JPH01106941A - Control device for learning of internal combustion engine - Google Patents

Abstract

PURPOSE:To improve learning speed by dividing the deviation of a correction factor from its reference value due to feedback control into the factors of a learned value corresponding to a multiplication term caused by an airflow meter and a learned value corresponding to an addition term caused by a fuel injection valve. 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 control of an air-fuel ratio based on the detected value of the O2 sensor 16 and stores the deviation of a correction factor at that time from its reference value. This deviation is divided based on the engine speed and the basic fuel injection quantity, to carry out renewal for a learned value corresponding to a multiplication term caused by the airflow meter 13 and for a learned value corresponding to an addition term caused by the fuel injection valve 6.

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 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 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 (E) control amount calculation means for calculating the control amount by dividing the plurality of factor-based learned values into addition terms and multiplication terms for the basic control amount and correcting them accordingly; control means (P) that operates according to the amount of control to control a controlled object of the internal combustion engine; (G) deviation detection means that detects a deviation of the feedback correction value from a reference value; and (G) analysis of the cause of the deviation based on various information. Factor analysis means (H) for separating the deviation into a plurality of parameters for each factor based on the analysis results; and update of the learning value for each factor for correcting and rewriting the learning value for each factor in the storage means based on each of the plurality of parameters. Means (Function) The basic control amount setting means A sets the basic control amount corresponding to the control target value of the control target of the internal combustion engine (for example, air-fuel ratio 2 ignition timing, idle rotation speed, etc.), and the feedback correction value setting means B The control target value and the actual value are compared, and 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 control target value. Then, the control amount calculation means corrects the basic control amount with the feedback correction value, and further adds a plurality of factor-specific learning values stored in the factor-specific learning value storage means C to the addition term and multiplication term to the basic control amount. The control amount is calculated by dividing the control amount into two parts and optimally correcting them accordingly.

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, the deviation (error amount) of feedback control is detected, and this is inferred and factor-analyzed using various information and databases, separated into multiple parameters, and the optimal calculation formula for each factor is calculated. That is, by dividing the basic control amount into an addition term and a multiplication term and correcting them with high accuracy, 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 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 reduces NO to 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 mentioned above, a hot wire type or flap type air flow meter 3 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 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 0□ concentration in the exhaust gas. If the 0□ sensor 16 is one 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 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.
Calculate -Q/N (K is a constant) on =. This step 2 corresponds to the basic control amount setting means.

In step 3, the water temperature correction coefficient K TW is calculated according to the water temperature Tw.
+ Various correction coefficients COE F including R etc. in the air-fuel ratio correction coefficient according to the engine speed N and the basic fuel injection amount Tp
= 1 + KTw + KMR +... is set.

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 XI and 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
+ = 0 and X2 = 1 are stored.

In step 7, the fuel injection amount Ti is calculated according to the following equation. Here, an arithmetic expression is determined for one of the factor-specific learned values X as an addition term to the basic fuel injection amount Tp, and for the other factor-specific learned value X2 as a multiplication term for the basic fuel injection amount Tp. This step 7 corresponds to the control amount calculation means.

Ti=X!・'rp −C0EF・α+(T s +
XI) In step 8, the calculated Ti is set in the output register. This allows predetermined engine rotation synchronization (
At the timing of fuel injection (for example, every 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 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 KMR, 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 the reversal), and when the air-fuel ratio is reversed, the process proceeds to step 15. After storing the previous deviation of the air-fuel ratio feedback correction coefficient α from the reference value 1 as a=α−1 for the optimum learning routine shown in FIG. Increase the previous value by a predetermined proportionality constant of 2 minutes. Otherwise, the process proceeds to step 17, where the air-fuel ratio feedback correction coefficient α is increased by a predetermined integral constant x4 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>>T.

When the air-fuel ratio is rich (■.z>Vrar), 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 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 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 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 condition is satisfied, the process proceeds to step 32 and the output voltage V of the 0□ sensor 16 is set. 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.

02 Output voltage 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. 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 0□ Output voltage of sensor 16■
. Engine speed N and basic fuel injection amount while 2 is reversed
Movement of rp (N I , N Z..., Tpt, Tp
z...) and specify the engine operating state (N, Tp).

Next, proceed to step 36 to check the engine operating status (N, TP).
The learning weight assigned to each area is searched for by referring to the map from the area of . however,
2 for K and + 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 divided into K1. It is expressed as 2.

Then, from a rule of thumb, it is assumed that the F/■ factor is large in the low rotation and low load area, and the Q factor is large in the high rotation and high load area, and the values of K I and K z are assigned to each area.
By referring to this map, factor analysis is performed based on the engine operating status.

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

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 Xs and Xt for each factor stored in a predetermined address on the RAM are read out, and the deviation Δα is set to the learned value XI of one F/I factor by M8 as shown in the following equation.
Add and update the other Q factor learning value X! deviation Δ
Update α2 by adding M2. M+ and Mz are learning weighting coefficients.

X, =X, +M,・Δα1 X t =X t + M t・Δα2 Next step 3
The program proceeds to step 9 and writes these factor-specific learning values X+ and Xz to a predetermined address on the RAM to rewrite the data. This R.A.
M 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 X and the Q factor learning value Based on the calculation formula, it is performed in the optimal form for each factor.

T i =Xg・'rp −C0EF・α+(Ts+X
+) In other words, since the learned value X of the F/I factor is caused by the fuel injector 6 itself, the voltage correction numerator s
Similarly, as an addition term to the basic fuel injection amount Tp, and for the learned value X2 of the Q factor, the basic fuel injection amount Tp
This is due to the intake air flow rate Q in (= -Q/N) and is used to correct the constant K, so an arithmetic expression is set as a multiplication term for the basic fuel injection amount TP, and these are used to determine the optimal Corrections are made.

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 and learning by factor, and learning by factor. Divide the value into an addition term and a multiplication term for the basic control amount,
Since we have adopted a method of correction using the optimal calculation formula for each factor,
It is possible to significantly improve learning speed while increasing learning correction accuracy. Also, with this kind of learning control,
Reduction of matching man-hours 9 Simplification of parts 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 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... 02 sensor patent applicant Japan Electronics Co., Ltd. Agent Patent attorney Fujio Sasashima 7 NOx1 Fig. 6: gr/km)

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. Correct it with
Further, a control amount calculation means for calculating a control amount by dividing the plurality of factor-based learned values into an addition term and a multiplication term for the basic control amount and correcting them accordingly; and operating according to the control amount. control means for controlling a controlled object of the internal combustion engine; deviation detection means for detecting a deviation of the feedback correction value from a reference value; and analysis of the cause of the deviation based on various information and detecting the deviation based on the analysis result. factor analysis means for separating the parameter into a plurality of parameters according to factors; 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. A learning control device for an internal combustion engine, characterized in that: