JPH01106937A - Control device for learning of internal combustion engine - Google Patents

Abstract

PURPOSE:To improve learning speed without lowering the accuracy of learned correction by dividing the deviation of a correction factor from its reference value due to feedback control into factors caused by a fuel injection valve, an airflow meter, etc. and learning same. 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. When an engine speed and a basic fuel injection quantity are below defined values, the control unit 12 carries out the feedback correction of an air-fuel ratio based on the detected value of the O2 sensor 16 and stores the deviation of the correction factor at that time from its reference value. This 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 based on the engine speed and the basic fuel injection quantity 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 area-specific learning value 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 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 H, 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 (E) a control amount calculating means for calculating a control amount by correcting the plurality of factor-specific learned values and using arithmetic formulas set accordingly. Control means for controlling (F) 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 (H) a factor analysis means for separating into parameters; (H) a factor-specific learning value updating means for correcting and rewriting the factor-specific learned value in the storage means based on each of the plurality of parameters; There is.

(1) Factor-specific learned value regulating means for regulating the upper and lower limits of the factor-specific learned values <Operation> The basic control amount setting means A sets a basic control amount corresponding to the control target value of the controlled object of the internal combustion engine, The feedback correction value setting means B compares the control target value and the actual value and sets the feedback correction value 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 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. 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 factor analysis means G
The system inferentially analyzes the factors that led to the deviation based on various information (for example, at least one of the following: engine operating condition, deviation amount, deviation direction, deviation speed, deviation direction, etc.) according to predetermined analysis rules. Based on the analysis results, the deviation is separated into multiple parameters according to factors. Then, the factor-specific learning value updating means H 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, by detecting the deviation (error amount) of feedback control, inferring it using various information and a database, analyzing the factors, and accurately correcting it using an arithmetic formula suitable for each factor, learning is possible. This achieves both correction accuracy and learning speed.

Further, the factor-specific learning value regulating means I regulates each factor-specific learning value to its upper and lower limit values. Thereby, safety against erroneous learning due to inference can be ensured.

<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 under pressure from the 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 a three-way catalyst 10 and a muffler 11. The three-way catalyst 10 converts Co and HC in the exhaust gas components.
This is an exhaust gas purification device that oxidizes NOl and reduces NOl to convert it into other harmless substances, and both conversion efficiencies are best when the air-fuel mixture is combusted at the stoichiometric air-fuel ratio.

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 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 16 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, 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 0□ sensor 16 is equipped with a NO8 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 functions of 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 factor-based learning value updating means, and the factor-based learning value regulation means are achieved by the program. Ru. 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, the water temperature correction coefficient K 7% according to the water temperature Tw
1. Various correction coefficients COE F including R etc. are added to the air-fuel ratio correction coefficient according to the engine speed N and the basic fuel injection amount Tp.
Set = 1 + KT%l+ KMI+...

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, , X are obtained from a predetermined address of the RAM as the factor-specific learning value storage means.

Load. Note that at the time when learning has not started, X, = O, xg = 1 is stored as an initial value.

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・Tp j C0EF・α+(Ts+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 with a pulse width of Ti that has been set latest 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 'rp 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 the reference value 1
), 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 KNR, suppresses the rise in exhaust temperature, and prevents seizure of the engine 1. This is to prevent the 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 02 sensor 16 is ■. 2 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 raf corresponding to the stoichiometric air-fuel ratio.

When the air-fuel ratio is lean (VO2〈■1.8f), the process proceeds from step 13 to step 14, where it is determined whether or not it is the time of reversal from rich to lean (immediately after the reversal). After proceeding to step 16, the deviation from the reference value 1 of the previous air-fuel ratio feedback correction coefficient α is stored as a=α−1 for the optimum learning routine shown in FIG. 5, which will be described later. α is increased by a predetermined proportionality constant of 2 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>>1.

When the air-fuel ratio is rich (■., >v-f), the process proceeds from step 13 to step 18 to determine whether it is the time of reversal from lean to rich (immediately after reversal), and when the air-fuel ratio is reversed, the process proceeds to step 19. For the optimum learning routine shown in FIG. 5, which will be described later, the deviation of the previous air-fuel ratio feedback correction coefficient α from the reference value 1 is stored as b=α−1, and then the process proceeds to step 20 where the air-fuel ratio feedback correction coefficient α is calculated. is decreased by a predetermined proportionality constant of 2 minutes from the previous value. 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 time intervals.
As a result, the factor-specific learning values X+ and Xz are set and updated.

In step 31, it is determined whether a predetermined learning condition is satisfied. Here, the predetermined learning condition is that air-fuel ratio feedback control is in progress and that the rich/lean signal of the Oz 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 sensor 16 is set to 0□■. 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.

Output voltage V of Ot sensor 16. When 2 is reversed, the process proceeds to step 34 to obtain the average value of a and b 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. , 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 and output voltage of 02 sensor 16■
. 2 is reversed, engine speed N and basic fuel injection amount T
Movement of p (N+, Nz...+ T P + , T
p2...) and specify 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,
K + + K z is 1 or less.

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

Then, based on a rule of thumb, estimate that the F/■ factor is large in low-speed, low-load areas, and the Q factor is large in high-speed, high-load areas, and assign Kl and K2O values to each area, and refer to this map. Therefore, factor analysis is performed based on the engine operating status.

As a result, the deviation Δα is changed to the parameter KI of the F/I factor.
・Δα and 2 ・Δα can be separated into Q factor parameters, and in the next step 37, ΔαI
Separate into each parameter as = KI ・Δα, Δα2− 2 ・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 learned values XI and Xi for each factor stored in a predetermined address on the RAM are read out, and the deviation Δα is set to the learned value X of one F/I factor by M5 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,,M,is a learning weighting coefficient.

Xl=X++M+・Δα.

X t = X t + M z・Δα 2nd step 3
Proceeding to step 9, the learning values X+ and Xz for each factor are compared with their respective upper and lower limits, and if they exceed the upper and lower limits, the values are set to the upper and lower limits.

Next, the process proceeds to step 40, where these factor-specific learning values X+ and Xz are written to a predetermined address on the RAM to 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, steps 38 and 40 correspond to a factor-specific learning value updating means, and step 39 corresponds to a factor-specific learning value regulating means.

In this way, the learning value X1 of the F/I prisoner and the learning value X of the Q factor are determined. This is done using a separate optimal calculation formula.

In other words, for the learned value X1 of the F/I prisoner, the learned value Xt of the Q factor is added as an addition term to the basic fuel injection amount Tp.
As for the multiplication term for the basic fuel injection amount Tp,
An arithmetic expression is set, and the optimum correction is performed using this formula.

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 feedback control of the air-fuel ratio, but also to ignition control based on knocking detection and feedback control of idling speed via an auxiliary air valve.

<Effects of the Invention> As explained above, according to the present invention, instead of the conventional method of learning by area, the method of analyzing the factors that caused the deviation and learning for each factor improves the learning correction accuracy. Learning speed can be significantly increased without slowing down.

Furthermore, such learning control can reduce the number of matching steps, simplify component management, and make maintenance free. Furthermore, the capacity of the backup memory can also be reduced.

Furthermore, by setting upper and lower limits for the learned values for each factor, safety is ensured.

[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
14... Crank angle sensor 16... 02 sensor patent applicant Fujio Sasashima, agent of Japan Electronics Co., Ltd., patent attorney

Claims (2)

[Claims] (1) Basic control amount setting means for setting a basic control amount corresponding to the control target value of the controlled object of the internal combustion engine, and feedback in the direction of bringing the actual value closer to the control target value by comparing the control target value and the actual value. Feedback correction value setting means for increasing or decreasing a correction value by a predetermined amount; Rewritable factor-specific learned value storage means for storing a plurality of factor-specific learned values; a control amount calculation means that calculates a control amount by correcting it with arithmetic formulas set accordingly based on learned values for each factor; and a control means that operates in accordance with the control amount to control a controlled object of the internal combustion engine. and a deviation detection means for detecting a deviation of the feedback correction value from a reference value, and a factor for analyzing the cause of the deviation based on various information and separating the deviation into a plurality of parameters for each factor based on the analysis result. A learning control for an internal combustion engine, comprising: an analysis means; and a factor-specific learning value updating means for correcting and rewriting the factor-specific learning value in the storage means based on each of the plurality of parameters. Device. (2) Basic control amount setting means for setting a basic control amount corresponding to the control target value of the controlled object of the internal combustion engine, and feedback in the direction of bringing the actual value closer to the control target value by comparing the control target value and the actual value. Feedback correction value setting means for increasing or decreasing a correction value by a predetermined amount; Rewritable factor-specific learned value storage means for storing a plurality of factor-specific learned values; a control amount calculation means that calculates a control amount by correcting it with arithmetic formulas set accordingly based on learned values for each factor; and a control means that operates in accordance with the control amount to control a controlled object of the internal combustion engine. and a deviation detection means for detecting a deviation of the feedback correction value from a reference value, and a factor for analyzing the cause of the deviation based on various information and separating the deviation into a plurality of parameters for each factor based on the analysis result. analysis means; factor-specific learning value updating means for correcting and rewriting the factor-specific learning value in the storage means based on each of the plurality of parameters; and factor-specific learning value regulation for regulating upper and lower limits of the factor-specific learning value. A learning control device for an internal combustion engine, comprising: means;