JPH10252536A - Air/fuel ratio control device for internal combustion engine - Google Patents

Abstract

PROBLEM TO BE SOLVED: To improve control accuracy of cylinder-wise air/fuel ratio, by accurately estimating the cylinder-wise air/fuel ratio even in a transient condition of an engine. SOLUTION: A neural net inputting a plurality of engine operation parameters including a detection value of an air/fuel ratio sensor is used, cylinder-wise equivalent ratio HATAF is calculated (S1), and a change amount ΔA/F thereof is calculated (S2). In accordance with the cylinder-wise equivalent ratio change amount ΔA/F, a basic fuel amount Ti is corrected, a fuel injection amount Tout is calculated (S4).

Description

DETAILED DESCRIPTION OF THE INVENTION

[0001]

The present invention relates to an air-fuel ratio control device for controlling an air-fuel ratio of an air-fuel mixture supplied to an internal combustion engine provided with an air-fuel ratio sensor in an exhaust system.

[0002]

2. Description of the Related Art In an internal combustion engine having a plurality of cylinders, an air-fuel ratio sensor is provided at a portion of an exhaust pipe connected to each cylinder, and the detected value of the air-fuel ratio sensor describes the behavior of the exhaust system of the internal combustion engine. Based on an input of an observer for observing the internal state based on the model, the air-fuel ratio of each of the plurality of cylinders is estimated by the observer, and the air-fuel ratio of the mixture supplied to the engine according to the estimated cylinder-by-cylinder air-fuel ratio is estimated. A method of feedback-controlling the air-fuel ratio has been conventionally known (for example, Japanese Patent Application Laid-Open No. 8-232718).

[0003]

However, the cylinder-by-cylinder air-fuel ratio estimation using the observer is based on a state equation for an exhaust system model in a steady state. However, there is a problem that the estimation accuracy of the cylinder-by-cylinder air-fuel ratio is reduced, and in order to cope with this problem, a configuration is adopted in which the observer itself has a built-in integral compensator.

[0004] Therefore, in the transient state, there is a problem that a control delay of the air-fuel ratio feedback control according to the estimated cylinder-by-cylinder air-fuel ratio occurs, and the control accuracy of the air-fuel ratio decreases.

In addition to the case where the feedback control is performed by estimating the air-fuel ratio for each cylinder and the case where the air-fuel ratio is generally feedback-controlled in accordance with the detection value of the air-fuel ratio sensor, fuel is injected into the intake pipe. It is difficult to perform a control with good responsiveness in a transient state because there is a transportation delay until the air is actually sucked into the combustion chamber of each cylinder and a response delay of the air-fuel ratio sensor. For this reason, for example, feedforward control (fuel adhesion correction) for correcting the fuel injection amount by modeling the dynamic characteristics of the fuel injected into the intake pipe to compensate for the transport delay is known (for example, Japanese Unexamined Patent Application Publication No. Hei 7 (1994)).
-127496).

However, since the dynamic characteristics of the fuel greatly change depending on the engine operating state, it has been difficult to make accurate corrections over a wide range of the engine operating state.

SUMMARY OF THE INVENTION The present invention has been made in view of the above points, and has an object to accurately estimate an air-fuel ratio for each cylinder even in a transient state of an engine and to improve the control accuracy of the air-fuel ratio for each cylinder. A first object is to provide an apparatus, and a second object is to provide an air-fuel ratio control apparatus capable of performing more accurate air-fuel ratio control over a wide range of an engine operating state.

[0008]

According to a first aspect of the present invention, there is provided an internal combustion engine having a plurality of cylinders, wherein an exhaust pipe connected to each of the plurality of cylinders is provided. An air-fuel ratio control device for an internal combustion engine that includes an air-fuel ratio sensor provided in the air-fuel ratio sensor and controls an air-fuel ratio of an air-fuel mixture supplied to the engine using a detection value of the air-fuel ratio sensor. A cylinder-by-cylinder air-fuel ratio estimating means for estimating the cylinder-by-cylinder air-fuel ratio of the plurality of cylinders by using a neural network that inputs the operating parameters of the engine including: Feedback control means for performing feedback control of the air-fuel ratio of the air-fuel mixture supplied to the engine according to

According to this configuration, the cylinder-by-cylinder air-fuel ratio of a plurality of cylinders is estimated by using a neural network that inputs at least the operating parameters of the engine including the detection value of the air-fuel ratio sensor. The air-fuel ratio of the air-fuel mixture supplied to the engine is feedback-controlled in accordance with the fuel ratio.

According to a second aspect of the present invention, there is provided an air-fuel ratio sensor provided in an exhaust pipe of an internal combustion engine, and the air-fuel ratio sensor is supplied to the engine using a detected value of the air-fuel ratio sensor. In an air-fuel ratio control device for an internal combustion engine that controls an air-fuel ratio of an air-fuel mixture, a plurality of neural nets that input operating parameters of the engine are set, and the plurality of neural nets are set to the operating state of the engine or the air-fuel ratio. By switching and using according to the active state of the sensor,
Air-fuel ratio estimating means for estimating the air-fuel ratio of the air-fuel mixture supplied to the engine, and air-fuel ratio control for controlling the air-fuel ratio of the air-fuel mixture supplied to the engine according to the air-fuel ratio estimated by the air-fuel ratio estimating means Means.

According to this configuration, a plurality of neural nets which input the operating parameters of the engine are set, and the plurality of neural nets are switched and used according to the operating state of the engine or the activation state of the air-fuel ratio sensor. As a result, the air-fuel ratio of the air-fuel mixture supplied to the engine is estimated, and the air-fuel ratio of the air-fuel mixture supplied to the engine is controlled according to the estimated air-fuel ratio.

According to a third aspect of the present invention, in the air-fuel ratio control apparatus according to the second aspect, the plurality of neural nets include a neural net that does not receive a value detected by the air-fuel ratio sensor, and When the air-fuel ratio sensor is inactive, the estimating means estimates the air-fuel ratio using a neural network that does not receive the detection value of the air-fuel ratio sensor.

According to this configuration, when the air-fuel ratio sensor is inactive, the air-fuel ratio is estimated using a neural network that does not receive the detection value of the air-fuel ratio sensor, and the engine is operated in accordance with the estimated air-fuel ratio. The air-fuel ratio of the supplied air-fuel mixture is controlled.

According to a fourth aspect of the present invention, in the air-fuel ratio control device according to the third aspect, the air-fuel ratio control means calculates an air-fuel ratio control amount applied when the air-fuel ratio sensor is inactive. Calculating means, and second calculating means for calculating an air-fuel ratio control amount applied when the air-fuel ratio sensor is activated,
According to the activation state of the air-fuel ratio sensor, one of the first calculation means and the second calculation means is selected to calculate an air-fuel ratio control amount, and the air-fuel ratio control is performed using the calculated air-fuel ratio control amount. It is characterized by performing.

According to this configuration, the air-fuel ratio control amount is calculated by selecting one of the first calculation means and the second calculation means in accordance with the activation state of the air-fuel ratio sensor, and the calculated air-fuel ratio control amount is calculated. Is used to perform air-fuel ratio control.

[0016]

Embodiments of the present invention will be described below with reference to the drawings.

FIG. 1 is an overall configuration diagram of an internal combustion engine (hereinafter simply referred to as “engine”) and an air-fuel ratio control device thereof according to an embodiment of the present invention.
In the middle of the intake pipe 2, a throttle valve 3 is provided. A throttle valve opening (θTH) sensor 4 is connected to the throttle valve 3, outputs an electric signal corresponding to the opening of the throttle valve 3, and supplies it to an electronic control unit (hereinafter referred to as “ECU”) 5. .

A fuel injection valve 6 is provided for each cylinder between the engine 1 and the throttle valve 3 and slightly upstream of the intake valve (not shown) of the intake pipe 2, and each injection valve is connected to a fuel pump (not shown). The fuel injection time (valve opening time) is controlled by a signal from the ECU 5 while being electrically connected to the ECU 5.

On the other hand, an intake pipe absolute pressure (PBA) sensor 12 is provided immediately downstream of the throttle valve 3, and the absolute pressure signal converted into an electric signal by the absolute pressure sensor 12 is supplied to the ECU 5. . Further, an intake air temperature (TA) sensor 13 is attached downstream thereof, detects the intake air temperature TA, outputs a corresponding electric signal, and supplies the electric signal to the ECU 5.

The engine water temperature (TW) sensor 14 mounted on the main body of the engine 1 is composed of a thermistor or the like, detects the engine water temperature (cooling water temperature) TW, outputs a corresponding temperature signal, and supplies it to the ECU 5. Engine speed (NE)
The sensor 15 and the cylinder discrimination (CYL) sensor 16 are mounted around a camshaft (not shown) of the engine 1 or around a crankshaft. The engine speed sensor 15 is the engine 1
A pulse (hereinafter referred to as a "TDC signal pulse") at a predetermined crank angle position every time the crankshaft rotates by 180 degrees, and the cylinder discriminating sensor 16 outputs a signal pulse at a predetermined crank angle position of a specific cylinder. These signal pulses are supplied to the ECU 5.

In FIG. 1, the exhaust pipe 21 is
The exhaust pipe 21 is provided with a catalytic converter (three-way catalyst) for purifying components such as HC, CO, and NOx in exhaust gas. ) 23 are arranged. Exhaust pipe 2
On the upstream side of the first catalytic converter 23, a proportional air-fuel ratio sensor 22 (hereinafter referred to as "LAF sensor 22") is mounted. The LAF sensor 22 detects the oxygen concentration in the exhaust gas, and detects the detected value. Output an electrical signal according to EC
Supply to U5.

Next, the exhaust gas recirculation mechanism (EGR) will be described.

An exhaust gas recirculation passage 25 is provided between the intake pipe 2 and the exhaust pipe 21 in a bypass shape. The exhaust gas recirculation path 2
5 is an exhaust pipe 2 whose one end is upstream of the LAF sensor 22.
1 and the other end is connected to the intake pipe 2.

An exhaust gas recirculation amount control valve (hereinafter, referred to as an EGR valve) 26 is interposed in the exhaust gas recirculation passage 25. The EGR valve 26 includes a valve chamber 27 and a diaphragm chamber 28.
A wedge-shaped valve body 30 disposed in the valve chamber 27 and movably arranged in the vertical direction so that the exhaust gas recirculation passage 25 can be opened and closed; and a valve shaft 31.
Diaphragm 32 connected to the valve body 20 through
And a spring 33 for urging the diaphragm 32 in the valve closing direction.
It is composed of In addition, the diaphragm chamber 28
Atmospheric pressure chamber 3 defined on the lower side via diaphragm 32
4 and a negative pressure chamber 35 defined on the upper side.

The atmosphere chamber 34 communicates with the atmosphere via a vent 34a, while the negative pressure chamber 35 is connected to a negative pressure communication passage 36. That is, the negative pressure communication passage 36 is connected to the intake pipe 2.
And the absolute pressure (negative pressure) P in the intake pipe in the intake pipe 2
BA is introduced into the negative pressure chamber 35 via the negative pressure communication passage 36. An atmosphere communication passage 37 is connected in the middle of the negative pressure communication passage 36, and a pressure regulating valve 38 is provided in the middle of the atmosphere communication passage 37. The pressure regulating valve 3
Reference numeral 8 denotes a normally closed solenoid valve, and the atmospheric pressure or the negative pressure is selectively supplied to the negative pressure chamber 35 of the diaphragm chamber 28 through the pressure regulating valve 38, and the negative pressure chamber 35 is controlled by a predetermined control. Generate pressure.

Further, the EGR valve 26 is provided with a valve opening (lift) sensor 39.
9 detects the operating position (valve lift amount LACT) of the valve element 30 of the EGR valve 26, and outputs the detection signal to the ECU 5
To supply. The EGR control is executed after the engine is warmed up (for example, when the engine coolant temperature TW is equal to or higher than a predetermined temperature).

The ECU 5 shapes input signal waveforms from various sensors, corrects a voltage level to a predetermined level, and converts an analog signal value to a digital signal value. The CPU 5b includes a storage unit 5c for storing various calculation programs executed by the CPU 5b, calculation results, and the like, an output circuit 5d for supplying a drive signal to the fuel injection valve 6, and the like.

The CPU 5b estimates an air-fuel ratio for each cylinder of the engine 1 and sets a target air-fuel ratio on the basis of the above-mentioned various engine operating parameters.
According to the estimated cylinder-by-cylinder air-fuel ratio and target air-fuel ratio, the TD
The fuel injection time T of the fuel injection valve 6 in synchronization with the C signal pulse
out is calculated. Since the fuel injection time Tout is proportional to the fuel injection amount by the fuel injection valve 6, it is also referred to as a fuel injection amount in this specification.

The CPU 5b also calculates a target lift amount LCMD of the EGR valve 26 based on various engine operating parameters.
Is calculated, and the pressure regulating valve 38 is controlled such that the detected valve lift amount LACT matches the target lift amount LCMD.

The CPU 5b outputs, via the output circuit 5d, drive signals for the fuel injection valve 6 and the pressure regulating valve 38 based on the results calculated as described above.

In this embodiment, the ECU 5 constitutes the cylinder-by-cylinder air-fuel ratio estimating means, the air-fuel ratio estimating means, the air-fuel ratio controlling means, the first calculating means and the second calculating means.

The estimation of the cylinder-by-cylinder air-fuel ratio by the CPU 5b is performed by using a neural network which inputs various engine operation parameters, and calculates the cylinder-by-cylinder equivalent ratio HATAF by converting the air-fuel ratio into an equivalent ratio. FIG. 2 is a diagram for explaining a schematic structure of the neural network employed in the present embodiment. This neural network has a three-layer structure of an input layer, an intermediate layer, and an output layer, and its learning algorithm is based on a well-known back-propagation (Back-Pro
pagation) Learning algorithm was adopted. Note that the learning algorithm may employ another method such as a random search method.

As shown in FIG. 2, information input to each cell (neuron) in the input layer is an engine operation parameter Xi (i = 1 to n), and the information is weighted by a coupling coefficient matrix. Is input to each cell in the middle layer. In the intermediate layer, the output is determined for each cell by, for example, a sigmoid function, and similarly to the processing from the input layer to the intermediate layer, the output weighted by the coupling coefficient matrix is input to the output layer, and the cylinder equivalent ratio ATAF Is output. Each coefficient of the coupling coefficient matrix is actually installed with an air-fuel ratio sensor at the exhaust port of each cylinder to actually detect the air-fuel ratio, and for each operation parameter value, the detected cylinder-specific air-fuel ratio as teacher data, It is determined by the back propagation learning algorithm so that the total error function is minimized.

In this embodiment, three neural nets having the structure shown in FIG. 2 are provided. Specifically, engine 1
A neural network for starting when the engine is started (during cranking), and a neural network for when the LAF sensor 22 is inactive after the engine 1 is started and the LAF sensor 22 is not activated. A neural network for activation of the LAF sensor, which is used when the LAF sensor 22 is activated after the engine 1 is started, is provided. A coupling coefficient matrix corresponding to each neural network is stored in the storage means of the ECU 5. Have been.

As the engine operation parameters input to the input layer of the neural network for the activation of the LAF sensor, the following are employed in this embodiment with n = 13. Further, as the engine operation parameters input to the input layers of the neural network for the inactive state of the LAF sensor and for the starting time, X1 to X10 excluding the following X11 to X13 are adopted as n = 10 in the present embodiment. .

X1 = Tout (k) (fuel injection time (current value)) X2 = Tout (k-4) (fuel injection time (value before 4 TDC)) X3 = TA (intake temperature) X4 = TW (engine water temperature) X5 = NE (engine speed) X6 = PBA (absolute pressure in intake pipe) X7 = θTH (throttle valve opening) X8 = LACT (EGR valve lift) X9 = KCMD (k) (target air-fuel ratio coefficient (current value) )) X10 = KCMD (k-4) (Target air-fuel ratio coefficient (4TDC
X11 = KACT (k) (Detection equivalent ratio (current value)) X12 = KACT (k-4) (Detection equivalent ratio (value before 4TDC)) X13 = ΔKACT (k) (Detection equivalent ratio change Quantity = KAC
T (k) -KACT (k-4)) Here, the target air-fuel ratio coefficient KCMD is a coefficient by which the basic fuel amount Ti is multiplied by a calculation formula of a fuel injection amount Tout described later, and depends on the engine operating state. The set target air-fuel ratio (A / F) is converted into an equivalent ratio. The detected equivalent ratio KACT is obtained by converting the air-fuel ratio detected by the LAF sensor 22 into an equivalent ratio. Subscript (k),
(k-4) is added to indicate that the value is the current value and the value 4 TDC before (the period before the TDC signal pulse is generated four times), and in this embodiment, the engine 1
Is a four-cylinder, for example, Tout (k) and Tout (k-
4) is the fuel injection time corresponding to one specific cylinder. All parameters for which the subscript (k) is omitted represent the current value.

In the following description, the input parameters are represented by X1 to Xn in order to avoid complexity of notation.

FIG. 3 is a flowchart of a process for calculating the fuel injection amount Tout. This process is executed in synchronization with the generation of the TDC signal pulse.

First, in step S1, the HAT shown in FIG.
By the AF calculation processing, the cylinder-by-cylinder equivalent ratio HATAF is calculated using the neural network.

In step S101 of FIG. 4, each engine operation parameter is input (read), and then normalization processing of the input data is performed (step S102).

Specifically, as shown in FIG. 5, first, a limit check of input data is performed (step S11).
1). That is, each input data is set to the input upper / lower limit values LMTINH, LMTINL preset for each input data.
And if the input data exceeds the input upper limit LMTINH, the input data is set to the input upper limit LMTINH, and the input lower limit LMTIH is set.
If it is lower than NL, a process for setting the input lower limit value LMTINL is performed. For example, the input lower limit value and the input upper limit value of the engine speed NE are respectively set to NELMTNILL, N
Assuming that ELMTINH is satisfied, if NE <NELMTINL, NE = NELMTINL, and NE> NEL
When it is MTINH, NE = NELMTINH. Here, input upper and lower limit values LMTINH, LMTINL
Is set to the maximum and minimum values of the data used in learning to determine the coupling coefficient matrix of the neural network,
Assuming that upper and lower limits that each input data can take when the device is normal are LMTH and LMTL, LMTL ≦ LMTI
It has a relationship of NL <LMTINH ≦ LMTH. That is, in the present embodiment, the value of the input data is limited to a range narrower than the range of values that can be actually taken during operation of the engine.

The reason for this is that the output value for the input data value exceeding the learning range of the neural network, that is, the output value for the input data value exceeding the learning range of the neural network may be significantly different from the desired value. By using in place of the closest value in the above, even if the desired value itself cannot be obtained as the output value of the neural network, it is possible to obtain an output value that does not significantly reduce the control performance. is there. By adopting such a configuration, it is possible to prevent the range of data used in learning from becoming too wide, and to reduce the number of steps required for determining a coupling coefficient matrix of a neural network. In addition, a problem such as a case where a limit process is performed on the output value does not occur, and the final control amount (fuel injection amount) becomes extremely inappropriate while taking advantage of the characteristics of the neural network. No good engine control can be performed.

The input upper and lower limits are set, for example, as follows (indicated by "input lower limit to input upper limit").

X1, X2 (TOUT): 2 msec to 8 msec X3 (TA): −25 ° C. to 50 ° C. X4 (TW): −25 ° C. to 85 ° C. X5 (NE): At start 200 rpm to 500 rpm After start 1000 rpm to 3000 rpm X6 (PBA): 260 mmHg to 560 mmHg X7 (θTH): 0 ° at start (fully closed fixed) 10 ° to 30 ° after start X8 (LACT): LACTMIN (fully closed) to LACTMAX (fully open) X9, X10 (KCMD) : 0.67 to 1.16 (A / F conversion 22 to 12.7) X11, X12 (KACT): (KCMD-0.136) to (KCMD + 0.135) (A / F conversion (A / F (KCMD) ) +2) to (A / F (KCMD) −2) X13 (ΔKACT): −0.034 to 0.034 (A / F conversion 0.5 to −) .5) where the input on the lower limit value of the detected equivalent ratio KACT, based on the target air-fuel ratio coefficient KCMD corresponding point, ± 2A
/ F.

In the following step S112, the input data X
i (i = 1 to n) is applied to the following equation to obtain normalized data N
Xi is calculated.

NXi = (Xi−CXi) / CXi (i = 1 to n) where CXi is the median of the input upper and lower limits.
In the input data, the detected equivalent ratio change amount ΔKA
Since the median value of CT is “0”, this normalization operation is not performed.

As described above, by normalization, all input data is converted into data having a median value of "0". Therefore, the sigmoid function calculation can be executed by providing only one sigmoid function table. It is possible to do.

Returning to FIG. 4, in step S103, FIG.
The calculation of the intermediate layer shown in FIG. That is, in step S121 of FIG. 6, it is determined whether or not a start flag FSTART indicating "1" indicating that the engine is being started is "1".
When FSTART = 1 and at the time of starting, a coupling coefficient matrix for starting is selected (step S125), and FS
If TART = 0 and after starting, it is determined whether or not an activation flag FLAFACT indicating “1” that the LAF sensor 22 is activated is “1” (step S122). And if FLAFACT = 0 and LA
When the F sensor 22 is inactive, a coupling coefficient matrix for when the LAF sensor is inactive is selected (step S124), and the FLAF is selected.
When ACT = 1 and the LAF sensor 22 is active, LA
A coupling coefficient matrix for F sensor activation is selected (step S123). At this time, in steps S123 to S125, not only the first coupling coefficient matrix [aji] used in the operation of the following step S125 but also the second coupling coefficient matrix [bj] used in the operation of the output layer described later are simultaneously performed. select. This selection processing corresponds to processing for switching between three neural nets.

Here, whether or not the LAF sensor 22 has been activated is determined by, for example, the resistance value of the sensor element or the LA value.
This is performed by a known method using a resistance value of a heater for hastening the activation of the F sensor 22.

In the following step S126, the first coupling coefficient matrix [aji] is added to the normalized data NXi obtained by the following equation (1).
(I = 1 to n, j = 1 to m) are multiplied by a matrix operation (step S126) to perform first intermediate variables YA1 to YAm. Here, the number m of the first intermediate variables YAj corresponds to the number of cells in the intermediate layer, and for example, m is about 20. When the number of cells is increased, the accuracy is improved but the amount of calculation is increased. Therefore, the number m of cells is determined in consideration of both.

[0051]

(Equation 1) Next, a sigmoid function operation by table search is performed on the first intermediate variables YA1 to YAm, and the second intermediate variable YB
1 to YBm are calculated (step S122). In this embodiment, Equation 2 is used as the sigmoid function, and the input / output characteristics of this function are as shown in FIG. Since Equation 2 is an odd function symmetrical with respect to the origin, a table for calculating y for the region of x ≧ 0 in FIG. 8 is actually set. For the region of x <0,
By performing a table search with | x | and setting the sign of the search value y to a minus value, a sigmoid function operation is performed.

[0052]

[Mathematical formula-see original document] y = 2 / (1 + exp (-2x))-1 Here, when Expression 2 is represented as y = SGM (x), the operation in step S127 is represented as Expression 3.

[0053]

## EQU3 ## YBj = SGM (YAj) (j = 1 to m) Returning to FIG. 4, in step S104, the operation of the output layer is performed. That is, as shown in FIG. 7, the second intermediate variable YBj
In the second coupling coefficient matrix (row vector) [bj] (j = 1
To m) to calculate a third intermediate variable YC (step S131), and a sigmoid function operation to perform a table search on the third intermediate variable YC and calculate output data Z (step S132). I do. These operations are represented by the following Expressions 4 and 5.

[0054]

(Equation 4)

[0055]

## EQU5 ## In step S105 of FIG. 4, the output data Z is inversely normalized, and the cylinder-by-cylinder equivalent ratio HATAF is calculated. The inverse normalization processing is performed by the following equation (6).

[0056]

HATAF = Z × HATAFC + HATAFC Here, HATAFC is an equivalent ratio of 1. that is equivalent to the stoichiometric air-fuel ratio.
Median value set to zero.

Since the calculations of the above equations 1 to 6 are executed in synchronization with the generation of the TDC signal pulse, the cylinder-by-cylinder equivalent ratios HATAF (k), HATAF (k + 1), and HAT are sequentially output.
AF (k + 2) and HATAF (k + 3) represent equivalent ratios corresponding to each of the four cylinders, and the corresponding cylinder of HATAF (k + 4) is H
Same as ATAF (k).

The coupling coefficient matrices [aji] and [bj] (j = 1 to m) used in Equations 1 and 4 are determined in advance by learning using teacher data as described above. .

As described above, in the present embodiment, the cylinder-by-cylinder equivalent ratio HATAF is calculated by using a neural network. Therefore, compared to the case where the cylinder-by-cylinder equivalent ratio is calculated by using an observer, the cylinder having no delay is used. The estimation of another air-fuel ratio can be performed. That is, an accurate cylinder-by-cylinder air-fuel ratio can be obtained even in a transient state in which the engine operating state changes.

The above is the cylinder-by-cylinder equivalence ratio calculation processing in step S1 in FIG. 3, and in the subsequent step S2 in FIG.
It is determined whether or not the start flag FSTART is "1", and FS is determined.
If TART = 0 and it is not at the time of starting, it is determined whether or not the activation flag FLAFACT is "1" (step S3). When FSTART = 1 and the engine is started, or when FLAFACT = 0 and the LAF sensor 22 is not activated, the process proceeds to step S4.

In step S4, time series data HATAF (k), HATAF (k + 4), H
A high-pass filter process is performed on ATAF (k + 8), HATAF (k + 12),. Specifically, the cylinder-by-cylinder equivalent ratio change amount ΔA / F is calculated by, for example, the following equation.

ΔA / F = HATAF (k) −HATAF (k−4) The high-pass filter processing is not limited to this. For example, a well-known averaging processing or smoothing processing is performed on the time-series data. Then, a process of subtracting the data after the averaging or the averaging process from the original data may be performed.

In the following step S5, a limit process for the cylinder-by-cylinder equivalent ratio change amount ΔA / F is performed. Specifically, for example, the cylinder-by-cylinder equivalent ratio change amount ΔA / F is equal to the change amount upper limit value ΔA / FL.
MTH (for example, 0.034 (A / F conversion -0.5))
Is exceeded, ΔA / F = ΔA / FLMTH, and the variation lower limit value ΔA / FLMTL (for example, −0.034 (A
/ F conversion 0.5)), ΔA / F = Δ
A / FLMTL.

Next, the fuel injection amount To is calculated by the following equation (7).
ut is calculated (step S6).

[0065]

## EQU7 ## Tout = Ti × KCMD + KP1 × ΔA / F
+ TIVB Here, Ti is a basic fuel amount set according to the engine speed NE and the intake pipe absolute pressure PBA.
D is the aforementioned target air-fuel ratio coefficient, KP1 is a proportional gain set to, for example, a negative predetermined value (for example, -0.05), TI
VB is a correction term that is set according to the output voltage of a battery that supplies drive power to the fuel injection valve 6.

The calculation based on the equation 7 is also equivalent to the cylinder equivalent ratio HAT.
Since it is executed in synchronization with the generation of the TDC signal pulse similarly to AF, the sequentially output fuel injection amounts Tout (k), Tout (k)
out (k + 1), Tout (k + 2), and Tout (k + 3) represent the fuel injection amount corresponding to each of the four cylinders, and Tout (k
The corresponding cylinder of +4) is the same as Tout (k).

Since the processing of steps S4 to S6 (corresponding to the first calculating means described in the claims) does not use the detected equivalent ratio KACT as the input parameter of the neural network before the activation of the LAF sensor 22, Considering that the reliability of the estimated absolute value of the cylinder-specific equivalence ratio HATAF becomes low due to the individual difference of the engine or the effect of purging the fuel vapor into the intake pipe, the variation ΔA of the cylinder-specific equivalence ratio HATAF is considered.
The feedback control according to only / F is performed.

On the other hand, in step S3, FLAFACT =
When the LAF sensor 22 is activated, the cylinder-by-cylinder air-fuel ratio correction coefficient KAF # N (N = 1 to 4) is calculated by PID control according to the cylinder-by-cylinder equivalent ratio HATAF (step S7). ). Specifically, the following formula 8
Is applied to the deviation DKAF (= KCMD-HATAF) between the target equivalent ratio KCMD and the cylinder equivalent ratio HATAF.
A proportional term KAFP, an integral term KAFI, and a derivative term KAFD are calculated, and a cylinder-specific correction coefficient KAF # N is calculated by Expression 9.

[0069]

KAFP (k) = DKAF (k) × KP2 KAFI (k) = DKAF (k) × KI2 + KAFI (k−4) KAFD (k) = (DKAF (k) −DKAF (k−4)) × KD
2 Here, KP2, KI2, and KD2 are a proportional gain, an integral gain, and a derivative gain, respectively.

[0070]

KAF # N = KAFP (k) + KAFI (k) + KAFD (k) In the next step S8, the fuel injection amount Tout is calculated by the following equation (10).

[0071]

Tout = Ti × KCMD × KAF # N + TIVB The processing in steps S7 and S8 (corresponding to the second calculating means described in the claims) is performed after the activation of the LAF sensor 22 and the detected equivalent ratio KACT. Since the cylinder-by-cylinder equivalence ratio HATAF is calculated by using a neural network having the input parameter as a parameter, feedback control is performed by ordinary cylinder-by-cylinder PID control in consideration of the high reliability of the absolute value of the HATAF value. It was done.

The fuel injection amount To is calculated by using equation (7) or (10).
By calculating ut, it is possible to perform control so that the air-fuel ratio of each cylinder matches the air-fuel ratio corresponding to the target air-fuel ratio coefficient KCMD according to the cylinder equivalent ratio HATAF calculated using the neural network, It is possible to perform air-fuel ratio feedback control that is superior in responsiveness and stability as compared with control using a conventional observer.

Also, three neural nets for calculating the cylinder-by-cylinder equivalence ratio HATAF are provided for starting, when the LAF sensor is inactive, and when the LAF sensor is active, and the operating state of the engine or the active state of the LAF sensor is provided. In this case, an appropriate cylinder-by-cylinder equivalent ratio can be obtained in each state. Then, since the fuel injection amount Tout is controlled according to the cylinder-by-cylinder equivalence ratio HATAF thus obtained, more accurate air-fuel ratio control can be performed over a wide range of the engine operating state.

When the LAF sensor is inactive, feedback control is performed in accordance with the variation ΔA / F of the cylinder equivalent ratio HATAF. When the LAF sensor is active, the cylinder equivalent ratio H is increased.
Since the normal cylinder-by-cylinder feedback control according to the deviation DKAF between the ATAF and the target air-fuel ratio coefficient KCMD is performed, appropriate cylinder-by-cylinder air-fuel ratio control is performed according to the reliability of the absolute value of the cylinder equivalent ratio HATAF. be able to.

The present invention is not limited to the embodiment described above, and various modifications are possible. For example,
Regarding the neural network for starting and the time when the LAF sensor is inactive, the input data may be the following operating parameters.

X1 = Tout (k) (fuel injection time (current value)) X2 = Tout (k-4) (fuel injection time (value before 4 TDC)) X3 = Tout (k-12) (fuel injection time ( X4 = TW (engine water temperature) X5 = NE (engine speed) X6 = PBA (intake pipe absolute pressure) X7 = TCOUNT (T from engine start time)
In the above-described embodiment, the plurality of neural nets are used for starting, when the LAF sensor is inactive, and when the LAF sensor is active. However, after the engine is started, the engine water temperature TW is changed. Depending on whether the temperature is higher or lower than a predetermined temperature (for example, 30 ° C.), the neural network for high temperature and the neural network for low temperature may be switched and used. Neural networks for the activation and inactivation of the LAF sensor (a total of four neural nets) may be provided when the engine water temperature TW is high and low, respectively. Further, a plurality of operating regions defined by the engine speed NE and the intake pipe absolute pressure PBA may be set, and a neural network corresponding to each of the plurality of operating regions may be provided.

Further, the cylinder-by-cylinder equivalent ratio HATAF (k), HA
TAF (k + 1), HATAF (k + 2), HATAF (k + 3), H
The time series data of ATAF (k + 4),... Are appropriately weighted and averaged to calculate an assembly equivalent ratio HATAFMIX in the exhaust pipe assembly, and the fuel injection is performed in accordance with the assembly equivalent ratio HATAFMIX. The quantity Tout may be calculated. Alternatively, the neural network itself may be configured to output the aggregate equivalent ratio HATAFMIX.

In the above-described embodiment, the process of limiting the input parameter value of the neural network is performed by controlling the ignition timing of the engine, controlling the opening amount of the EGR valve, or controlling the intake valve by using the neural network inputting the engine operating parameters. Alternatively, the present invention can be applied to control of the valve timing of an exhaust valve.

[0079]

As described in detail above, according to the first aspect of the present invention, a neural network which inputs an engine operating parameter including at least a detection value of an air-fuel ratio sensor provided in an exhaust pipe collecting section is provided. In addition, the air-fuel ratio of each cylinder of the plurality of cylinders is estimated, and the air-fuel ratio of the air-fuel mixture supplied to the engine is feedback-controlled in accordance with the estimated air-fuel ratio of each cylinder. In this case, the air-fuel ratio for each cylinder can be accurately estimated, and the control accuracy of the air-fuel ratio for each cylinder can be improved.

According to the second aspect of the present invention, a plurality of neural nets which input the operating parameters of the engine are set, and the plurality of neural nets are set according to the operating state of the engine or the activation state of the air-fuel ratio sensor. By switching and using, the air-fuel ratio of the air-fuel mixture supplied to the engine is estimated, and the air-fuel ratio of the air-fuel mixture supplied to the engine is controlled according to the estimated air-fuel ratio. It is possible to perform more accurate air-fuel ratio control over a wide range of the engine operating state including the operating state where the value cannot be used.

According to the fourth aspect of the present invention, the air-fuel ratio control amount is calculated by selecting one of the first calculating means and the second calculating means in accordance with the activation state of the air-fuel ratio sensor. Since the air-fuel ratio control is performed using the calculated air-fuel ratio control amount, it is possible to perform appropriate air-fuel ratio feedback control according to the reliability based on the estimated absolute value of the air-fuel ratio.

[Brief description of the drawings]

FIG. 1 is a diagram showing a configuration of an internal combustion engine and a control device thereof according to an embodiment of the present invention.

FIG. 2 is a diagram showing a configuration of a neural network.

FIG. 3 is a flowchart of a fuel injection amount calculation process.

FIG. 4 is a flowchart of a process for calculating a cylinder equivalent ratio.

FIG. 5 is a flowchart of processing for normalizing input data.

FIG. 6 is a flowchart of a process corresponding to an operation in a hidden layer of the neural network.

FIG. 7 is a flowchart of a process corresponding to a calculation in an output layer of the neural network.

FIG. 8 is a diagram for explaining a sigmoid function.

[Explanation of symbols]

Reference Signs List 1 internal combustion engine 2 intake pipe 4 throttle valve opening sensor 5 electronic control unit (cylinder-specific air-fuel ratio estimating means, air-fuel ratio controlling means, air-fuel ratio estimating means, first calculating means,
6 second fuel injection valve 12 intake pipe absolute pressure sensor 13 intake temperature sensor 14 engine water temperature sensor 15 engine speed sensor 21 exhaust pipe 22 air-fuel ratio sensor

Claims (4)

[Claims] 1. An internal combustion engine having a plurality of cylinders, comprising: an air-fuel ratio sensor provided at a collection portion of an exhaust pipe connected to each of the plurality of cylinders, wherein the engine uses values detected by the air-fuel ratio sensor. An air-fuel ratio control device for an internal combustion engine that controls an air-fuel ratio of an air-fuel mixture supplied to a plurality of cylinders of at least one of the plurality of cylinders by using a neural network that inputs an engine operating parameter including at least a detection value of the air-fuel ratio sensor. Cylinder-by-cylinder air-fuel ratio estimating means for estimating the air-fuel ratio; air-fuel ratio controlling means for feedback-controlling the air-fuel ratio of the air-fuel mixture supplied to the engine according to the cylinder-by-cylinder air-fuel ratio estimated by the cylinder-by-cylinder air-fuel ratio estimating means. An air-fuel ratio control device for an internal combustion engine, comprising: 2. An air-fuel ratio control device for an internal combustion engine, comprising: an air-fuel ratio sensor provided in an exhaust pipe of the internal combustion engine, wherein the air-fuel ratio of an air-fuel mixture supplied to the engine is controlled using a detection value of the air-fuel ratio sensor. In the engine, by setting a plurality of neural nets inputting the operating parameters of the engine, and switching and using the plurality of neural nets according to the operating state of the engine or the activation state of the air-fuel ratio sensor, Air-fuel ratio estimating means for estimating the air-fuel ratio of the air-fuel mixture supplied to the engine; air-fuel ratio controlling means for controlling the air-fuel ratio of the air-fuel mixture supplied to the engine according to the air-fuel ratio estimated by the air-fuel ratio estimating means; An air-fuel ratio control device for an internal combustion engine, comprising: 3. The air-fuel ratio sensor according to claim 2, wherein the plurality of neural nets include a neural net that does not receive a detection value of the air-fuel ratio sensor. The air-fuel ratio control device for an internal combustion engine according to claim 2, wherein the air-fuel ratio is estimated using a neural network that does not receive a detected value. 4. An air-fuel ratio control means for calculating an air-fuel ratio control amount to be applied when the air-fuel ratio sensor is inactive, and an air-fuel ratio control amount to be applied when the air-fuel ratio sensor is active. A second calculating means for selecting one of the first calculating means and the second calculating means to calculate an air-fuel ratio control amount in accordance with an activation state of the air-fuel ratio sensor. 4. The air-fuel ratio control device for an internal combustion engine according to claim 3, wherein the air-fuel ratio control is performed using a control amount.