JPH06248995A - Driving force control device for vehicle - Google Patents

Abstract

PURPOSE:To ensure excellent driving and operation performance even during turning by a method wherein a drive force is controlled to valve constantly matching with the characteristics of a driver regardless of a conscious state and operation environment and the control is continuously performed throughout the whole operation range of an accelerator pedal. CONSTITUTION:In neurocomputer 22, acceleration G is set as teacher data, and a relation of the acceleration G with an accelerator stroke S and a car speed V is learnt as a demand acceleration model. Otherwise, a deviation between the demand acceleration model output and the acceleration G produces an error signal and a deviation between the demand acceleration model output and the acceleration G produces an error signal and a relation of a throttle opening Th with the accelerator stroke S and the car speed V is learnt as a throttle sensitivity model. Meanwhile, during cornering, variation and setting to throttle sensitivity suitable for cornering are effected. In a throttle computer 21, an engine 2 output is controlled through opening and closing of a throttle valve 9 according to an accelerator stroke by means of a throttle sensitivity model output or throttle sensitivity for cornering serving as reference data.

Description

Detailed Description of the Invention

[0001]

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to a driving force control device for a vehicle which controls the driving force so that the acceleration of the vehicle becomes the acceleration required by the driver.

[0002]

2. Description of the Related Art Generally, a vehicle is required to run under various environmental conditions, and various driving operations are performed by individual drivers. As for the behavior of the vehicle, the responsiveness and smoothness intended by the driver are required. Conventionally, regarding the behavior related to the driving force of the vehicle,
For example, in a vehicle equipped with an internal combustion engine, it is known that control is performed according to the amount of depression of an accelerator pedal by a driver.

For example, in Japanese Patent Application No. 3-80103 previously proposed by the present applicant, in a vehicle equipped with an internal combustion engine, the opening of a linkless type throttle valve (throttle opening) depends on the driver. It is designed to be controlled according to the amount of depression of the accelerator pedal (accelerator stroke). Here, the data for determining the target acceleration corresponding to the accelerator stroke is stored in advance in the backup RAM as a map. Then, the throttle opening is controlled so that the actual acceleration of the vehicle becomes the target acceleration determined from the map, and thus the driving force of the vehicle is controlled. Further, in this proposed technique, the change in accelerator stroke and the actual acceleration are detected as a change in the acceleration request degree of the driver, and the deviation between the detected acceleration request degree and the target acceleration determined from the above map. The data of the map is corrected so as to be minimized and stored again in the backup RAM. Mathematically, correction (correction) regarding target acceleration
Is performed according to the above deviation to rewrite the map. That is, the data of the target acceleration corresponding to the accelerator stroke is learned.

Therefore, since the target acceleration data is learned so as to match the degree of acceleration demand of the driver as described above, the target acceleration that always matches the characteristics of the driver is determined. . As a result, regardless of the driver's consciousness and driving environment, a driving force suitable for the driver's characteristics can always be obtained, and good driving performance can be obtained.

However, in the above-mentioned proposed technique, in order to learn the data of the target acceleration, the data is simply corrected (corrected) and the map is rewritten. When rewriting the map, the target acceleration data is only learned for a certain point or a certain range of the accelerator stroke.
Therefore, the target acceleration is only corrected (corrected) only in a specific driving region, and the rewritten map is locally biased. As a result, in the rewritten map, the relationship between the target acceleration and the accelerator stroke is partially discontinuous, and the control of the driving force of the vehicle tends to be partially discontinuous with respect to the change of the accelerator stroke. there were.

Therefore, a new technique has been proposed by the applicant of the present application in Japanese Patent Application No. 4-336 aiming at addressing the above-mentioned problems.
No. 323 was proposed. In this proposed technique, map learning control is performed using a neural network technique. Here, the driver's demand for the running of the vehicle is estimated as a "requested acceleration model" from the actual acceleration at that time, and the "requested acceleration model" is calculated.
Based on the above, the "throttle sensitivity model" is changed and the throttle sensitivity is determined. Further, the throttle valve of the engine is controlled to be opened and closed so that the target throttle opening obtained from the product of the determined throttle sensitivity and the accelerator stroke and the actual throttle opening match.

Therefore, since the learning control of the map is performed by using the neural network technology as described above, the entire relation between the acceleration and the throttle opening with respect to the entire range of the accelerator stroke is not discontinuous, and the engine is driven. Force control could now be continuous over the entire range of the driver's accelerator stroke.

[0008]

However, in the latter newly proposed technique, the throttle sensitivity is determined by executing learning control based on the acceleration of the vehicle. That is, the throttle sensitivity is determined by the way the vehicle is running during acceleration. Therefore, a certain high throttle sensitivity is determined when the vehicle is accelerating, and after the vehicle accelerates into cornering, U-turn, or other turning, the throttle valve is controlled to open and close with high throttle sensitivity for a while. There is a case. As a result, when the driver's intention is reflected in the control of the driving force of the vehicle, the accelerator controllability may be poor. For example, immediately after the vehicle is suddenly started, the throttle sensitivity is increased, and the changes in vehicle speed and longitudinal acceleration with respect to the accelerator stroke are large. Therefore, in the cornering after the sudden start, there is a possibility that it may not be possible to secure good driving and driving performance against the driver's intention. On the contrary, immediately after the vehicle is gently started, the throttle sensitivity is lowered, and the changes in the vehicle speed and the longitudinal acceleration with respect to the accelerator stroke are small.
Therefore, if the driver tries to make a quick U-turn after a gentle start of the vehicle or quickly exits a winding road with many corners, the acceleration response becomes undesirably slow against the driver's intention. There was a risk that the operation and driving performance would not be secured sufficiently.

The present invention has been made in view of the above-mentioned circumstances, and an object thereof is to realize control of a driving force which always matches the characteristics of a driver regardless of the driver's consciousness and driving environment. It is possible to control the driving force continuously over the entire range of the operation amount of the accelerator pedal etc. by the driver, and at the same time, it is possible to secure good maneuvering / driving performance during turning. An object is to provide a driving force control device for a vehicle.

[0010]

In order to achieve the above object, in the present invention, as shown in FIG.
Control amount changing means M3 for changing the control amount of the drive source M2 mounted on the drive unit 1, output operation means M4 operated by the driver to arbitrarily control the output of the drive source M2,
An operation amount detecting means M5 for detecting the operation amount of the output operation means M4 is provided, and the output of the drive source M2 is controlled by driving the control amount changing means M3 according to the detection result of the operation amount detecting means M5. In the vehicle driving force control device configured to control the driving force of the vehicle M1, the acceleration detecting means M6 for detecting the acceleration of the vehicle M1, and the vehicle M
The velocity detection means M7 for detecting the velocity of 1 and the acceleration obtained by the detection of the acceleration detection means M6 are used as teacher data to be compared, and the deviation between the teacher data and the output of the means M8 is used as an error signal. The operation amount of the output operation means M4 and the speed detection means M7 so that the error amount becomes small.
Required acceleration model learning means M8 for learning the relationship of the acceleration of the vehicle M1 with respect to the speed obtained by the detection as the acceleration model required by the driver, and the acceleration obtained by the detection of the acceleration detection means M6 and the required acceleration model learning. The deviation from the output of the acceleration model learned by the means M8 is used as an error signal, and the drive source M2 with respect to the speed obtained by the operation amount of the output operation means M4 and the speed detection means M7 is detected so that the error becomes small. Control quantity sensitivity model learning means M9 for learning the relationship of the control quantities as a control quantity sensitivity model, and the output of the control quantity sensitivity model learned by the control quantity sensitivity model learning means M9 as reference data, and the reference data thereof. The drive of the control amount changing means M3 is controlled according to the operation amount detected by the operation amount detecting means M5 based on And drive control means M10, vehicle M1
State detecting means M11 for detecting the turning state of the vehicle
And when it is determined that the vehicle M1 is in the turning state based on the detection result of the turning state detecting means M11, the drive control means M1
Reference for changing reference data used for controlling the drive of the control amount changing means M3 at 0 to reference data separately set as a value suitable for turning instead of the output of the control amount sensitivity model. The purpose is to include the data changing means M12.

[0011]

According to the above structure, as shown in FIG. 1, the acceleration obtained by the detection of the acceleration detecting means M6 is used as the teacher data to be compared in the required acceleration model learning means M8. That is, the actual acceleration of the vehicle M1 is used for comparison as the acceleration requested by the driver.
Then, in the required acceleration model learning means M8, the deviation between the teacher data and the output of the means M8 becomes small, that is, the deviation between the acceleration requested by the driver and the output of the requested acceleration model becomes small. , Output operation means M4
The relationship between the operation amount and the acceleration of the vehicle M1 with respect to the speed obtained by the detection of the speed detection means M7 is learned as an acceleration model required by the driver. In the control amount sensitivity model learning means M9, the deviation between the acceleration similarly obtained by the detection of the acceleration detecting means M6 and the output of the acceleration model learned as described above is used as an error signal.
Then, in the control amount sensitivity model learning means M9, the operation of the output operation means M4 is performed so that the error component of the error signal is reduced, that is, the deviation between the acceleration of the vehicle M1 and the output of the acceleration model is reduced. The relationship between the amount and the control amount of the drive source M2 with respect to the speed of the vehicle M1 is learned as a control amount sensitivity model. Then, in the drive control means M10, the output of the control amount sensitivity model learned as described above is used as reference data, and based on the reference data, control is performed according to the operation amount of the output operation means M4 by the driver's operation. The drive of the quantity changing means M3 is controlled. As a result, the output of the drive source M2 is controlled, and thus the driving force of the vehicle M1 is controlled.

Therefore, according to the present invention, the acceleration model that always meets the driver's request can be obtained, and the control amount sensitivity model can be obtained corresponding to the acceleration model. Then, the control amount of the drive source M2 is controlled with an acceleration that always meets the driver's request. Further, according to the present invention, the entire relation between the operation amount of the output operation means M4 and the acceleration of the vehicle M1 with respect to the speed of the vehicle M1 is learned as a continuous model,
The entire relationship between the operation amount of the output operation means M4 and the control amount of the drive source M2 with respect to the speed of the vehicle M1 is learned as a continuous model. Therefore, in the entire range of the speed of the vehicle M1, the relationship between the acceleration and the control amount with respect to the entire range of the operation amount of the output operation means M4 does not become partially discontinuous.

On the other hand, the turning state detecting means M11 detects the turning state of the vehicle M1. Then, in the reference data changing means M12, the vehicle M1 is detected based on the detection result of the turning state.
When it is determined that the turning state is, the reference data used by the drive control unit M10 to control the drive of the control amount changing unit M3 is changed by the reference data changing unit M12. That is, instead of the output of the control amount sensitivity model, the reference data set separately as a value suitable for turning is changed.

Therefore, when the vehicle M1 shifts from straight forward acceleration to turning, instead of the output of the control amount sensitivity model learned during acceleration, the output operating means M4 is used based on reference data suitable for turning. The drive of the control amount changing means M3 is controlled according to the operation amount of. Therefore, when the vehicle M1 turns, the operation of the output operation means M4 required for the output control of the drive source M2 becomes suitable for the driver to turn.

[0015]

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS An embodiment of the vehicle driving force control device according to the present invention will be described in detail below with reference to FIGS.

FIG. 2 is a schematic diagram showing the vehicle driving force control system in this embodiment. A vehicle 1 is equipped with a gasoline engine (hereinafter simply referred to as “engine”) 2 as a drive source. The engine 2 is of a multi-cylinder in-line type. Outside air is taken into the intake passage 3 of the engine 2 and fuel injected from an injector (not shown) is supplied. Then, a mixture of outside air and fuel is taken into each combustion chamber (not shown) of the engine 2 through the intake passage 3. Further, the air-fuel mixture taken into each combustion chamber is exploded and burned by the operation of each spark plug (not shown), whereby the piston (not shown), the crankshaft, etc. are operated and the output of the engine 2 is obtained. Further, the burnt gas after being burned in each combustion chamber of the engine 2 is discharged to the outside through the exhaust passage 4.

In this embodiment, the vehicle 1 is of a front engine / rear drive system (FR system), and the crankshaft of the engine 2 is provided with a transmission, a propeller shaft, a differential gear, a drive shaft, etc., which are not shown. It is drivingly connected to a pair of left and right rear wheels 5, which are driving wheels. Further, the pair of left and right front wheels 6 that are driven wheels are steering wheels that are interlocked with the operation of the steering wheel 7 provided in the driver's seat. The steering wheel 7 is provided with a steering angle sensor 8 as a turning state detecting means for detecting the steering angle θS.

In this embodiment, a linkless type throttle valve 9 constituting a control amount changing means is provided in the middle of the intake passage 3. That is, the throttle valve 9 is connected to the DC motor 10 provided in the vicinity thereof. Then, by operating the DC motor 10, the opening degree of the throttle valve 9 as the control amount of the engine 2,
That is, the throttle opening Th is controlled. As a result, the amount of air taken into each combustion chamber of the engine 2 through the intake passage 3 is adjusted, and the output of the engine 2 is controlled by adjusting this amount of air.

A throttle sensor 11 is provided near the throttle valve 9. The throttle sensor 11 detects the throttle opening Th and outputs a signal corresponding thereto. The driver's seat of the vehicle 1 is provided with an accelerator pedal 12 as an output operating means.
The accelerator pedal 12 is operated by the driver DR in order to arbitrarily control the output of the engine 2. Further, an accelerator sensor 13 as an operation amount detecting means is provided near the accelerator pedal 12. The accelerator sensor 13 detects the operation amount of the accelerator pedal 12, that is, the accelerator stroke S, and outputs a signal corresponding to the detected amount. Further, a well-known acceleration sensor 14 which constitutes an acceleration detecting means is provided at substantially the center of the vehicle 1. The acceleration sensor 14 detects an acceleration G in the front-rear direction of the vehicle 1 and outputs a signal corresponding thereto. In addition, the front wheel 6 is provided with a known vehicle speed sensor 15 which constitutes a speed detecting means. This vehicle speed sensor 1
In 5, the speed of the vehicle 1, that is, the vehicle speed V is detected according to the number of rotations of the front wheels 6, and a signal corresponding thereto is output.

Further, in this embodiment, a throttle computer 21 and a neuro computer 22 are provided in order to suitably control the opening / closing of the throttle valve 9 in response to the request of the driver DR. The throttle computer 21 constitutes drive control means, and the DC motor 10 and the throttle sensor 11 are electrically connected to the throttle computer 21, respectively. Further, the neuro computer 22 constitutes a required acceleration model learning means, a control amount sensitivity model learning means, a turning state detecting means and a reference data changing means, and is constituted by applying a neural network technique. A steering angle sensor 8, an accelerator sensor 13, an acceleration sensor 14, and a vehicle speed sensor 15 are electrically connected to the neuro computer 22. Further, the neuro computer 22 and the throttle computer 21 are electrically connected to each other.

FIG. 3 is a block diagram showing the electrical configuration of the throttle computer 21 and the neuro computer 22. The neuro computer 22 includes a central processing unit (CPU) 23 also having a counter function, a read-only memory (ROM) 24 in which a predetermined learning control program and the like are stored in advance, and a random access memory (temporarily storing operation results of the CPU 23 and the like). RAM) 25, a backup RAM 26 for storing previously stored data, and the like. The neuro computer 22 is configured as a logical operation circuit in which the respective units 23 to 26, the external input / output circuit 27 and the like are connected by a bus 28. The steering angle sensor 8, the accelerator sensor 13, and the vehicle speed sensor 15 described above are connected to the external input / output circuit 27, respectively. Further, the acceleration sensor 14 is connected to the external input / output circuit 27 via a low-pass filter 29. The low-pass filter 29 freely passes a signal having a frequency lower than a predetermined cutoff frequency serving as a reference among detection signals of the acceleration sensor 14, and gives a large attenuation to a high frequency. In addition, the above-mentioned throttle computer 21 is connected to the external input / output circuit 27. Further, the ROM 24 stores in advance a learning control program and the like using the neural network technology.

The CPU 23 uses the external input / output circuit 2
Various signals from the respective sensors 8 and 13 to 15 input via 7 and the like are read as input values. Based on these input values, the CPU 23 follows the learning control program stored in the ROM 24 and learns the "requested acceleration model" requested by the driver DR and the "throttle sensitivity model" as a control amount sensitivity model corresponding to the "requested acceleration model". To execute. Then, the CPU 23 outputs the learning result to the throttle computer 21 via the external input / output circuit 27. Alternatively, when it is determined that the vehicle 1 is in a turning state, the CPU 23 releases the learning control and, instead of the above learning result, externally inputs a preset value separately set as a value suitable for turning. Output to the throttle computer 21 via the output circuit 27.

On the other hand, the throttle computer 21 basically has the same structure as the neuro computer 22, and is composed of a CPU 30, a ROM 31, a RAM 32, a backup RAM 33, an external input / output circuit 34, a bus 35 and the like. The aforementioned DC motor 10, throttle sensor 11 and neuro computer 22 are connected to the external input / output circuit 34, respectively. Also, RO
In M31, a throttle opening control program for controlling opening / closing of the throttle valve 9 based on the learning result of the neuro computer 22 or a separately set value is stored in advance.

Then, the CPU 30 reads the learning result data input from the neurocomputer 22 via the external input / output circuit 34 as an input value. Also, CPU3
0 reads the signal from the throttle sensor 11 as an input value. Also, the CPU 30 uses the input values to
The DC motor 10 is preferably controlled according to the throttle opening control program stored in the ROM 31.

Here, the conceptual configuration of the neural network technology applied to the neuro computer 22 will be described with reference to FIGS. 4 (a) and 4 (b). The neural network in this embodiment is shown in FIGS.
As shown in (2), it has two multilayer neural networks. Each multilayer neural network basically has the same configuration, and has an “input layer” composed of two neurons n1, an “intermediate layer” composed of 2 to 10 neurons n2, and one neuron. and an "output layer" composed of n3. In addition, each neuron n between each layer
1, n2 and n3 are connected by synapse sp.
In each multilayer neural network, signals flow in one direction from the “input layer” to the “intermediate layer” and from the “intermediate layer” to the “output layer”. Neurons n1, n2 of each layer
At n3, the state is determined based on the signal received from the previous layer and the signal is transmitted to the next layer. The output result of each multilayer neural network is the "output layer".
It is obtained as the state value of the neuron n3.

Here, in the multilayer neural network shown in FIG. 4A, each neuron n1 in the "input layer" has
The accelerator stroke S detected by the accelerator sensor 13 and the vehicle speed V detected by the vehicle speed sensor 15 are input. Further, the output obtained from the neuron n3 in the “output layer”, that is, the required acceleration model output Gx, is compared with the “teacher data” by using the acceleration G of the vehicle 1 obtained by the detection of the acceleration sensor 14 as “teacher data”. It Then, the acceleration deviation ΔG (= G−G
x) is an “error signal”, and the “weight coefficient” of the synapse sp of all the neurons n1, n2, n3 is corrected in the direction in which the error amount becomes smaller. That is, the acceleration G of the vehicle 1
Is the acceleration required by the driver DR, and the acceleration G is the “teacher data” to be compared. Then, the relationship between the accelerator stroke S and the acceleration G with respect to the vehicle speed V is learned as a “requested acceleration model” required by the driver DR so that the deviation from the “teacher data” becomes small. The output result of this multilayer neural network is obtained as the required acceleration model output Gx. That is, the "requested acceleration model" is learned as a characteristic shown in FIG. 5 in the direction in which the requested acceleration model output Gx approaches the acceleration G.

On the other hand, in the multi-layered neural network shown in FIG. 4B, the accelerator stroke S and the vehicle speed V are input to each neuron n1 in the "input layer", as described above. Further, the output result obtained from the neuron n3 in the "output layer", that is, the throttle sensitivity model output Thx is
Learning is performed by using the acceleration deviation ΔG between the acceleration G and the required acceleration model output Gx as an “error signal”, and the “weighting coefficient” of the synapse sp of all the neurons n1, n2, n3 is corrected. That is, the acceleration deviation ΔG is used as an “error signal”, and the relationship between the throttle opening Th and the accelerator stroke S and the vehicle speed V is learned as a “throttle sensitivity model” required by the driver DR so that the error amount becomes small. It The output result of this multilayer neural network is obtained as the throttle sensitivity model output Thx. That is, the "throttle sensitivity model" is learned as a characteristic as shown in FIG.

The conceptual structure of the neural network as described above is merely for convenience, and the substance of the neural network is the learning control program stored in the ROM 24 of the neurocomputer 22 in advance. The neural network is based on a mathematical operation in the learning control program, and a generally known "error back propagation learning algorithm" is applied as a learning method.
In this embodiment, a learning control program is created in order to finally obtain the characteristic of the relationship of the throttle sensitivity Thg with respect to the accelerator stroke S as shown in FIG.

Next, the processing operation for learning the "requested acceleration model" and "throttle sensitivity model" executed by the neuro computer 22 using the above-mentioned neural network technique will be described.
FIG. 8 is a flowchart showing a “learning control routine” of the learning control program executed by the neuro computer 22. After the processing of this routine is started, it is periodically executed with a fixed cycle, for example, a time interval of "0.1 seconds".

When the processing of this routine is started, in step 101, based on various signals from the steering angle sensor 8, accelerator sensor 13, acceleration sensor 14 and vehicle speed sensor 15, the steering angle θS, accelerator stroke S, vehicle speed V And acceleration G are read respectively.

Next, at step 102, the accelerator stroke change amount ΔS is calculated from the accelerator stroke S. Here, the accelerator stroke change amount ΔS is obtained by the difference between the read values of the accelerator stroke S between the present time and the previous time.

Next, at step 103, it is judged whether or not the learning start for obtaining the throttle sensitivity Thg is started, that is, whether or not the learning start condition is satisfied. In this embodiment, when the absolute value of the accelerator stroke change ΔS and the vehicle speed V are larger than arbitrary set values, it is determined that the learning is started. If learning has started, the process proceeds to step 104, the learning start flag F is set to "1", and then the process proceeds to step 105. If learning is not started, step 1 is performed as it is.
Move to 05.

In step 105, it is determined whether or not learning is continuing. In this embodiment, the learning start flag F is "1", the count value T1 from the start of learning, which will be described later, is smaller than an arbitrary predetermined value, and the vehicle speed V and the accelerator stroke S are arbitrary set values. If it is larger than the above, it is determined that the learning is continuing. Then, if the learning is not continued, it is determined that the learning is not performed, and the process proceeds to step 113. If the learning is being continued, the learning is performed and the process proceeds to step 106.

In step 106, the count value T1 by the counter is incremented by "1". Next, in step 107, the "throttle sensitivity model" is executed. That is, the throttle sensitivity model output Thx is obtained from the characteristics of the already learned "throttle sensitivity model" (see FIG. 6) using the accelerator stroke S and the vehicle speed V read this time as input values. Then, in step 108, the throttle sensitivity Thg is calculated from the throttle sensitivity model output Thx according to the following calculation formula (1).
To decide.

Thg = α + Thx * K (1) Here, “α” is a reference value, and in this embodiment, “α =
It is 1.0 ". Also, "K" is a positive constant.

Then, in step 109, the throttle sensitivity Thg and the accelerator stroke S which are determined this time are set.
And are output to the throttle computer 21. Alternatively,
The product of throttle sensitivity Thg and accelerator stroke S,
That is, the target throttle opening Thg · S is obtained, and the target throttle opening Thg · S is set to the throttle computer 21.
Output to.

Next, in step 110, learning of the "requested acceleration model" requested by the driver DR is executed using the acceleration G of the vehicle 1 as "teacher data". That is, the acceleration G of the vehicle 1 detected by the acceleration sensor 14 is used as "teacher data" to be compared, and the relationship between the acceleration G and the acceleration G with respect to the accelerator stroke S and the vehicle speed V is set so that the deviation from the "teacher data" becomes small. , As a “requested acceleration model” requested by the driver DR.

For example, it is assumed that the curve shown by the solid line in FIG. 5 is the characteristic of the current "requested acceleration model". Then, in order for the driver DR to drive faster than at present, the accelerator pedal 12
It is assumed that the acceleration G of the vehicle 1 has become larger than the current required acceleration model output Gx by operating. The acceleration G at this time is the new required acceleration,
Is updated to a characteristic like the curve shown by the broken line in FIG. That is, the entire relationship of the required acceleration model output Gx with respect to the accelerator stroke S and the vehicle speed V is learned as a continuous model. And this characteristic is not partially discontinuous.

Although the characteristics when the vehicle speed V is "0" are shown in FIG. 5, in the "requested acceleration model", the entire range of the accelerator stroke S and the entire range of the vehicle speed V and the acceleration of the vehicle 1 are shown. The relationship with G is being learned.

Subsequently, at step 111, the difference between the acceleration G and the above-mentioned required acceleration model output Gx is obtained as an acceleration deviation ΔG. In step 112, learning of the "throttle sensitivity model" is executed using the acceleration deviation ΔG as the "error signal". That is, the acceleration deviation ΔG is used as an “error signal”, and the relationship between the throttle opening Th and the accelerator stroke S and the vehicle speed V is represented by the “throttle sensitivity model” so that the error becomes small.
To learn as.

For example, assume that the straight line shown in FIG. 6 is the initial value of the "throttle sensitivity model". When the driver DR operates the accelerator pedal 12 in order to drive faster than at present, the acceleration G of the vehicle 1 increases, and a difference from the required acceleration model output Gx occurs, and the acceleration deviation ΔG at that time is obtained. To be Then, the acceleration deviation ΔG
Is set as an “error signal”, and learning is performed so that the error amount becomes small, the throttle sensitivity model output Thx at that time is a new throttle sensitivity model output Thx, and the “throttle sensitivity model” is shown by a solid line in FIG. The initial value shown is updated to a characteristic like a curve shown by a broken line. That is, the entire relationship of the throttle sensitivity model output Thx with respect to the accelerator stroke S and the vehicle speed V is learned as a continuous model. And this characteristic is not partially discontinuous.

In this way, the learning control process using the neural network technique is executed, and the driver DR
The characteristics of the “required acceleration model” and the “throttle sensitivity model” required by are respectively learned. Here, the “weight coefficient” of the synapse sp as the characteristics of the “requested acceleration model” and the “throttle sensitivity model” that are learned from time to time are rewritten and stored in the backup RAM 26.

The initial value of the above-mentioned "weighting coefficient" when the vehicle 1 is shipped from the factory is determined as follows. First, in the required acceleration model, the acceleration G when the vehicle 1 is driven by a plurality of drivers DR is learned as "teacher data", and an average characteristic of the learning results is set as an initial value. And the "weighting coefficient" of the throttle sensitivity model is
The throttle sensitivity model output Thx is learned so as to be "0".

Then, after the processing of step 112 is executed, the subsequent processing is once terminated and "0.1
When "second" has elapsed, the processing from step 101 is started again.

On the other hand, if it is determined in step 105 that the learning is not continued, the process proceeds to step 113, and the learning start flag F is reset to "0". Next, step 11
In 4, the count value T1 from the start of learning is reset to "0".

Then, in step 115, the vehicle 1
It is determined whether is in a "turning state" such as cornering or U-turn. This determination is performed according to the process of the flowchart shown in FIG. That is, in step 115a after shifting from step 114, it is determined whether or not the absolute value of the steering angle θS read this time is larger than an arbitrary set value α. Here, when the steering angle θS is larger than the set value α, the process proceeds to step 115b, and the count value T2 by the counter is incremented by “1”.
Then, in step 115c, the count value T
It is determined whether 2 is larger than an arbitrary set time β.
Here, when the count value T2 is larger than the set time β, the steering angle θS exceeding the set value α continues for a certain period of time, so that it is determined that the vehicle 1 is in the “turning state” and the process proceeds to step 116. Transition.

On the other hand, when the count value T2 is not larger than the set time β in step 115c, it is determined that the vehicle 1 is not in the "turning state", and the routine proceeds to step 117. In step 115a, the steering angle θS
Is not larger than the set value α, step 115
In d, after resetting the count value T2 to "0", it is determined that the vehicle 1 is not in the "turning state", and the routine proceeds to step 117.

That is, in this embodiment, when the steering angle θS exceeding the set value α continues for the set time β, the vehicle 1
Is determined to be in the "turning state". If the vehicle 1 is in the "turning state", step 1
The process moves from 15 to step 116.

In step 116, the learning as described above is canceled, and a separately set value is output to the throttle computer 21 in place of the throttle sensitivity Thg or the target throttle opening Thg · S obtained by the learning. To do. That is, the throttle sensitivity ThgT for turning and the accelerator stroke S, which are separately set as values suitable for turning the vehicle 1, are output to the throttle computer 21. Alternatively, the product of the turning throttle sensitivity ThgT and the accelerator stroke S, that is, the turning target throttle opening ThgT · S is obtained, and the target throttle opening Th is obtained.
Output gT · S to the throttle computer 21. Here, the turning throttle sensitivity ThgT is set according to the type of the vehicle 1, the characteristics of the engine 2, and the like, and is a constant value determined in advance in the range of "0.8 to 1.0". Yes, and is stored in the ROM 24 in advance. In this embodiment, the turning throttle sensitivity ThgT is set to "0.9". Then, after the subsequent processing is terminated once and "0.1 seconds" has elapsed from the start of the processing, the step 1 is performed again.
The processing from 01 is started.

On the other hand, when the vehicle 1 is not in the "turning state", the routine proceeds from step 115 to step 117. Then, in step 117, the "throttle sensitivity model" is executed as in step 107. That is, the throttle sensitivity model output Thx is obtained from the characteristics of the already learned "throttle sensitivity model" using the accelerator stroke S and the vehicle speed V read this time as input values. continue,
In step 118, as in step 108, the throttle sensitivity Thg is determined from the throttle sensitivity model output Thx according to the above-described calculation formula (1).

Then, in step 119, similarly to step 109, the throttle sensitivity Th determined this time is set.
The g and the accelerator stroke S are output to the throttle computer 21. Alternatively, the product of the throttle sensitivity Thg and the accelerator stroke S, that is, the target throttle opening Th
g · S is obtained, and the target throttle opening Thg · S is output to the throttle computer 21. Then, the subsequent processing is once ended, and when “0.1 seconds” has elapsed from the start of the processing, the processing from step 101 is started again.

In this way, the throttle sensitivity or the target throttle opening degree in the "turning state" of the vehicle 1 is set. Next, the processing operation of the throttle opening degree control executed by the throttle computer 21 based on the throttle sensitivity Thg or the turning throttle sensitivity ThgT determined by the above processing operation and the accelerator stroke S at that time will be described. To do. Figure 10
3 is a flowchart showing a “throttle opening control routine” of a throttle opening control program executed by the throttle computer 21. After the processing of this routine is started, it is periodically executed at predetermined time intervals.

When the processing of this routine is started, first, at step 201, when learning the throttle sensitivity Thg, the throttle opening Th is read based on a signal from the throttle sensor 11. Also, the latest throttle sensitivity Thg and accelerator stroke S output from the neuro computer 22 or the target throttle opening Thg · S
Read. Here, if it is premised that the throttle sensitivity Thg and the accelerator stroke S are read, in step 201, the product of both Thg and S is obtained as the target throttle opening Thg · S. Alternatively, when the vehicle 1 is in the “turning state” in which the throttle sensitivity Thg is not learned, the throttle opening Th is read based on the signal from the throttle sensor 11. Further, the latest turning throttle sensitivity ThgT and accelerator stroke S or the target turning throttle opening ThgT · S output from the neuro computer 22 are read. Here, when it is premised that the turning throttle sensitivity ThgT and the accelerator stroke S are read, in step 201, the product of both ThgT and S is obtained as the turning target throttle opening ThgT · S.

Next, at step 202, it is judged if the current throttle opening Th is smaller than the target throttle opening Thg · S. Alternatively, it is determined whether or not the current throttle opening Th is smaller than the turning target throttle opening ThgT · S. Here, when the throttle opening Th is smaller than the target throttle opening Thg · S or the turning target throttle opening ThgT · S,
In step 203, the DC motor 10 is normally rotated so as to drive the throttle valve 9 in the opening direction. or,
In step 204, the throttle opening Th is read based on the signal from the throttle sensor 11.

Then, in step 205, it is judged again whether or not the throttle opening Th is smaller than the target throttle opening Thg · S. Alternatively, the throttle opening T
It is determined whether h is smaller than the target throttle opening degree ThgT · S for turning. Here, if the throttle opening Th is smaller than the target throttle opening Thg · S or the turning target throttle opening ThgT · S, the routine jumps to step 203 to drive the throttle valve 9 further in the opening direction. In steps 203, 204, 205
The process of is repeated. On the other hand, when the throttle opening Th is equal to or larger than the target throttle opening Thg · S or the turning target throttle opening ThgT · S, the throttle valve 9 is not driven further in the opening direction. Then, the subsequent processing is once ended. On the other hand, in step 202, the current throttle opening T
When h is not smaller than the target throttle opening Thg · S or the turning target throttle opening ThgT · S, the routine proceeds to step 206. And the same step 2
At 06, it is determined whether the current throttle opening Th is larger than the target throttle opening Thg · S. Alternatively, it is determined whether or not the current throttle opening Th is larger than the turning target throttle opening ThgT · S. Here, the throttle opening Th is the target throttle opening Thg
If S is not larger than the target throttle opening degree ThgT · S for turning, the subsequent processing is temporarily terminated.

If the throttle opening Th is larger than the target throttle opening Thg · S or the turning target throttle opening ThgT · S in step 206, the throttle valve 9 is closed in step 207. The DC motor 10 is reversely rotated so as to drive it.
Further, at step 208, the throttle opening Th is read based on the signal from the throttle sensor 11.

Then, in step 209, it is again determined whether or not the throttle opening Th is larger than the target throttle opening Thg · S or the turning target throttle opening ThgT · S. Here, if the throttle opening Th is larger than the target throttle opening Thg · S or the turning target throttle opening ThgT · S, the routine jumps to step 207 to drive the throttle valve 9 further in the closing direction. Then, the processes of steps 207, 208 and 209 are repeated. On the other hand, when the throttle opening Th is equal to or smaller than the target throttle opening Thg · S or the turning target throttle opening ThgT · S, the throttle valve 9 is not driven further in the closing direction. Then, the subsequent processing is once ended.

In this way, the rotation of the DC motor 10 is controlled so that the throttle opening Th coincides with the target throttle opening Thg · S or the turning target throttle opening ThgT · S, whereby the throttle valve 9 is controlled. It is controlled to open and close. As a result, the output of the engine 2 is controlled, and as a result, the driving force of the vehicle 1 is controlled.

As described above, in this embodiment, the vehicle 1 is used during the learning for obtaining the throttle sensitivity Thg.
The demand of the driver DR for the running is estimated from the acceleration G at that time as a "requested acceleration model". Further, the "throttle sensitivity model" is changed based on the "requested acceleration model" to determine the throttle sensitivity Thg.
Further, the target throttle opening T obtained from the product of the determined throttle sensitivity Thg and the accelerator stroke S
so that hg · S and throttle opening Th match
The throttle valve 9 is controlled to open and close. Moreover, the “requested acceleration model” is always obtained according to the request of the driver DR, and the “throttle sensitivity model” is obtained corresponding to the “requested acceleration model”. Then, the throttle opening Th of the engine 2 is controlled with the acceleration G that always meets the request of the driver DR.

Therefore, when the required acceleration of the driver DR for the vehicle 1 is large, the throttle sensitivity Thg becomes large. Therefore, the change range of the accelerator stroke S for obtaining the same acceleration G is narrowed, and a large acceleration G can be obtained by a small operation of the accelerator pedal 12, and the vehicle 1 is operated as if the acceleration performance is improved. The person DR can be made to feel. For example, when the driver DR is in a rush of consciousness or when the driving environment of the vehicle 1 is a highway with no traffic congestion, and the user wants to drive the vehicle 1 at high speed, a large acceleration is applied by operating the accelerator pedal 12 less. G can be obtained, and the feeling of acceleration can be improved.

On the other hand, when the required acceleration of the driver DR for the vehicle 1 is small, the throttle sensitivity Thg becomes small. Therefore, the variation range of the accelerator stroke S for obtaining the same acceleration G becomes wider, and the accelerator pedal 1
The acceleration G can be delicately changed by a large number of operations, and the driver DR can improve the operation performance of the accelerator pedal 12. For example,
When the driver DR is in a laid-back state or the driving environment of the vehicle 1 is a congested road or a snowy road,
Accelerator pedal 1 when you want to drive slowly
The acceleration G can be delicately changed by a large number of operations, and the operation feeling of the vehicle 1 can be improved.

That is, in this embodiment, since the learning is performed so as to match the degree of demand for the acceleration of the driver DR, the throttle sensitivity Thg that always matches the characteristics of the driver DR is determined. As a result, the driver's DR consciousness (hurrying, relaxing, etc.) and driving environment (road condition, day / night, tunnel, rainy road, snowy road,
Regardless of mountain road, traffic jam, etc.)
It is possible to always control the driving force that matches the characteristics of the driver DR.

Further, in this embodiment, since the neural network technology is used for the learning control in the neuro computer 22, the accelerator stroke S and the vehicle speed V
The entire relationship of the required acceleration model output Gx with respect to is learned as a continuous model, and its characteristics are not partially discontinuous. Similarly, the entire relationship of the throttle sensitivity model output Thx with respect to the accelerator stroke S and the vehicle speed V is learned as a continuous model, and its characteristics are not partially discontinuous. This is because the "requested acceleration model" learned between the discontinuity points of the accelerator stroke S and the vehicle speed V is interpolated by using the neural network technology. That is, the correction of the required acceleration model output Gx performed in a specific range of the accelerator stroke S and the vehicle speed V is reflected in the correction of the required acceleration model output Gx corresponding to other ranges of the accelerator stroke S and the vehicle speed V. . Further, the correction of the throttle sensitivity model output Thx performed in a certain range of the accelerator stroke S and the vehicle speed V is also reflected in the throttle sensitivity model output Thx corresponding to the other range of the accelerator stroke S and the vehicle speed V.

As a result, the control of the driving force of the vehicle 1 is controlled by the driver DR over the entire range of the vehicle speed V.
It is possible to make the operation amount of 2, that is, continuous over the entire operation range of the accelerator stroke S. Therefore, when the driver DR continuously depresses the accelerator pedal 12, the acceleration G of the vehicle 1 does not suddenly change, and the increase in the vehicle speed V can be made smooth at all times.

Further, in this embodiment, the "requested acceleration model" is estimated from the acceleration G, and the "requested acceleration model" is estimated.
Since learning is performed to change the "throttle sensitivity model" from, the map interpolation calculation becomes unnecessary compared to the case where the map is rewritten by only partial correction (correction), and the calculation time is reduced. It can be further shortened.

In addition, in this embodiment, in the neuro computer 22, the acceleration sensor 14 is connected to the external input / output circuit 27 via the low pass filter 29. Therefore, when noise occurs in the detection signal of the acceleration sensor 14 due to the harshness when the vehicle 1 is traveling on a bumpy road, the high-pass component correlated with the noise is attenuated by the low-pass filter 29. It That is,
The signal of the acceleration G from the acceleration sensor 14 is shown in FIG.
Even when a large noise due to the harshness is included as shown in (a), the signal passes through the low-pass filter 29, so that the signal of the acceleration G with less noise as shown in FIG. 11 (b) is obtained. Will be adjusted.

As a result, in the neurocomputer 22, the signal of the acceleration G used for learning can be a signal from which noise due to harshness has been removed. Therefore, erroneous learning about the “requested acceleration model” and the “throttle sensitivity model” can be prevented in advance, and in turn, the throttle sensitivity Thg can be prevented from being adjusted in the wrong direction.

In addition, in this embodiment, when the vehicle 1 is in the "turning state", the learning for obtaining the throttle sensitivity Thg is canceled, and the throttle sensitivity Thg required at the time of learning is replaced with the turning. The turning throttle sensitivity ThgT set in advance as a suitable value is set. Also, the set turning throttle sensitivity ThgT
And the target throttle opening degree ThgT · S for turning obtained from the product of the accelerator stroke S and the throttle opening Th
The throttle valve 9 is controlled to open and close so that and match.

Therefore, when the vehicle 1 shifts from straight forward acceleration running to turning, the target throttle opening ThgT.multidot.Tg suitable for turning is replaced with the target throttle opening Thg.S learned during acceleration running. Based on S, the opening / closing control of the throttle valve 9 is performed according to the operation amount of the accelerator pedal 12. Therefore, at the time of turning, the operation of the accelerator pedal 12 required for output control of the engine 2 is suitable for the driver DR to turn.

For example, in FIG. 12, it is assumed that the throttle sensitivity Thg is finally determined to be a relatively high value of "1.5" by learning when the vehicle 1 starts to accelerate and travel. Then, at time t1 after the vehicle starts, it is assumed that the driver DR appropriately returns the depression of the accelerator pedal 12 and appropriately turns the steering wheel 7, whereby the vehicle 1 shifts to cornering. In this case, as shown by the solid line in FIG. 12, the high throttle sensitivity Thg of “1.5” determined by learning is “0” suitable for turning at time t2 when the set time β has elapsed after the start of cornering. .9 ”
The throttle sensitivity ThgT for turning, which is relatively low, is immediately changed. Thereafter, the turning throttle sensitivity ThgT is maintained until time t3 when the driver DR returns the steering wheel 7 and depresses the accelerator pedal 12 appropriately to get out of the corner. Even if learning is performed by depressing the accelerator pedal 12 from the time t3 and the throttle sensitivity Thg is slightly changed, the throttle sensitivity Thg is increased until the next large acceleration is performed.
g will not be unnecessarily high. This is
It is clear by comparing with the change in the throttle sensitivity Thg of the conventional example shown by the broken line in 2. Then, the opening / closing control of the throttle valve 9 is performed on the basis of the target throttle opening degree ThgT · S obtained from the turning throttle sensitivity ThgT or a target throttle opening degree close thereto.

That is, in this embodiment, when the vehicle 1 shifts to cornering after a sudden start, the throttle sensitivity is suppressed to an appropriately small value, and the changes in the vehicle speed V and the acceleration G with respect to the accelerator stroke S become small. Therefore, in the cornering, good steering / driving performance can be secured without violating the intention of the driver DR. On the other hand, when the vehicle 1 makes a quick U-turn after a gentle start or tries to quickly exit a winding road with many corners, the throttle sensitivity is appropriately increased and the accelerator stroke is increased. The changes in the vehicle speed V and the acceleration G with respect to S increase moderately. Therefore, in those U-turns and the like, it is possible to prevent the acceleration response of the vehicle 1 from becoming dull against the intention of the driver DR, and it is possible to secure good steering / driving performance. That is, in this embodiment, when the vehicle 1 is turning, a good control
The driving performance can be secured. In addition, since it is possible to secure good steering and driving performance suitable for turning, the accelerator pedal 12 can be used on roads with many corners.
It is also possible to reduce the driver's feeling of fatigue when operating the.

The present invention is not limited to the above-described embodiment, but may be implemented as follows with a part of the configuration appropriately changed without departing from the spirit of the invention. (1) In the above-described embodiment, the gasoline engine 2 is used as the drive source and the linkless type throttle valve 9 is used as the control amount changing means, but other drive sources and control amount changing means may be embodied. . For example, in an electric vehicle, an electric motor such as a DC motor may be used as a drive source, and a current control circuit that controls the current to the electric motor may be used as the control amount changing means.

(2) In the above embodiment, the accelerator pedal 12 is used as the output operation means operated by the driver DR, but an accelerator lever or other operation member may be used as the output operation means.

(3) In the above embodiment, the accelerator sensor 1
Although the accelerator stroke S is detected by using 3 as the operation amount detection means, the following may be performed. That is, instead of using the accelerator stroke S, a sensor for detecting the accelerator pedal force is used, or an accelerator sensor for detecting the accelerator stroke S and a sensor for detecting the accelerator pedal force are used together.

(4) Although the acceleration sensor 14 is used as the acceleration detecting means in the above embodiment, the acceleration G is obtained by differentiating the vehicle speed V detected by the vehicle speed sensor 15 with respect to the vehicle speed sensor 15 as the acceleration detecting means. You may get it.

(5) In the above embodiment, as the neural network technology in the neuro computer 22,
Although a multi-layered neural network is adopted, an interconnected neural network can also be adopted.

(6) In the above embodiment, the throttle sensitivity model output Thx is used as the correction amount of the throttle sensitivity, and the throttle sensitivity Thg is obtained by the calculation formula (1). However, the throttle sensitivity Thg can be directly output. In this case, the initial value when the vehicle 1 is shipped from the factory is learned so as to output the set value α.

(7) In the above-described embodiment, the learning start is determined when the absolute value of the accelerator stroke change ΔS and the vehicle speed V are larger than arbitrary set values. The start of learning may be determined based on the learning. For example, the learning start may be determined when the acceleration G is larger than an arbitrary set value.

(8) In the above embodiment, the learning continues when the count value T1 from the start of learning is smaller than an arbitrary predetermined value, and the vehicle speed V and the accelerator stroke S are larger than arbitrary set values. Although it is determined that the learning is in progress, it may be determined that the learning is continuing based on other conditions. For example, a brake sensor that detects the operation of the brake pedal that is operated to brake the vehicle 1 may be provided, and when the brake operation is not detected by the brake sensor after the start of learning, it may be determined that the learning is continuing. Good.

(9) In the above embodiment, when the steering angle θS exceeding the set value α continues for the set time β, it is determined that the vehicle 1 is in the “turning state”.
It may be possible to immediately determine that the vehicle is in the "turning state" when S exceeds the set value α.

(10) In the above embodiment, the steering angle θS obtained from the steering angle sensor 8 is used to judge the "turning state" of the vehicle 1. However, the yaw rate obtained from the yaw rate sensor provided in the vehicle may be used. The "turning state" may be determined based on the above. Alternatively, the "turning state" may be determined based on the lateral acceleration obtained from the lateral acceleration sensor provided in the vehicle. Furthermore, the “turning state” may be determined by combining the steering angle θS obtained from the steering angle sensor 8 and the yaw rate obtained from the yaw rate sensor. In the case of this combination, it is possible to more accurately determine the "turning state" of the vehicle.

[0082]

As described above in detail, according to the present invention, the acceleration of the vehicle is used as teacher data to be compared,
The relationship between the acceleration of the vehicle and the operation amount of the output operation means
Learning as an acceleration model required by the driver.
Further, the deviation between the learned output of the acceleration model and the acceleration of the vehicle is used as an error signal, and the relationship between the control amount of the drive source and the operation amount of the output operation means is learned as a control amount sensitivity model. Then, the learned output of the control amount sensitivity model is used as reference data, and the control amount of the drive source is controlled according to the operation amount of the output operation means.

Therefore, an acceleration model corresponding to the driver's request is obtained, a control amount sensitivity model is obtained corresponding to the acceleration model, and the control amount is always controlled with an acceleration that meets the driver's request. . Further, the entire relationship between the acceleration and the control amount with respect to the operation amount and the entire relationship between the control amount and the operation amount are learned as a continuous model, and the entire relation between the acceleration and the control amount with respect to the entire operation range of the output operation means becomes discontinuous. There is no.

As a result, regardless of the driver's consciousness and the driving environment, it is possible to always control the driving force that matches the characteristics of the driver. Moreover, there is an excellent effect that the control of the driving force can be made continuous over the entire range of the operation amount of the accelerator pedal or the like by the driver.

Further, according to the present invention, when it is determined that the vehicle is in a turning state, the reference data used for controlling the control amount of the drive source in accordance with the operation amount of the output operation means is
Instead of the output of the controlled variable sensitivity model, reference data set separately as a value suitable for turning is changed.

Therefore, when the vehicle shifts from straight forward acceleration to turning, the operation of the output operation means required for output control of the drive source becomes suitable for the driver to turn. As a result, it has an excellent effect that it is possible to secure good steering and driving performance suitable for the turning.

[Brief description of drawings]

FIG. 1 is a conceptual configuration diagram showing a basic conceptual configuration of the present invention.

FIG. 2 is a schematic configuration diagram showing a schematic configuration of a driving force control device for a vehicle in one embodiment embodying the present invention.

FIG. 3 is a block diagram showing an electrical configuration of a throttle computer and a neuro computer in one embodiment.

FIG. 4 is a configuration diagram showing a conceptual configuration of a multilayer neural network applied to a neuro computer in one embodiment.

FIG. 5 is a characteristic diagram showing characteristics of a “requested acceleration model” in one embodiment.

FIG. 6 illustrates a “throttle sensitivity model” in one embodiment.
It is a characteristic diagram showing the characteristics of.

FIG. 7 is a characteristic diagram showing a characteristic of “throttle sensitivity” in one embodiment.

FIG. 8 is a flowchart showing a “learning control routine” executed by a neuro computer in one embodiment.

FIG. 9 is a flowchart showing a part of the processing of a “learning control routine” in detail in one embodiment.

FIG. 10 is a flowchart showing a “throttle opening control routine” executed by a throttle computer in the embodiment.

FIG. 11 is a time chart showing changes in the acceleration signal related to the action of the low pass filter in the embodiment.

FIG. 12 is a time chart showing a change in throttle sensitivity and the like when the vehicle shifts from start acceleration running to turning in one embodiment.

[Explanation of symbols]

DESCRIPTION OF SYMBOLS 1 ... Vehicle, 2 ... Engine as a drive source, 8 ... Steering angle sensor as a turning state detection means, 9 ... Throttle valve, 10 ... DC motor (9, 10 constitutes the control amount changing means), 12 ... accelerator pedal as output operation means, 13 ... accelerator sensor as operation amount detection means,
14 ... Acceleration sensor as acceleration detection means, 15 ... Vehicle speed sensor as speed detection means, 21 ... Throttle computer constituting drive control means, 22 ... Required acceleration model learning means, control amount sensitivity model learning means, turning state detection means And a neuro computer constituting the reference data changing means.

Continuation of the front page (51) Int.Cl. ⁵ Identification code Office reference number FI Technical display location F02D 45/00 340 H 7536-3G A 7536-3G G05B 13/02 L 9131-3H 13/04 9131-3H ( 72) Inventor Masuharu Oshima, 41, Nagachote, Nagakute-cho, Aichi-gun, Aichi Prefecture, 1 Toyota Central Research Institute, Inc. (72) Inventor ▲ Hiroyuki Yoshita 41, Nagakute, Nagakute-cho, Aichi-gun, Aichi Prefecture Local 1 Toyota Central Research Institute Co., Ltd.

Claims (1)

[Claims] 1. A control amount changing unit for changing a control amount of a drive source mounted on a vehicle, an output operating unit operated by a driver for arbitrarily controlling an output of the drive source, and An operation amount detection unit for detecting an operation amount of the output operation unit, and controlling the output of the drive source by driving the control amount changing unit according to a detection result of the operation amount detection unit. A vehicle driving force control device configured to control a driving force of a vehicle, an acceleration detecting means for detecting an acceleration of the vehicle, a speed detecting means for detecting a speed of the vehicle, and the acceleration detecting means. As the teacher data to be compared with the acceleration obtained by the detection of, the difference between the teacher data and the output of the means is used as an error signal, and the operation amount of the output operation means is set so that the error becomes small. And a required acceleration model learning means for learning the relationship of the acceleration of the vehicle with respect to the speed obtained by the detection of the speed detecting means as an acceleration model required by the driver, and the acceleration obtained by the detection of the acceleration detecting means. And a deviation obtained from the output of the acceleration model learned by the required acceleration model learning means as an error signal, and the speed obtained by the operation amount of the output operation means and the detection of the speed detection means so that the error becomes small. The relationship of the control amount of the drive source with respect to the control amount sensitivity model learning means for learning as a control amount sensitivity model, and the output of the control amount sensitivity model learned by the control amount sensitivity model learning means as reference data, In accordance with the operation amount detected by the operation amount detection means based on the reference data, Drive control means for controlling drive; turning state detection means for detecting a turning state of the vehicle; and the drive control means when it is determined that the vehicle is in a turning state based on a detection result of the turning state detection means. In the reference data used for controlling the drive of the controlled variable changing means in, instead of the output of the controlled variable sensitivity model,
A driving force control device for a vehicle, comprising: reference data changing means for changing to reference data separately set as a value suitable for turning.