and an offset part, and the respective occupying ratios are K ₁ , K ₂ ,
It is represented by K ₃ .

Then, based on empirical rules, it is estimated that the F / I factor is large in the low rotation / low load region and the AF / M factor is large in the high rotation / high load, and the values of K ₁ , K ₂ , and K ₃ are assigned to each area. The factor analysis is performed based on the engine operating state by referring to the factor analysis map.

As a result, the deviation Δα is set to the parameter K ₁ · F of the F / I factor.
Δα and the parameter K _{2 for the} slope of AF / M factors
It becomes possible to separate Δα from the AF / M factor of the offset parameter K ₃ · Δα, and in the next step 37, Δα ₁ = K ₁ · Δα, Δα ₂ = K ₂ · Δα, Δ
Separate into each parameter with α ₃ = K ₃ · Δα.

Therefore, steps 35 to 37 correspond to factor analysis means.

Incidentally, the factor analysis is performed based on the information regarding the engine operating state as described above, and also based on the information regarding the deviation such as the deviation amount, the deviation direction, the deviation speed, and the deviation change direction, based on those databases. May be.

Next, the routine proceeds to step 38, where the factor-based learning values X ₁ , X ₂ , X ₃ stored in a predetermined address on the RAM are read out, and the deviation Δα _{1 is} added to the F / I factor learning value X _{1 as shown} in the following equation. Is updated by adding M _{1 to} the learning value X of the inclination of the AF / M factors.
_The deviation Δα ₂ is added to M by ₂ and updated, and the deviation Δα ₃ is added by M ₃ to the learning value X ₃ of the AF / M factor for the offset and updated. M ₁ , M ₂ , and M ₃ are learning weighting coefficients.

X ₁ = X ₁ + M ₁ · Δα ₁ X ₂ = X ₂ + M ₂ · Δα ₂ X ₃ = X ₃ + M ₃ · Δα ₃ Next, at step 39, the learning value X for each factor is assigned to a predetermined address on the RAM. _The data is rewritten by writing ₁ , X ₂ , X ₃ . This RAM is a backup memory,
The stored contents are stored and retained even after the engine key switch is turned off.

Therefore, the steps 38 and 39 correspond to the factor-based learning value updating means.

In this way, the learning values X ₁ , X ₂ , X ₃ for each factor are determined, and the correction based on these values is optimized for each factor as shown in step 2 and step 6 in FIG. It is performed by an arithmetic expression.

That is, of the learning values X ₂ and X ₃ of the AF / I factor, the slope X ₂ is added as a multiplication term to the intake air flow rate Q, and the offset X ₃ is added to the intake air amount Q. An arithmetic expression is set as a term,
Optimal correction is performed by these.

Further, regarding the learning value X ₁ of the F / I factor, the fuel injection valve 6
Since it is caused by itself, an arithmetic expression is set as an addition term for the basic fuel injection amount Tp as with the voltage correction amount Ts, and the optimum correction is also performed by this.

Fig. 7 shows that the effect of this learning control is +16 marked with □.
% Engine with rich tendency in 4 times of learning ● Appearance approaching to engine with median variation of mark ●, and -16% lean engine with △ in 3 times of learning ●
It shows how the engine approaches the engine with the median variation of the mark, and clearly shows the improvement of the learning speed by this learning control.

In this embodiment, as the electronically controlled fuel injection device, the so-called L-Jetro system having an air flow meter to detect the intake air flow rate is shown, but the intake manifold negative pressure is detected using a pressure sensor. So-called D-Je
It can be applied to various systems such as a tro system or a so-called α-N system based on the throttle valve opening (α) and the engine speed (N).

In the case of the D-Jetro method, the learning value X of the AF / M factor
_The value based on the pressure sensor output may be corrected by ₂ and X ₃ .

In the case of the α-N method, the learning values X ₂ and X ₃ of the AF / M factor
Therefore, the value of the opening area with respect to the throttle valve opening may be corrected.

Further, not only the feedback control of the air-fuel ratio, but also the ignition time control by knocking detection and the feedback control of the idle speed via the auxiliary air valve can be applied.

<Effects of the Invention> As described above, according to the present invention, the factor that causes the deviation is analyzed based on various information at that time, and learning is performed for each factor, instead of the conventional method for learning by area. Since the method is used and the learning value for each factor is divided into a multiplication term and an addition term for the physical quantity such as the intake air flow rate, which is the basis of the basic control amount calculation, and is corrected by the optimal calculation formula according to each factor. , The learning speed can be greatly improved while improving the learning correction accuracy. Further, by such learning control, it is possible to reduce the matching man-hours, simplify component management, and maintain free. Also, the capacity of the backup memory can be reduced.

[Brief description of drawings]

FIG. 1 is a functional block diagram showing a configuration of the present invention, FIG. 2 is a system diagram showing an embodiment of the present invention, FIGS. 3 to 5 are flow charts showing control contents, and FIG. 6 is an air-fuel ratio feedback. FIG. 7 is a diagram showing how the correction coefficient changes, and FIG. 7 is a diagram showing the effect of learning control. 1 ...... engine, 6 ...... fuel injection valve, 12 ...... control unit, 13 ...... air flow meter, 14 ...... crank angle sensor, 16 ...... O ₂ sensor

Claims (1)

[Claims] 1. A physical quantity detection means for detecting a physical quantity related to an operating state of an internal combustion engine, a rewritable learning value for each factor for storing a plurality of learning values for each factor, and a plurality of learning values for each factor. Physical quantity correcting means for correcting the physical quantity by dividing into a multiplication term and an addition term for the physical quantity, and a basic for calculating a basic control quantity corresponding to a control target value of the control target of the internal combustion engine based on the corrected physical quantity Control amount calculation means, feedback correction value setting means for comparing the control target value with the actual value and increasing or decreasing the feedback correction value by a predetermined amount in the direction to bring the actual value closer to the control target value, and the basic control amount A control amount calculating means for calculating a control amount by correcting the control amount with the feedback correction value; a control means for controlling a control target of the internal combustion engine which operates according to the control amount; Deviation detecting means for detecting a deviation of the feedback correction value from the reference value, and a factor of the deviation is analyzed on the basis of at least one of information on the engine operating state at the time of deviation detection and information on the deviation, and the deviation is concerned. Factor analysis means for separating into a plurality of parameters by factor at a rate determined by, and factor-based learning value updating means for modifying and rewriting a plurality of factor-based learning values in the storage means based on each of the plurality of parameters, A learning control device for an internal combustion engine, comprising: