JPH01106947A - Control device for learning of internal combustion engine - Google Patents

Abstract

PURPOSE:To improve accuracy in learning by changing an analysis rule by the correctness of a factor analysis based on the difference between a controlling quantity which is corrected based on a learned value classified by factor and a controlling quantity to which a feedback correction value is added, at the time of analyzing the deviation of a feedback correction value from its reference value being classified by plural factors to renew the learned value. CONSTITUTION:The detected values of an airflow meter 13, a crank angle sensor 14, an O2 sensor 16, etc. are inputted into a control unit 12 to operatingly control the valve opening time of a fuel injection valve 6. The control unit 12 analyzes and divides the deviation of an air-fuel ratio feedback correction value based on the O2 sensor 16 from its reference value into the factors of one caused by the airflow meter 13 and one caused by the fuel injection valve 6, etc., based on an engine speed and a basic fuel injecting quantity. By judging the correctness of the factor analysis based on the difference between a controlling quantity in which a basic controlling quantity is corrected based on a learned value classified by factor and a controlling quantity on which feedback correction is carried out, the learned value classified by factor is renewed in the direction of removing this difference while changing an analysis rule.

Description

[Detailed description of the invention] <Industrial application field> The present invention relates to an air-fuel ratio (fuel injection amount) for an internal combustion engine.

This invention relates to a learning control device for a feedback control system such as ignition timing and idle speed.

<Prior art> As a conventional learning control device for an internal combustion engine,
203828, JP-A-59-211738, and JP-A-60-90944.

There is one disclosed in Japanese Patent Application Laid-Open No. 61-190141.

These are feedback correction values that are set using proportional/integral control, etc., while comparing the basic control amount, which is set in accordance with the control target value such as the air-fuel ratio, based on the operating state of the engine, with the control target value and the actual value. In systems that perform feedback control of the air-fuel ratio, etc. to the control target value by correcting the control amount and controlling this control amount, the deviation from the reference value of the feedback correction value during feedback control is calculated based on the engine operating state. Learn for each area and determine the learning value for each area.
When calculating the control amount, the basic control amount is corrected using the learning value for each area so that the control amount obtained by calculating the control amount without correction using the feedback correction value matches the control target value. is further corrected using a feedback correction value to calculate the control amount.

According to this, it is possible to eliminate the follow-up delay of feedback control that occurs during transient operation during feedback control, and it is possible to accurately obtain a desired control output when feedback control is stopped.

Therefore, it absorbs variations in component parts such as electronically controlled fuel injection devices, and also absorbs changes over time such as engine filling efficiency, atmospheric pressure, etc.
It is used to maintain the maximum performance of the engine over a long period of time by correcting changes in operating environment conditions such as temperature and humidity.

<Problems to be Solved by the Invention> However, such conventional learning control devices use a so-called iterative learning method using a data map, that is, data map grid divisions are set according to engine operating conditions, and feedback control deviation amount in each learning area is calculated. Since this method repeatedly updates the information based on learning experience, it has the disadvantage that if each learning area classification is set in detail in order to improve learning correction accuracy, the learning update speed becomes slow.

In other words, learning correction accuracy and learning speed are contradictory conditions.

SUMMARY OF THE INVENTION In view of these conventional problems, it is an object of the present invention to provide a learning control device for an internal combustion engine that can significantly improve learning speed while increasing learning correction accuracy.

Means for Solving the Problems> In order to achieve the above object, the present invention constitutes a learning control device for an internal combustion engine, which includes the following means A-, as shown in FIG.

(A) Basic control amount setting means for setting the basic control amount corresponding to the control target value of the controlled object of the internal combustion engine. (B) Comparing the control target value and the actual value and moving 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 (C) Rewritable factor-specific learned value storage means for storing a plurality of factor-specific learned values (D) Setting the basic control amount to the feedback correction value and a control amount calculation means (H) that calculates a control amount by correcting it with arithmetic formulas set according to the plurality of factor-specific learning values, and operates in accordance with the control amount to control the control target of the internal combustion engine. Control means for controlling (P) Deviation detecting means for detecting the deviation of the feedback correction value from the reference value (G) Analyzing the causes of the deviation based on various information and detecting the deviation by factor based on the analysis result Factor analysis means (H) for separating into parameters (H) a plurality of factor-based learning based on the control amount calculated by the control amount calculation means when the deviation detection means detects the deviation and the plurality of parameters separated by the factor analysis means; Factor analysis result correct/incorrect judgment means (I) for judging the correctness or incorrectness of the factor analysis result based on the difference from the control amount obtained by correcting the basic control amount based on the value; factor analysis correction means (J) for increasing or decreasing the plurality of parameters separated by the factor analysis means in a direction to reduce the difference based on the factor analysis correction means; Factor-specific learning value updating means (K) for correcting and rewriting the factor-specific learning values in the value storage means; analysis for changing analysis rules by the factor analysis means based on the results of increase/decrease correction of a plurality of parameters by the factor analysis correction means; Operation of the rule changing means> The basic control amount setting means A sets the basic control amount corresponding to the control target value of the controlled object of the internal combustion engine, and the feedback correction value setting means B compares the control target value and the actual value. Then, the feedback correction value is increased or decreased by a predetermined amount based on, for example, proportional/integral control and set in a direction that brings the actual value closer to the control target value. Then, the control amount calculation means corrects the basic control amount with the feedback correction value, and further calculates the optimal value set according to the plurality of factor-specific learning values stored in the factor-specific learning value storage means C. A control amount is calculated by correcting it using an arithmetic expression, and the control means E operates according to this control amount to control the control object of the internal combustion engine.

On the other hand, the deviation detection means F detects the deviation of the feedback correction value from the reference value. And factor analysis means G
The method inferentially analyzes the factors that led to the deviation based on various information (for example, at least one of the following: engine operating status, deviation amount, deviation direction, 'deviation speed, deviation direction, etc.) according to predetermined analysis rules. Then, based on the analysis results, the deviation is separated into multiple parameters according to factors.

The factor analysis result correctness determination means H includes 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-specific learning value of the factor analysis result. The correctness or failure of the factor analysis result is determined based on the difference between the factors. Then, the factor analysis correction means I corrects the plurality of parameters in the direction of reducing the difference based on the correctness or failure of this factor analysis result, and based on this correction result, the factor-based learned value updating means J updates the factor-based learned value. The learning values for each factor in the storage means C are corrected and rewritten.

Further, the analysis rule changing means changes the analysis rule by the factor analysis means G based on how the plurality of parameters have been increased or decreased by the factor analysis correction means I.

That is, when the factor analysis means 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 based on the learning value for each factor based on the analysis result, and control based on the feedback correction is performed. By comparing the learning results with the values, it is possible to determine whether the learning results from the factor analysis are consistent with the feedback correction that brings the actual value closer to the target value. If the factor analysis is inaccurate and does not match the correction by the feedback correction, the factor analysis correction means I adjusts a plurality of parameters for each factor to reduce this difference. The correction result is stored in the factor-specific learning value storage means C.

Furthermore, when correction by the factor analysis correction means (■) is required, since the rule of factor analysis by the factor analysis means G is inaccurate, the analysis rule change means K changes the analysis rule according to the correction direction. This eliminates the need for correction using the factor analysis correction means ■, and allows factor analysis to be performed in accordance with the institution.

2 In this way, the deviation (error amount) of feedback control
is detected, and this is inferred and factor analyzed using various information and databases, and this factor analysis is corrected so that the actual value approaches the control target, and analysis rules are set according to the correction results. By changing the parameters to obtain the optimal analysis and performing accurate correction using calculation formulas suitable for each factor, both learning correction accuracy and learning speed can be achieved.

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

In FIG. 2, an engine 1 is connected to an air cleaner 2 through an intake duct 3. Air is taken in via the throttle valve 4 and the intake manifold 5. A fuel injection valve 6 as a control means is provided in a branch portion of the intake manifold 5 for each cylinder. The fuel injection valve 6 is an electromagnetic fuel injection valve that opens when the solenoid is energized and closes when the energization is stopped. Fuel is injected and supplied by a pump and adjusted to a predetermined pressure by a pressure regulator.

Note that this example is a multi-point injection system (M, P, 1. method), but a single-point injection system (S, P) has a single fuel injection valve common to all cylinders, such as upstream of the throttle valve.
, 1. method).

An ignition plug 7 is provided in the combustion chamber of the engine 1, which ignites a spark to ignite and burn the air-fuel mixture.

From engine 1, exhaust manifold 8° exhaust duct 9. Exhaust gas is discharged through a three-way catalyst 10 and a muffler. The three-way catalyst 10 is an exhaust purification device that oxidizes Go and HC in the exhaust components, and also reduces NOx to convert it into other harmless substances, and when the air-fuel mixture is burned at the stoichiometric air-fuel ratio. The reconversion efficiency is the best.

The control unit 12 includes a CPU and a ROM.

It is equipped with a microcomputer including a RAM, an A/D converter, and an input/output interface, and receives input signals from various sensors, performs arithmetic processing as described below, and controls the operation of the fuel injection valve 6. .

As the various sensors mentioned above, 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 MQ.

Further, a crank angle sensor 14 is provided. , in the case of a 4-cylinder engine, a reference signal for every 180 degrees of crank angle and a unit signal for every 1 degree or 2 degrees of crank angle are output. Here, the engine speed N can be calculated by measuring the cycle of the reference signal or the number of unit signals generated within a predetermined time.

Further, a water temperature sensor 15 and the like are provided to detect the cooling water temperature Tw of the water jacket of the engine l.

Furthermore, the 02 sensor 16 is located at the collecting part of the exhaust manifold 8.
is provided to detect the air-fuel ratio of the air-fuel mixture taken into the engine 1 via the 02 concentration in the exhaust gas. If the 02 sensor 16 is equipped with a NOx reduction catalyst layer as proposed in Japanese Patent Application No. 62-65844, more accurate detection will be possible.

Here, the CPU of the microcomputer built in the control unit 12 performs calculations according to programs (fuel injection amount calculation routine, air-fuel ratio feedback control routine, optimal learning routine) on the ROM shown as flowcharts in FIGS. 3 to 5. processing and control fuel injection.

The basic control amount setting means, the feedback correction value setting means, the control amount calculation means, the deviation detection means, the factor analysis means, the learning value update means for each factor, the factor analysis result correctness determination means, the factor analysis correction means, and the analysis rule change means The function as
This is achieved by the program. Further, a RAM is used as the factor-specific learning value storage means, and the stored contents are retained even after the engine key switch is turned off using a backup power source.

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

FIG. 3 shows a fuel injection amount calculation routine, which is executed at predetermined time intervals.

In step 1 (denoted as Sl in the figure; the same applies hereinafter), the intake air flow rate Q is detected based on the signal from the air flow meter 13, and the engine speed N is calculated based on the signal from the crank angle sensor 14. .

Water temperature T detected based on the signal from the water temperature sensor 15
Enter w etc.

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

In step 3, a water temperature correction coefficient Ktw corresponding to the water temperature Tw,
Various correction coefficients C0EF=1+KT including air-fuel ratio correction coefficient KNR etc. according to engine speed N and basic fuel injection amount Tp
Set R+... to 11+.

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

In step 5, the voltage correction numerator s is calculated based on the battery voltage.
Set. This is to correct changes in the injection flow rate of the fuel injection valve 6 due to changes in battery voltage.

In step 6, the factor-specific learning values X, , X2 are read from a predetermined address of the RAM serving as the factor-specific learning value storage means. Note that when learning has not started, the initial value is x
, =o, X2 =1 are stored.

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

Ti-X2.Tp-COEF.alpha.0 (Ts+Xl) In step 8, the calculated Ti is set in the output register. As a result, when a predetermined engine rotation synchronization (for example, every rotation) fuel injection timing is reached, a drive pulse signal having a pulse width of the latest set Ti is applied to the fuel injection valve 6, and fuel injection is performed.

FIG. 4 shows an air-fuel ratio feedback control routine, which is executed in rotational synchronization or time synchronization, and thereby sets the air-fuel ratio feedback correction coefficient α. Therefore, this routine corresponds to feedback correction value setting means.

In step 11, it is determined whether predetermined air-fuel ratio feedback control conditions are satisfied. Here, the predetermined air-fuel ratio feedback control condition is that the engine speed N is equal to or less than a predetermined value, and the basic fuel injection amount Tp representing the load is equal to or less than a predetermined value. If such conditions are not met, this routine is terminated. In this case, the air-fuel ratio feedback correction coefficient α is the previous value (or reference value 1)
is clamped, and air-fuel ratio feedback control is stopped. This stops the air-fuel ratio feedback control in the high rotation or high load region, and obtains a rich output air-fuel ratio by changing the air-fuel ratio correction coefficient □ to suppress the rise in exhaust temperature and prevent engine seizure and This is to prevent the base catalyst 10 from burning out.

When the air-fuel ratio feedback control conditions are met, step 1
Proceed to step 2 and beyond.

In step 12, the output voltage of the sensor 16 is 0□■. 2 is read, and in the next step 13, it is compared with the slice level voltage V rot corresponding to the stoichiometric air-fuel ratio to determine whether the actual air-fuel ratio is richer or leaner than the stoichiometric air-fuel ratio. That is, in this embodiment, the control target is the air-fuel ratio of the engine intake air-fuel mixture, and the control target is the stoichiometric air-fuel ratio.

When the air-fuel ratio is lean (■.t<V□f), proceed from step 13 to step 14 to determine whether it is the time of reversal from rich to lean (immediately after reversal), and when the ratio is reversed, proceed to step 15. For the optimal learning routine of FIG. 5, which will be described later, the previous deviation of the air-fuel ratio feedback correction coefficient α from the reference value 1 is stored as a=α−1, and then the process proceeds to step 16 where the air-fuel ratio feedback correction coefficient α is calculated. is increased by a predetermined proportionality constant P with respect to the previous value. Otherwise, the process proceeds to step 17, where the air-fuel ratio feedback correction coefficient α is increased by a predetermined integral constant of 1 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 (■.2>Vrar), the process proceeds from step 13 to step 18, and it is determined whether or not there is a reversal from lean to rich (immediately after the reversal), and when the air-fuel ratio is reversed, the process proceeds to step 19. After storing the previous deviation of the air-fuel ratio feedback correction coefficient α from the reference value 1 as b=α−1 for the optimum learning routine shown in FIG. The previous value is decreased by a predetermined proportionality constant P. Otherwise, the process proceeds to step 21, where the air-fuel ratio feedback correction coefficient α is decreased by a predetermined integral constant of 1 minute from the previous value, and thus the air-fuel ratio feedback correction coefficient α is decreased at a constant slope.

Figure 5 shows the optimal learning routine, which is executed at predetermined intervals,
As a result, the factor-specific learning value X1. X2 is set/updated.

In step 31, it is determined whether a predetermined learning condition is satisfied. Here, the predetermined learning condition is that the air-fuel ratio is under feedback control and that the rich/lean signal of the 02 sensor 16 is inverted at an appropriate period. If such conditions are not met, this routine is terminated.

If the predetermined learning conditions are met, the process advances to step 32 and the output voltage of the 02 sensor 16 is determined. 2 is reversed, and if it is not reversed, the process proceeds to step 33, where the engine speed N and basic fuel injection amount Tp are sampled as data on the engine operating state at that time.

When the output voltage of the 0□ sensor 16 is inverted, the process proceeds to step 34 to obtain the average value of a and b described above for optimal learning. At this time, a and b are the upper and lower peak values of the deviation of the air-fuel ratio feedback correction coefficient α from the reference value 1 from reversal to reversal of the increase/decrease direction of the air-fuel ratio feedback correction coefficient α, as shown in FIG. , by calculating these average values, the reference value 1 of the air-fuel ratio feedback correction coefficient α is determined.
The average deviation Δα from Δα is detected.

Therefore, step 15.19 in FIG. 4 and step 34 in FIG. 5 correspond to the deviation detection means.

Next, proceed to step 35 to check the output voltage V of the 0□ sensor 16.
. Engine speed N and basic fuel injection amount while 2 is reversed
Movement of rp (N1.N2...+Tp+, Tpz...
) to identify the engine operating state (N, Tp).

Next, proceed to step 36 and check the engine operating state (N, Tp).
The learning weight assigned to each area is searched for by referring to the map from the area of . however,
The initial value of 2 for Kl+ is 1 or less.

Here, the factors that led to the deviation Δα are mainly caused by the fuel injector 6 (hereinafter referred to as the F/I factor), and factors caused by the air flow meter 13 including changes in air density (hereinafter referred to as the Q factor). ), and the proportion of each is expressed by + and Kz.

Then, based on a rule of thumb, we estimate that the F/I critical factor is high in the low-speed, low-load region, and that the Q factor is large in the high-speed, high-load region, so we assign + and KzO values to each area, and use this map. By referring to this, factor analysis is performed based on the engine operating status.

As a result, the deviation Δα is added to the parameter of the F/I factor.
・Δα and the Q factor parameter Kz ・Δα can be separated, and in the next step 37, Δα
Separate into each parameter as α, −, Δα, Δα2−2, and Δα.

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

In addition to being performed based on the engine operating state as described above, the factor analysis may also be performed based on the amount of deviation, direction of deviation, speed of deviation, direction of deviation change, etc., and by inference from these databases.

Next, the process proceeds to step 38, where the factor-specific learned values X, , X2 stored in a predetermined address on the RAM are read out, and the deviation Δα, is set to M8 in the learned value X of one F/I factor as shown in the following equation.
The difference Δ is added to the learning value X2 of the other Q factor.
Update α2 by adding M2. M, , M2 are learning weighting coefficients.

X + = X + + M + ・Δα.

X2 = Xz + Mz ・Δα2 Next step 3
9, the fuel injection amount Ti is calculated using the factor-specific learned values X, , X2 updated in step 38 above. however,
The calculation formula for the fuel injection amount Ti at this time does not include the air-fuel ratio feedback correction coefficient α, as shown below, and does not include the feedback correction coefficient α and the newly updated factor-based learning value X8. The fuel injection amount Ti is calculated using X2.

Ti=Xz·Tp-COEF+(TS+XI) In the next step 40, when the deviation Δα is detected, the fuel injection amount Ti calculated in the fuel injection amount calculation routine of FIG. 3 is read and this value is set as MTi.

The fuel injection amount Ti when the deviation Δα is detected is, for example, the average value of the fuel injection amount Ti when the upper and lower peak values of the air-fuel ratio feedback correction coefficient α are taken.

Next, proceeding to step 41, the fuel injection amount Ti calculated without the air-fuel ratio feedback correction coefficient α in step 39,
Learning by factor (iX+) read in step 40.

Compare the fuel injection amount MTi set using the air-fuel ratio feedback correction coefficient α, which was the basis for updating X2,
Determine whether the factor analysis is correct or not. Therefore, this step 40
．． The part 41 corresponds to means for determining whether the factor analysis result is correct or incorrect.

Here, if it is determined that T i is MT i, the factor-specific learning value X + updated by the current factor analysis.

By using X2, it is determined that an air-fuel mixture approximately equivalent to the stoichiometric air-fuel ratio is obtained by injecting and supplying fuel equivalent to the calculated fuel injection amount Ti to the engine 1, even without using the air-fuel ratio feedback correction coefficient α. .

This is because the air-fuel ratio feedback correction coefficient α is set to approximate the actual air-fuel ratio to the stoichiometric air-fuel ratio, which is the target air-fuel ratio. It can be said that the fuel injection amount is equivalent to the fuel ratio, and on the other hand, the learning value for each factor obtained from the results of this factor analysis is X,,X.

If the fuel injection amount Ti calculated without using the air-fuel ratio feedback correction coefficient α is approximately equal to the fuel injection amount MTi corresponding to this stoichiometric air-fuel ratio, then the factor analysis results indicate that the target can be achieved without the air-fuel ratio feedback correction coefficient α. This means that a certain stoichiometric air-fuel ratio can be approximately obtained, and the factors are correctly analyzed and the learning is found to be accurate.

On the other hand, in step 41, Ti<<MTi or Ti>>M
If it is determined to be Ti, then the fuel injection amount Ti may have been calculated using the factor-specific learned values X=, Xz obtained in step 38 through factor analysis without the air-fuel ratio feedback correction coefficient It turns out that the target stoichiometric air-fuel ratio cannot be obtained.

That is, the fuel amount vs. amount M obtained by air-fuel ratio feedback correction
When the fuel injection amount Ti calculated in step 39 is smaller than Ti, when setting the actual fuel injection amount Ti,
It is necessary to increase the fuel injection amount Ti using the air-fuel ratio feed puncture correction coefficient α. On the other hand, when the fuel injection amount Ti calculated in step 39 is greater than the fuel injection amount MTi, fuel injection is performed using the air-fuel ratio feedback correction coefficient α. It is necessary to correct the amount Ti by decreasing it.

Therefore, in a state where the fuel injection amount Ti is corrected to the stoichiometric air-fuel ratio by the air-fuel ratio feedback correction coefficient α, it can be said that the factor analysis result is poor, and in this case, proceed to step 42 or step 43. , the factor-based learning values X, , X2 are increased or decreased as explained below so that a fuel injection amount Ti equivalent to the stoichiometric air-fuel ratio can be obtained without the air-fuel ratio feedback correction coefficient α.

If it is determined in step 41 that T i <<MT i, the factor-specific learning value X obtained in step 38.

If the fuel injection amount Ti is calculated using only X2 without using the air-fuel ratio feedback correction coefficient α, the fuel amount will be insufficient and the air-fuel ratio will become lean, so step
Proceed to step 2 and obtain the factor-specific learning values X, , X, obtained in step 38.
Add the minute values ΔX+ and ΔX2 to each to create a new learning value for each factor X, ,X, XI←XI+ΔX, ,X,
←X2+ΔX2), fuel injection amount Ti is factor-specific learning value X,
, X2, and then the process returns to step 39 again.

That is, the correction of the factor-based learning values Xl and Xz in step 42 is repeated until TiζMTi is reached.

On the other hand, if it is determined in step 41 that T i >>MT i, the factor-specific learning value X obtained in step 38
,, If the fuel injection amount Ti is calculated using only X2 and without using the air-fuel ratio feedback correction coefficient α, the fuel amount is excessive and the air-fuel ratio becomes rich, so the process proceeds to step 43 and then to step 38. Obtained factor-specific learning value X,,X
, respectively, the minute value Δxl.

Subtract ΔX2 to create new learning values for each factor X, , X, binding (
x, -X, -ΔX+ , X2 ←Xz AXz),
The fuel injection amount Ti is further reduced by the factor-based learning values X+ and Xz, and the process returns to step 39, where T
The correction of the factor-based learning values X, , X2 in step 43 is repeated until iζMTi is reached.

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

Here, if the correction in step 42 or step 43 is corrected to the factor-based learning value X+, T i #M in step 41 using the learning values X+ and Xz for each factor.
If it is determined as T i, the process proceeds to step 44, where the factor-specific learning values X+ and Xz (values set in step 38) before correction are read out and set as Y, , X-, respectively.

Then, in the next step 45, the final resultant learning value X1 for each factor is compared with the aforementioned Xl. Here, the final result of the learning value X1 for each factor is Ti#MT at the first time in step 41.
When it is determined that i, this is the setting result at step 38, and when it is determined that Ti≠MTi, this is the final correction result at step 42 or step 43.

If it is determined in step 45 that XIζX, the correction is small or step 42 or
This indicates that the correction in step 3 was not made, so steps 46 to 48 are jumped and the process proceeds to step 49, but XI
If it is determined that "=X+" is not the case, this indicates that the increase/decrease has been modified by more than a predetermined value, so the process proceeds to step 46 or step 47, and the learning weighting is performed by modifying the parameter KI by 2.

That is, when it is determined in step 45 that X + > Since there is no one, this indicates that the factor-specific learning value Set the parameters, and in the next step 48, the learning weighting is performed using the parameter K1. Rewrite the K z map. As a result, next time, the parameter of F/I factor will be 1 ・Δ
By increasing the ratio of separation into α and increasing the factor-specific learning value x1, the factor-specific learning value
.

Either eliminates the need for an increase correction or suppresses the increase.

On the other hand, if it is determined in step 45 that x+<Y, this indicates that the factor-specific learning value A new learning weight is set by subtracting , and the parameter KI is set to 1 in the next step 48.
Perform rewriting. As a result, in the next time, the ratio of the parameters of the F/I factor to be separated into 1 and Δα is reduced, and the learning value for each factor, X, is reduced, so that step 43
This eliminates the need to correct the decrease in the factor-specific learning value

In this way, when it is not X, #Y, the learning weighting is modified by increasing or decreasing the parameter 1 by a predetermined minute amount Δ, depending on whether XI has been modified to increase or decrease, and the initially set learning weighting is The parameters are changed to the optimum values corresponding to the institution.

In addition, in steps 49 to 52, the above-mentioned learning weighting is done in the same way as the modification of 1 to the parameter, and the learning weighting is modified to the optimum value by modifying the parameter 2 depending on whether X2 has been modified to increase or decrease, and the map value is rewritten. By doing so, the ratio of 2·Δα is set to a value corresponding to the engine in the parameter of the Q factor.

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

As described above, learning weighting is applied to the parameter KI+ by 2.
When the rewriting of the map values is completed, the process proceeds to step 53, where the factor-specific learned values X, , and rewrite the data. This RAM is a backup memory, and the stored contents are retained even after the engine key switch is turned off.

Therefore, the step 440 corresponds to a factor-based learning value updating means.

In this way, the learned value X of the F/I factor and the learned value X2 of the Q factor are determined, and the correction based on these 30 is shown in step 7 of Figure 3. The calculation is performed using the optimal calculation formula for each factor.

That is, an arithmetic expression is set for the learned value X of the F/T factor as an addition term to the basic fuel injection amount Tp, and for the learned value X2 of the Q factor as a multiplication term to the basic fuel injection amount Tp. corrections will be made.

Figure 7 shows that the effect of this learning control is +16 on the landmark.
The engine with a rich tendency of % approaches the engine with the median variation of ∆ mark after about 4 learnings, and the engine with a lean tendency of 16% with ∆ mark after about 3 learnings.
This shows how the dispersion of the mark approaches the median value engine, clearly showing the improvement in learning speed by this learning control.

In this embodiment, a so-called L-Jetro system that has an air flow meter and detects the intake air flow rate is shown as the electronically controlled fuel injection system, but a so-called D-Jetro system that detects the intake manifold negative pressure is used. It can be applied to various systems such as the so-called α-N method based on the throttle valve opening (α) and the engine speed (N).

Moreover, it can be applied not only to air-fuel ratio feedback control, but also to ignition timing control based on knocking detection and idling speed feedback control via an auxiliary air valve.

<Effects of the Invention> As explained above, according to the present invention, instead of learning by area as in the past, the factors that led to the deviation are analyzed, and learning by factor is performed based on whether the analysis results are correct or incorrect. By using a method of learning by correcting the values and correcting the analysis rules in accordance with the corrections, the learning speed can be greatly improved, and since the accuracy of the analysis results is determined, errors in the analysis can be avoided. It is possible to improve the accuracy of learning correction by modifying the analysis rules and optimizing the analysis rules. Furthermore, such learning control can reduce the number of matching steps, simplify the management of parts, and make maintenance free. Furthermore, the capacity of the backup memory can also be reduced.

[Brief explanation of the drawing]

Fig. 1 is a functional block diagram showing the configuration of the present invention, Fig. 2 is a system diagram showing an embodiment of the invention, Figs. 3 to 5 are flow charts showing control details, and Fig. 6 is 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 1
4...Crank angle sensor 16...0□Sensor patent applicant Japan Electronics Co., Ltd. Agent Patent attorney Fujio Sasashima

Claims (1)

[Scope of Claims] Basic control amount setting means for setting a basic control amount corresponding to a control target value of a controlled object of an internal combustion engine, and comparing the control target value and the actual value 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 in the direction; rewritable factor-specific learning value storage means for storing a plurality of factor-specific learning values; and setting the basic control amount to the feedback correction value. and a control amount calculation means that calculates a control amount by correcting the plurality of factor-specific learned values using calculation formulas set accordingly, and operates in accordance with the control amount to control a controlled object of the internal combustion engine. a control means for detecting a deviation of the feedback correction value from a reference value, a deviation detection means for detecting a deviation of the feedback correction value from a reference value, and analyzing the cause of the deviation based on various information and converting the deviation into a plurality of parameters for each factor based on the analysis result. factor analysis means for separating; and a plurality of factor-specific learning values based on the control amount calculated by the control amount calculation means and the plurality of parameters separated by the factor analysis means when the deviation detection means detects the deviation. factor analysis result correct/incorrect determination means for determining whether the factor analysis result is correct or incorrect based on the difference between the basic control amount and the control amount obtained by correcting the basic control amount; factor analysis correction means for increasing or decreasing the plurality of parameters separated by the factor analysis means in the direction of increasing or decreasing the plurality of parameters separated by the factor analysis means; The learning value updating means for each factor corrects and rewrites the learned value, and the analysis rule changing means changes the analysis rule by the factor analysis means based on the increase/decrease correction result of a plurality of parameters by the factor analysis correction means. A learning control device for an internal combustion engine, comprising: