JPH0656122B2 - 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 feedback correction values set by proportional / integral control, etc. while comparing the control target value and the actual value with the basic control amount set corresponding to the control target value such as the air-fuel ratio based on the operating state of the engine. In a system in which the control amount is calculated by performing a feedback control of the air-fuel ratio and the like by performing feedback control of the air-fuel ratio to the control target value, the deviation of the feedback correction value during feedback control from the reference value of the engine operating state Learning values for each area are used to determine the learning value for each area, and when calculating the control amount, the basic control amount is corrected by the learning value for each area, and the control value obtained by the control amount calculated without correction by the feedback correction value is controlled. The control amount is made to match the target value, and this is further corrected by the feedback correction value during the feedback control 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 K as shown in FIG.

(A) Basic control amount setting means for setting a basic control amount corresponding to the control target value of the control target of the internal combustion engine (B) Comparing the control target value and the actual value, in the direction of approaching the actual value to the control target value Feedback correction value setting means for increasing or decreasing the feedback correction value by a predetermined amount (C) Rewritable learning value storage means for storing a plurality of learning values for each factor (D) The basic control amount for the feedback correction value And a control amount calculating means for calculating a control amount by correcting the control amount by an arithmetic expression respectively set based on the plurality of factor-based learning values (E) The control target of the internal combustion engine which operates according to the control amount. Control means for controlling (F) Deviation detecting means for detecting a deviation of the feedback correction value from a reference value (G) The factor of the deviation is at least one of information on the engine operating state at the time of deviation detection and information on the deviation. Basis Factor analysis means for analyzing and separating the deviation into a plurality of parameters for each factor at a rate determined by the information (H) Control amount calculated by the control amount calculation means at the time of deviation detection by the deviation detection means and the factor analysis means Factor analysis result correctness determination based on a difference from the control amount obtained by correcting the basic control amount based on a plurality of factor-based learning values based on a plurality of parameters separated by Means (I) Factor analysis correction means for increasing / decreasing a plurality of parameters separated by the factor analysis means in a direction to reduce the difference based on the determination result by the factor analysis result correctness determination means (J) By the factor analysis correction means Factor-specific learning value updating means for modifying and rewriting the factor-specific learning value of the factor-specific learning value storage means based on each of the plurality of parameters of the modification result (K) Analysis rule changing means for changing the analysis rule by the factor analyzing means based on the increase / decrease correction result of a plurality of parameters by the analysis correcting means <Operation> The basic control amount setting means A corresponds to the control target value of the control target of the internal combustion engine The feedback correction value setting means B compares the control target value with the actual value, and the feedback correction value is set in a direction to bring the actual value closer to the control target value based on, for example, proportional / integral control. Increase or decrease the amount of to set. Then, the control amount calculation means D corrects the basic control amount with the feedback correction value, and further, based on the plurality of factor-based learning values stored in the factor-based learning value storage means C, the optimum values respectively set in accordance therewith. The control amount is calculated by correcting with a simple calculation formula. Then, the control means E operates according to the control amount to control the control target of the internal combustion engine.

On the other hand, the deviation detection type F detects the deviation of the feedback correction value from the reference value. And the factor analysis means G
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.). An inferential analysis is performed according to the analysis rule, and the deviation is separated into a plurality of parameters for each factor based on the analysis result.

The factor analysis result correctness determination means H is a control amount set by feedback correction when a deviation is detected, and a control amount obtained by correcting the basic control amount based on the factor-based learning value of the factor analysis result. Whether the factor analysis result is correct or not is determined based on the difference between. Then, the factor analysis correction means I corrects the plurality of parameters in the direction of decreasing the difference based on the correctness of the factor analysis result, and the factor-based learning value updating means J uses the correction result to cause the factor-based learning value updating means J to correct. The learning value for each factor in the storage means C is corrected and rewritten.

The analysis rule changing means K changes the analysis rule by the factor analysis means G based on how the factor analysis correction means I increases / decreases a plurality of parameters.

That is, when the factor analysis unit G separates the deviation of the feedback correction value from the reference value into a plurality of parameters for each factor, the control amount is calculated by the factor-based learning value based on the analysis result, and the control based on the feedback correction is performed. Whether the learning result by factor analysis agrees with the correction in the direction to bring the actual value closer to the target value by feedback correction by comparing with the amount (control by using only the learning value by factor without feedback correction value) Whether or not the controlled object is controlled by the target) is determined, and when the factor analysis is inaccurate and does not match the correction by the feedback correction, the factor analysis correction means I reduces a plurality of parameters for each factor so as to reduce the difference. After correction, this correction result is stored in the factor-based learning value storage means C.

Furthermore, when correction by the factor analysis correction means I is required, the rule of the factor analysis by the factor analysis means G is in an inaccurate state, and therefore the analysis rule changing means K changes the analysis rule according to the correction direction. No need for correction by the factor analysis correction means I, and the factor analysis corresponding to the institution is performed.

In this way, the deviation (error amount) of the feedback control is detected, and this is inferred by using various information and the database for factor analysis, and this factor analysis serves as a correction to bring the actual value closer to the control target. The learning correction accuracy and the learning speed are compatible by making corrections and changing the analysis rules according to the correction results to achieve the optimum analysis and correcting accurately with the arithmetic expression suitable for each factor. Let them do it.

<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 (MPI system), a single-point injection system (SPI) in which a single fuel injection valve is provided in common to all cylinders upstream of the throttle valve and the like. .method)
May be

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 voltage signal 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 built 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 basic control amount setting means, the feedback correction value setting means, the control amount calculating means, the deviation detecting means, the factor analyzing means, the factor-based learning value updating means, the factor analysis result correctness determining means, the factor analysis correcting means, and the analysis rule changing means. Function as
It is achieved by the program. 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 drawing; the same applies hereinafter), the intake air flow rate Q detected based on the signal from the air flow meter 13 and the engine speed N calculated based on the signal from the crank angle sensor 14 , The water temperature Tw or the like detected based on the signal from the water temperature sensor 15 is input.

In step 2, the basic fuel injection amount Tp corresponding to the intake air amount per unit rotation is calculated from the intake air flow rate Q and the engine speed N.
= K · Q / N (K is a constant) is calculated. This step 2
The part of corresponds to the basic control amount setting means.

In step 3, various correction coefficients including the water temperature correction coefficient K _TW according to the water temperature Tw, the air-fuel ratio correction coefficient K _MR according to the engine speed N and the basic fuel injection amount Tp, etc. COEF = 1 + K _TW + K
Set _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.

Step 6 In the factor learning value factor learning value from the predetermined address of the RAM as storage means X1, X ₂ reads the. still,
When learning is not started, the initial value is X ₁
= 0 and X ₂ = 1 are stored.

In step 7, the fuel injection amount Ti is calculated according to the following equation. The part of this step 7 corresponds to the control amount calculation means.

Ti = X ₂ · Tp · COEF · α + (Ts + X ₁ ) In step 8, the calculated Ti is set in the output register. As a result, a predetermined engine rotation synchronization (for example, 1
At every fuel injection timing (every rotation), a 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 speed 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 to prevent seizure or three This is to prevent the original catalyst 10 from burning.

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

In step 12, the output voltage V ₀₂ of the O ₂ sensor 16 is read,
In the next step 13, it is determined whether the actual air-fuel ratio is richer than the theoretical air-fuel ratio or lean by comparing with the slice level voltage _Vref corresponding to the theoretical air-fuel ratio. That is, in the present embodiment, the control target is the air-fuel ratio of the engine intake air-fuel mixture, and the control target is the theoretical air-fuel ratio.

When the air-fuel ratio is lean (V ₀₂ <V _ref ), step 13
To step 14 to determine whether it is during the reversal from rich to lean (immediately after reversal), and when reversing, proceed to step 15 to perform the previous air-fuel ratio feedback correction for the optimum learning routine of FIG. 5 described later. After the deviation of the coefficient α from the reference value 1 is stored as a = α−1, the routine proceeds to step 16, where the air-fuel ratio feedback correction coefficient α is increased by a predetermined proportional coefficient P with respect to the previous value. Step 17 except when reversing
Then, 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. still,
P >> I.

When the air-fuel ratio is rich (V ₀₂ <V _ref ), step 13
To step 18 to determine whether or not the lean-to-rich reversal is being performed (immediately after the reversal), and when reversing, the process proceeds to step 19 to perform the previous air-fuel ratio feedback correction for the optimum learning routine shown in FIG. After the deviation of the coefficient α from the reference value 1 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 coefficient P with respect to the previous value. Step 21 except when reversing
Then, the air-fuel ratio feedback correction coefficient α is decreased from the previous value by a predetermined integration constant I, and thus the air-fuel ratio feedback correction coefficient α is decreased at a constant slope.

FIG. 5 shows an optimal learning routine, which is executed at predetermined time intervals.
Accordingly factors learning value X1, X ₂ is set and updated.

In step 31, it is determined whether or not a predetermined learning condition is satisfied. The predetermined learning condition is in the feedback control of the air-fuel ratio, and rich in O ₂ sensor 16
The condition is that the lean signal is inverted at an appropriate period. 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 it is not 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 of the O ₂ sensor 16 is reversed, for optimal learning,
Proceeding to step 34, the average value of the above-mentioned a and b is obtained.
At this time, a and b are peak values above and below the deviation from the reference value 1 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. 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 O engine speed N and the basic fuel injection amount Tp of movement between _{the second} output voltage V ₀₂ of the sensor 16 is inverted _{(N1, N 2 ..., Tp1} , Tp 2 ...) proceeds to step 35 reads the engine The operating state (N, Tp) is specified.

Next Search engine operating condition (N, Tp) with reference to the learning weights were assigned to each area of the map than the area of the parameter K1, K ₂ proceeds to step 36. However, K ₁ +
The initial value of K ₂ 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 Q factor). ) And divide each by K
It is represented by 1, K ₂ .

The rule of thumb large F / I factor in the low-speed low-load range, at high speed and high load estimate including a Q factor is large, leave assigned the value of K1, K ₂ in each area, this map By referring to this, factor analysis is performed based on the engine operating state.

As a result, the deviation Δα is set to the parameter K ₁ of the F / I factor.
It becomes possible to separate into Δα and Q factor parameter K ₂ · Δα, and in the next step 37, Δα = K ₁ · Δ
The parameters are separated into α and Δα = K ₂ · 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.

Then read the cause learning value X1, X ₂ which is stored in a predetermined address in the RAM proceeds to step 38, as follows,
The deviation [Delta] [alpha] ₁ to the learning value X ₁ of one F / I factor updated by adding M ₁ minute, the deviation [Delta] [alpha] ₂ to the learning value X ₂ of the other Q factors
Is added by M ₂ and updated. M1, _{M 2} is a learning weighting factors.

X ₁ = X ₁ + M ₁ · Δα ₁ X ₂ = X ₂ + M ₂ · Δα ₂ Next, in step 39, the fuel injection amount Ti is calculated using the factor-based learning values X1 and X ₂ updated in step 38. Calculate However, the calculation formula of the fuel injection amount Ti at this time, not included air-fuel ratio feedback correction coefficient α as shown below, the feedback correction coefficient and without α this updated factor learning value X1, X ₂ The fuel injection amount Ti is calculated using this.

TI = X ₂ · Tp · COEF + (Ts + X ₁ ) At the next step 40, the fuel injection amount Ti calculated by the fuel injection amount calculation routine of FIG. 3 when the deviation Δα is detected.
Is read and this value is set as MTi. The fuel injection amount Ti when the deviation Δα is detected is, for example, an average value of the fuel injection amounts Ti when the upper and lower peak values of the air-fuel ratio feedback correction coefficient α are taken.

Then the fuel injection quantity Ti calculated in step 39 proceeds to step 41 without the air-fuel ratio feedback correction coefficient alpha, the air-fuel ratio feedback the basis of the update factors is read learning value X1, X ₂ at step 40 The fuel injection amount MTi set using the correction coefficient α is compared to determine whether or not the factor analysis is correct. Therefore, the steps 40 and 40 correspond to the factor analysis result correctness determination means.

Here, when it is determined that the Ti ≒ MTi may be used this factor analysis and factor learning value X1 which is updated, X _2, without using the air-fuel ratio feedback correction coefficient alpha,
It is determined that the fuel corresponding to the calculated fuel injection amount Ti is injected and supplied to the engine 1 to obtain the air-fuel mixture corresponding to the substantially stoichiometric air-fuel ratio.

Because the air-fuel ratio feedback correction coefficient α is set so as to approximate the actual air-fuel ratio to the theoretical air-fuel ratio which is the target air-fuel ratio, the fuel injection amount MTi read in step 40 is substantially the theoretical air-fuel ratio. said to be the fuel injection quantity of fuel ratio corresponding, whereas the fuel injection quantity Ti computed without using the air-fuel ratio feedback correction coefficient using the factor analysis results factors obtained from learning value X1, X ₂ of this α If the fuel injection amount MTi equivalent to the stoichiometric air-fuel ratio is substantially equal, the target theoretical air-fuel ratio can be obtained almost without the air-fuel ratio feedback correction coefficient α according to the factor analysis result, and the factor analysis is correctly performed for learning. It turns out to be accurate.

On the other hand, if it is determined that the Ti«MTi or TI»MTi in step 41, using the air-fuel ratio feedback correction factor learning value X1 obtained by coefficients at no time factor analysis α step 38, X ₂ It is found that the target stoichiometric air-fuel ratio cannot be obtained if the fuel injection amount Ti is calculated. That is, when the fuel injection amount Ti calculated in step 39 is smaller than the fuel injection amount MTi obtained by air-fuel ratio feedback correction, the fuel injection amount Ti is set with the air-fuel ratio feedback correction coefficient α when setting the actual fuel injection amount Ti. It is necessary to correct the fuel injection amount MT while increasing the fuel injection amount MT.
When the fuel injection amount Ti calculated in step 39 is larger than i, the fuel injection amount T is calculated with the air-fuel ratio feedback correction coefficient α.
i must be reduced and corrected.

Therefore, in the state in which the fuel injection amount Ti corresponding to the stoichiometric air-fuel ratio is corrected by the air-fuel ratio feedback correction coefficient α in this manner, it can be said that the factor analysis result is defective. In this case, the process proceeds to step 42 or step 43. , increased or decreased corrected by the air-fuel ratio feedback correction coefficient fuel injection amount corresponding stoichiometric air-fuel ratio without alpha Ti factors learning value so as to obtain X1, X ₂ as described below.

If it is determined that the Ti«MTi in step 41, when calculating the fuel injection amount Ti without using the air-fuel ratio feedback correction coefficient α using only factor learning value X1, X ₂ obtained in step 38 since in a state of leaning the air-fuel ratio the amount of fuel is insufficient for the respective small value ΔX on factors learning value X1, X ₂ obtained in step 38 proceeds to step 42
1, by adding the [Delta] X ₂ as a new factor learning value _{X1, X 2 (X 1 ←} X 1 + ΔX 1, X 2 ← X 2 + ΔX 2), the fuel injection amount Ti factors learning value X1, _{X 2} After that, the amount is further corrected by, and the process returns to step 39 again. That is, Ti ≒
Factor-based learning value X1, at step 42 until MTi is reached
Repeat the correction of X ₂ .

On the other hand, if it is determined that the Ti»MTi in step 41, when calculating the fuel injection amount Ti without using the air-fuel ratio feedback correction coefficient α using only factor learning value X1, X ₂ at step 38 Is in a state where the fuel amount is excessive and the air-fuel ratio becomes rich, so proceed to step 43 and proceed to step
Each minute value ΔX from factor learning value X1, X ₂ obtained in 38
1, [Delta] X ₂ were as a new factor learning value X1, _{X 2} subtraction _{(X 1 ← X 1 -ΔX1,} X 2 ← X 2 -ΔX2), the fuel injection quantity Ti factors learning value X1, _{X 2} returning again to step 39 so as to be more decrease correction by repeated correction factor learning value X1, X ₂ at step 43 until the Ti ≒ MTi.

Therefore, the steps 42 and 43 correspond to the factor analysis correction means.

Here, if it is modified Ti ≒ MTi determined to be such a factor learning value X1, X ₂ in step 41 by the modification in step 42 or step 43, or, factor analysis is performed satisfactorily, step another factor resulting in 38 learning value X1, when it is determined that Ti ≒ MTi in step 41 with X _2, set in the step proceeds to 44 before correction factor learning value X1, X _{2 (step} 38 The read values) are read and set as ₁ and ₂ , respectively.

Then, in the next step 45, the factor-based learning value X1 of the final result is compared with ₁ described above. Here, the factor-based learning value X1 of the final result is the setting result in step 38 when it is determined that Ti≈MTi is the first time in step 41, and the step when it is determined that Ti ≠ MTi
42 or the final correction result in step 43.

When it is determined in step 45 that X ₁ ≈ ₁ ,
Either the correction is slight or the correction in step 42 or step 43 could not be made.
Although Forward jumps to step 49 corrects the learning weighting parameters K1, X ₂ proceeds to step 46 or step 47 it indicates that it is increased or decreased modified more than a predetermined when it is determined not equal X ₁ ≒ ₁ .

That is, when it is determined in step 45 that X ₁ > ₁ , the target theoretical air-fuel ratio cannot be obtained with the factor-based learning value X ₁ obtained by factor analysis using the learning weighting parameter K _1. it indicates that the increased correct the cause learning value X ₁ in 42, by adding a predetermined small amount [Delta] K ₁ sets the new learning weighting parameters K ₁ to the current learning weighting parameters K ₁ proceeds to step 46, in the next step 48 rewrites the learning weighting parameters _K 1, _{K 2} maps _{K 1.} As a result, in the next step, the ratio of separation into the F / I factor parameter K ₁ · Δα is increased, and the factor-based learning value X ₁ is increased, so that step 42
The increase correction of the factor-based learning value X _{1 in (1} ) is unnecessary or the increase width is suppressed.

On the other hand, when it is determined that X _{1 <1} In step 45, it indicates that it has reduced modify the factor learning value X _1, the predetermined minute amount from the current learning weighting parameters K ₁ proceeds to step 47 [Delta] K ₁ Is set to set a new learning weighting parameter K ₁ , and then in step 48, K ₁ is rewritten. As a result, in the next time, the ratio of separation into the F / I factor parameter K ₁ · Δα is reduced, and the factor-based learning value X ₁ is reduced, so that reduction correction of the factor-based learning value X ₁ in step 43 is unnecessary. Or suppress the reduction width.

Thus, when not X ₁ ≒ ₁ is a learning weighting parameters K ₁ only decrease corrected predetermined amount small amount [Delta] K ₁ depending on whether X ₁ is reduced or modified is increased corrected, the learning weighting parameters K _1, which is initially set It will be changed so that it will be the optimum value corresponding to the engine.

In step 49-52, the learning weights in the same manner as the modification the learning weighting parameters K ₁ Parameter K ₂
Is corrected to the optimum value depending on whether X ₂ is increased or decreased, and the map value is rewritten to set the ratio of the parameter K ₂ · Δα of the Q factor to a value corresponding to the engine.

Therefore, the steps 45 to 52 correspond to the analysis rule changing means.

As described above, the learning weighting parameters K _1, K ₂
Of the map value of the rewriting is finished, the program proceeds to step 53, writing the cause learning value X1, X ₂ is a modification results in a value or step 42 and 43 set in step 38 to a predetermined address of the RAM Rewrite the data with. This RAM
Is a backup memory, and the stored contents are retained even after the engine key switch is turned off.

Therefore, the part of step 44 corresponds to the factor-based learning value updating means.

In this way, the learning value X ₂ of the learning values X ₁ and Q factor of the F / I factor is not is determined, these correction on groups, as shown by step 7 in Figure 3, cause Separately, the optimum arithmetic expression is used.

That is, the arithmetic expression is set as the addition term for the basic fuel injection amount Tp for the learning value X ₁ of the F / I factor, and as the multiplication term for the basic fuel injection amount Tp for the learning value X ₂ of the Q factor. Optimal corrections are made.

Figure 7 shows that the effect of this learning control is + 16% of □.
The engine with a rich tendency of 4 approaches the engine with the central value of the variation of ● after learning about 4 times, and the engine of △ indicates-
The engine with a lean tendency of 16% approaches the center value engine with the variation marked by ● after three learnings, and clearly shows the improvement of 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 for detecting the intake air flow rate is shown, but a so-called D-Jetro for detecting the intake manifold negative pressure is shown. Method or so-called α depending on throttle valve opening (α) and engine speed (N)
-It can be applied to various systems such as N system.

Further, not only the feedback control of the air-fuel ratio, but also the ignition timing 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 instead of the conventional learning method for each area, and the factor-based learning is performed based on the correctness determination of the analysis result. The learning method is modified by correcting the value, and the analysis rule is modified according to this modification, so the learning speed can be greatly improved. However, it is possible to improve the accuracy of learning correction by optimizing the analysis rule. Further, by such learning control, it is possible to reduce matching man-hours, simplify component management, and maintain free. Furthermore, 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 basic control amount setting means for setting a basic control amount corresponding to a control target value of an object to be controlled of an internal combustion engine, and a direction in which a control target value and an actual value are compared to bring the actual value closer to the control target value. Feedback correction value setting means for increasing or decreasing the feedback correction value by a predetermined amount, rewritable learning value storage means for storing a plurality of learning values for each factor, and the basic control amount for the feedback correction value and Based on the plurality of factor-based learning values, control amount calculating means for calculating a control amount by correcting each with an arithmetic expression set accordingly, and controlling a control target of the internal combustion engine that operates according to the control amount Control means, a deviation detection means for detecting a deviation of the feedback correction value from a reference value, and a factor of the deviation of information concerning the engine operating state at the time of deviation detection and information concerning the deviation. A factor analysis means for analyzing the deviation based on at least one and separating the deviation into a plurality of parameters for each factor at a rate determined by the information; and a control calculated by the control amount calculation means when the deviation is detected by the deviation detection means. Of the factor analysis result based on the difference between the control amount obtained by correcting and calculating the basic control amount based on a plurality of factor-based learning values based on a plurality of parameters separated by the factor analysis means. Factor analysis result correctness determination means, factor analysis correction means for increasing or decreasing a plurality of parameters separated by the factor analysis means in a direction to reduce the difference based on the determination result by the factor analysis result correctness determination means, and the factor The learning value for each factor of the learning value for each factor is corrected and rewritten based on each of the plurality of parameters of the correction result by the analysis and correction unit. Learning of an internal combustion engine, characterized by comprising: factor-based learning value updating means; and analysis rule changing means by the factor analyzing means based on the increase / decrease correction results of a plurality of parameters by the factor analysis correcting means. Control device.