JPH01106940A - Control device for learning of internal combustion engine - Google Patents

Abstract

PURPOSE:To improve learning speed by temporarily storing the deviation of a correction factor from its reference value due to feedback control for each operating zone and dividing the deviation into factors of a fuel injection valve, an airflow meter, etc. based on the changing direction in the same zone. CONSTITUTION:The detected values of an airflow meter 13, a crank angle sensor 14, a cooling water temperature sensor 15, 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. When an engine speed and a basic fuel injection quantity are below defined values, the control unit 12 carries out the feedback control of an air-fuel ratio based on the detected value of the O2 sensor 16 and temporarily stores the deviation of a correction factor at that time from its reference value for each operating zone which is determined by the engine speed and basic fuel injection quantity. Based on the changing direction from the last-time value for each operating zone, the deviation is divided into one caused by the characteristic of the fuel injection valve 6 and one caused by the characteristic of the airflow meter 13 and renewed for each learning zone.

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 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 configures a learning control device for an internal combustion engine including the following means A-1, 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 for the feedback Controlled amount calculation means (E) that calculates a controlled amount by correcting it based on the correction value and the plurality of factor-specific learning values using arithmetic formulas set accordingly; (E) operating in accordance with the controlled amount to control the internal combustion engine; Control means for controlling the object (P) Deviation detection means for detecting the deviation of the feedback correction value from the reference value (G) Area-specific deviation for temporarily storing the detected deviation for each area of a plurality of different engine operating states Temporary storage means (H) Analyzes the cause of the deviation based on at least the direction of deviation among the stored contents of deviations in areas of a plurality of different past engine operating states, and stores the deviation in a plurality of parameters based on the analysis result. (I) Factor-specific learned value update means that corrects and rewrites the factor-specific learned value in the storage device based on each of the plurality of parameters. The feedback correction value setting means B compares the control target value and the actual value to perform control. The feedback correction value is set by increasing or decreasing a predetermined amount based on, for example, proportional/integral control in a direction that brings the actual value closer to the 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. The control amount is calculated by correcting it using a calculation formula.

Then, the control means E operates according to this control amount to control the controlled 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 the area-specific deviation temporary storage means G stores deviations detected in the past for each area of a plurality of different engine operating states. . Then, the factor analysis means H infers the factors that led to the current deviation based on at least the direction of the deviation from among the stored contents regarding deviations in areas of a plurality of different past engine operating conditions, according to a predetermined analysis rule. Based on the analysis results, the current deviation is separated into multiple parameters by factor. Here, for example, if there are deviations in the same direction in many areas, a factor analysis is performed, such as a change in air density, and the parameters are separated into factors. Then, the factor-specific learning value updating means (2) corrects and rewrites the factor-specific learning value in the storage means C based on each of the plurality of separated parameters.

In this way, the feedback control deviation (error amount) is detected, and this is inferred and factor-analyzed using information on deviations (especially deviation direction) in areas of multiple different past engine operating conditions and a database. By 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. Although this example is a multi-point injection system, it may also be a single-point injection system in which a single fuel injection valve is provided in common to all cylinders, such as upstream of a throttle valve.

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 via the three-way catalyst 1o and the muffler 11. The three-way catalyst 1o is an exhaust purification device that oxidizes Co and HC in the exhaust components, and also reduces NoX and converts it into other harmless substances, and when the air-fuel mixture is burned at the stoichiometric air-fuel ratio. In this case, the conversion efficiency is the best.

Control unit 7 and 12 are CPU and 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, processes them as described later, and controls the operation of the fuel injection valve 6.

As the various sensors described 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 rate Q.

Further, a crank angle sensor 14 is provided, and in the case of a four-cylinder engine, outputs a reference signal for every 180 degrees of crank angle and a unit signal for every 1 degree or 2 degrees of crank angle. 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 for detecting the cooling water temperature Tw of the water jacket of the engine 1 is provided.Furthermore, an 02 sensor 16 is provided at the gathering part of the exhaust manifold 8.
The air-fuel ratio of the air-fuel mixture taken into the engine 1 is detected through the 0□ concentration in the exhaust gas. In addition, as the 0□ sensor 16, a patent application was made in 1986.
More accurate detection is possible by using the NOx reduction catalyst layer proposed in No. 2-65844.

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 functions of basic control amount setting means, feedback correction value setting means, control amount calculation means, deviation detection means, area-specific deviation temporary storage means, factor analysis means, and factor-specific learned value updating means are achieved by the program. Ru. Also,
A RAM is used as the factor-specific learned value storage means, and the stored contents are retained even after the engine key switch is turned off by a backup power source.

Next, the state of the calculation processing of the micro combimeter in the control unit 12 will be explained with reference to the flowcharts shown in 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, the water temperature correction coefficient KTW according to the water temperature Tw,
Various correction coefficients COE F = including air-fuel ratio correction coefficient KMR, etc. according to engine speed N and basic fuel injection amount TP
Set 1 + Ktw+ KMR+...

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+ and Xz 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
, =O, X2 =1.

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=Xz・'rp −C0EF・α+(T s +
X + ) In step 8, the calculated Ti is set in the output register. As a result, when the predetermined fuel injection timing is synchronized with the engine rotation (for example, every rotation), a drive pulse signal having the most recently set Ti pulse is given 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, obtains a rich output air-fuel ratio using the air-fuel ratio correction coefficient KM□, and suppresses the rise in exhaust temperature.
This is to prevent seizure of the engine 1 and burnout of the three-way catalyst 10.

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

In step 12, the output voltage VO2 of the 02 sensor 16 is read, and in the next step 13, it is determined whether the air-fuel ratio is rich or lean by comparing it with the slice level voltage V Pof corresponding to the stoichiometric air-fuel ratio.

When the air-fuel ratio is lean (Voz<Vror), the process proceeds from step 13 to step 14, and it is determined whether or not it is the time of reversal from rich to lean (immediately after the reversal), and when the air-fuel ratio is reversed, the process proceeds to step 15, which will be described later. For the optimal learning routine shown in FIG. 5, 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 process proceeds to step 16 where the air-fuel ratio feedback correction coefficient α is set to the previous value. On the other hand, it is increased by a predetermined proportionality constant P. 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 (Voz>Vr-v), the process proceeds from step 13 to step 18 to determine whether or not it is the time of 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 deviation of the previous 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 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 values XI and X2 are set and 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 feedback control is being performed and 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 V of the 02 sensor I6 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.

0□Output voltage V of sensor 16. When 2 is reversed, the process proceeds to step 34 to obtain the average value of a and b for optimal learning. In this case, 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 I from reversal to reversal of the increase/decrease direction of the air-fuel ratio feedback correction coefficient α, as shown in FIG. , and by calculating these average values, the average deviation Δα of the air-fuel ratio feedback correction coefficient α from the reference value 1 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 02 sensor 16.
. 2 is reversed, engine speed N and basic fuel injection amount T
Movement of p (N1.N2..., TPI, TP2...)
is read and the area of the engine operating state (N, Tp) is specified.

Next, the process proceeds to step 36, where it is determined whether the area of the engine operating state (N, Tp) to which the current deviation Δα is given is equal to any of the three areas stored, and if so, this routine is terminated.

If they are not equal, proceed to step 37 and perform the following operation to store the deviations Δα−□ for three areas with different engine operating states (N, Tp).

Δα−3←Δα−2 Δα−20Δα−! Δα−1←Δα Therefore, steps 35 to 37 correspond to area-specific deviation temporary storage means. Note that the number of areas to be stored is not limited to three.

Next, a factor analysis will be performed. In addition, here, the factors that led to the deviation Δα are mainly caused by the fuel injector 6 (hereinafter referred to as F/'I factor) and those due to air density changes (hereinafter referred to as Q factor). Divide into.

In step 38, three different past engine operating states (N
, Tp) area deviation Δα−,4(Δα, ~Δα−1
) is read.

Next, proceed to step 39, check the number of areas where the deviation Δα-□ is in the (+) direction or the number of areas in the (-) direction, and refer to the map to confirm that the factor that led to the deviation Δα is the Q factor. Search for satisfaction level 2 (=O~1).

This map shows the deviation Δα−□ in the same direction in many areas.
It is created based on the inference that when it has, the Q factor is due to changes in air density.

Next, the process proceeds to step 40, where factors other than the Q factor are considered to be F/I factors, and 2 is calculated from =1 to the degree of satisfaction that the factor that led to the deviation Δα is the F/■ prisoner factor.

As a result, the deviation Δα is set to 1 in the F/I prisoner factor parameter.
It is now possible to separate Δα and the parameter of the Q factor into 2 ・Δα, and in the next step 41, Δα, =
Separate into each parameter as ・Δα, Δα2=2・Δα.

Therefore, steps 38 to 41 correspond to factor analysis means.

In addition to performing the factor analysis based on the deviation direction by area as described above, it may also be made more reliable by using a database based on various information such as the engine operating state.

Next, the process proceeds to step 42, where the factor-specific learning value X1. Read X2 and set the deviation Δα, M1, to the learning value X1 of one F/I factor as shown in the following formula.
The difference Δ is added to the learning value X2 of the other Q factor.
Update α2 by adding M2. M,,M,is a learning weighting coefficient.

X l = X + + M I・Δα.

X z = X z + M z ·Δα2 Next, the process proceeds to step 43, where these factor-specific learning values X, , X2 are written to a predetermined address on the RAM to rewrite the data. This R
AM is a backup memory, and the stored contents are retained even after the engine key switch is turned off.

Therefore, steps 42 and 43 correspond to the factor-based learning value updating means.

In this way, the F/I prisoner factor learning value XI and the Q factor learning value It is performed using the optimal calculation formula.

That is, for the F/I critical factor learned value Optimum correction is performed.

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 engine approaches the median dispersion value of the marks, 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 the conventional method (learning method for each area), the factors that led to the occurrence of the deviation are determined by determining the deviation (particularly the deviation) in the areas of multiple past engine operating states. This method analyzes the factors based on the direction (direction) and learns for each factor, making it possible to significantly improve learning speed without reducing learning correction accuracy.In addition, this kind of learning control reduces matching man-hours. Simplification of one-part management and maintenance-free operation can be realized.

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 contents, and Fig. 6 is an air-fuel ratio. FIG. 7 is a diagram showing how the feedback correction coefficient changes, and FIG. 7 is a diagram showing the effect of learning control. ■...Engine 6...Fuel injection valve 12...Control unit 13...Air flow meter
14...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; and an area-specific deviation temporary storage means for temporarily storing the detected deviation for each area of a plurality of different engine operating states; Factor analysis means for analyzing the cause of the deviation based on at least the direction of the deviation among the stored contents regarding deviations in areas of a plurality of different past engine operating states, and separating the deviation into a plurality of parameters based on the analysis result. A learning control device for an internal combustion engine, comprising: a learning value updating unit for modifying and rewriting the learning value for each factor in the storage unit based on each of the plurality of parameters.