JPH01106939A - Internal combustion engine learning control device - Google Patents

Abstract

(57) [Summary] This bulletin contains application data before electronic filing, so abstract data is not recorded.

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 to (ii), as shown in FIG.

(^) 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 (F) Deviation detection means for detecting the deviation of the feedback correction value from the reference value (G) Deviation temporary storage means for temporarily storing previously detected deviations (H) Factor analysis means (I) for analyzing the current deviation based on the speed of change of the deviation determined from the current deviation and separating the current deviation into a plurality of parameters according to factors based on the analysis result; (I) the storage based on each of the plurality of parameters; Factor-specific learned value update means for correcting and rewriting the factor-specific learned values of the means (Operation) The basic control amount setting means A is used to set control targets for the control target of the internal combustion engine (for example, air-fuel ratio 1 ignition timing, idle rotation speed, etc.) The feedback correction value setting means B compares the control target value and the actual value and sets the feedback correction value in a direction that brings the actual value closer to the control target value, for example, in proportional/integral control. The setting is increased or decreased by a predetermined amount based on the specified amount. The control amount calculation means corrects the basic control amount with the feedpack correction value, and further sets the basic control amount based on a 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 the optimal 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 deviation temporary storage means G stores previously detected deviations. And factor analysis means H
The analysis method speculatively analyzes the factors that led to the current deviation based on the speed of change in the deviation (including the direction of change depending on its sign) based on the current and past deviations, and calculates the analysis results. Separate the current deviation into multiple parameters based on factors. Then, the factor-specific learning value updating means I 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 deviation (error amount) of feedback control is detected, this is inferred and factor-analyzed using information on current and past deviations and the database, and accurate correction is made using an arithmetic formula suitable for each factor. By doing so, it is possible to achieve both learning correction accuracy and learning speed.

<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 a three-way catalyst 10 and a muffler 11. The three-way catalyst 10 is an exhaust purification device that oxidizes Co and HC in the exhaust components, and also reduces No and converts them into other harmless substances, and burns the air-fuel mixture at the stoichiometric air-fuel ratio. Sometimes both conversion efficiencies are the best.

The control unit 12 includes a CPU 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 4 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, the 0□ 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 0□ concentration in the exhaust gas. If the 0□ 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 functions of the basic control amount setting means, the feedback correction value setting means, the control amount calculation means, the deviation detection means, the deviation temporary storage means, the factor analysis means, and the learning value updating means for each factor are 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, 7w is added to the water temperature correction coefficient according to the water temperature Tw.
Various correction coefficients C0EF=1+K including air-fuel ratio correction coefficient KMR according to engine speed N and basic fuel injection amount Tp
Set tw+KMy+ten...

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,X, =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=X,・'rp −C0EF・α+(TS1X,)
In step 8, the calculated Ti is set in the output register. This allows predetermined engine rotation synchronization (for example, 1
At the fuel injection timing (for each rotation), a drive pulse signal having 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 Tp representing the load is equal to or less than a predetermined value. If such conditions are not met, this routine is terminated. In which 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 KNR, 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 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 ref corresponding to the stoichiometric air-fuel ratio.

When the air-fuel ratio is lean (Voz<V, -r), 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 reversal). After proceeding to step 15, the deviation from the previous air-fuel ratio feedback correction coefficient α from the reference value 1 is stored as a=α−1 for the optimal learning routine of FIG. The coefficient 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>>I.

When the air-fuel ratio is rich (Vow>Vr-r), the process proceeds from step 13 to step 18, where it is determined 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 of 2 minutes. 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 same 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 of the 02 sensor 16 is determined. Determine whether □ has been reversed or not, and end this routine if it is not reversed.

0, output voltage V of sensor 16. When 2 is reversed, the process proceeds to step 33 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 33 in FIG. 5 correspond to the deviation detection means.

Next, a factor analysis will be performed. Here, the factors that led to the deviation Δα are mainly those caused by the fuel injector 6 (hereinafter referred to as F/I critical factors), and those caused by changes in air density (
(hereinafter referred to as the Q factor).

In step 34, the past deviation Δα-M (in this example, the past five deviations Δα-5 to Δα-8) is read out, and the rate of change of the deviation ■Δα=Δα-8−Δα-2 is calculated.

This ■Δα also represents the direction of change depending on its sign.

Next, the process proceeds to step 35, where the degree of satisfaction Kg (-0 to 1) that the factor that led to the deviation Δα is the Q factor is searched by referring to the map based on the variation rate VΔα of the deviation.

This map includes, for example, (i) VΔα is large (this is not due to component deterioration, etc., because the progress of component deterioration is slow), (
ii) If VΔα is the child direction, then these (i),
It was created based on the inference that driving to high altitudes is considered from (ii) and that the Q factor is due to changes in air density.

Next, the process proceeds to step 36, where factors other than the Q factor are considered as F/I factors, and +=I Kz is calculated as the degree of satisfaction that the factor that gave the deviation Δα is the F/I factor.

As a result, the deviation Δα is set to 1 in the F/I prisoner factor parameter.
・Δα can be separated into 2 ・Δα in the Q factor parameter, and in the next step 37 Δα1
= to ・Δα, Δα2=to 2 ・Δα, and separate each parameter.

Therefore, steps 34 to 37 correspond to factor analysis means.

In addition to performing the factor analysis based on the change rate and direction of deviation in this way, it may also be made more reliable by using a database based on various information such as engine operating conditions.

Next, the process proceeds to step 38, where the factor-specific learning values X,,
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+ =Xl+M,・Δα1 The RAM is a backup memory, and the stored contents are retained even after the engine key switch is turned off.

Therefore, steps 38 and 39 correspond to the factor-specific 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 prisoner factor learned value X, the learned value X2 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.

Next, the process proceeds to step 40, where operations (i) to (V) below are performed in order, and the past five deviations Δα−□ are stored. This step 40 corresponds to the deviation temporary storage means.

(i) Δα−5←Δα−=(ii) Δα−4←Δα−3
(ij) Δα−3←Δα−, (iv) Δα−2←Δα−
1(v)Δα−1←Δα Figure 7 shows that the effect of this learning control is +16
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 of learning by area, a method of analyzing the factors that caused the deviation based on the speed of change of the deviation and learning by factor. Therefore, the learning speed can be significantly improved without reducing the learning correction accuracy. 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
14...Crank angle sensor 16...0□Sensor patent applicant Japan Electronics Co., Ltd. Agent Patent attorney Fujio Sasashima Figure 1D

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 temporary storage means for temporarily storing deviations detected in the past; Factor analysis means for analyzing the required deviation based on the change rate and separating the current deviation into a plurality of parameters according to factors based on the analysis results; and factor-specific learning of the storage means based on each of the plurality of parameters. A learning control device for an internal combustion engine, comprising: factor-based learning value updating means for correcting and rewriting values; and a learning control device for an internal combustion engine.