CN110001654B - Intelligent vehicle longitudinal speed tracking control system and control method for self-adaptive driver type - Google Patents

Abstract

The invention relates to an adaptive driver-type intelligent vehicle longitudinal speed tracking control method, comprising the following steps: a data acquisition unit collects vehicle speed information in real time; a data processing unit preprocesses the information collected in the previous step; and manually inputs to a logic operation unit Driver type and automatically read the target vehicle speed of the system; judge whether the vehicle needs to accelerate or decelerate; enter the corresponding acceleration control module or deceleration control module according to the judgment result of the previous step; output the output signal to the corresponding line according to the calculation result of the previous step control system. The method of the present invention can provide drivers with different characteristics highly acceptable longitudinal speed tracking control strategies for smart cars, improve vehicle driving safety and improve ride experience, and at the same time, the acceleration and braking switching strategies in the design avoid longitudinal dynamics system Frequent actions at unnecessary moments improve the safety of the vehicle in longitudinal control and reduce energy consumption.

Description

An adaptive driver type intelligent vehicle longitudinal speed tracking control system and its control system method

technical field

The invention relates to a longitudinal speed tracking control system and control method of an intelligent vehicle, in particular to an adaptive driver (passenger) type intelligent vehicle longitudinal speed tracking control system and control method.

Background technique

With the rapid development of smart car-related technologies, the degree of intelligence of vehicles is gradually increasing, and drivers will gradually be liberated from heavy driving tasks. Especially in longitudinal control, the existing cruise control and adaptive cruise systems have been applied on a large scale, which greatly reduces the driver's driving burden. However, the pursuit of more intelligent and humanized longitudinal speed control is still the development direction of the longitudinal speed control of intelligent vehicles.

The longitudinal speed control of the smart car is the key core part of the smart car to achieve unmanned driving in the longitudinal direction. The longitudinal speed control will have a decisive impact on the power, stability and comfort of the vehicle. At present, there have been many research results on the longitudinal speed tracking control of intelligent vehicles. For example, Chinese patent CN108279563A proposes a speed adaptive unmanned vehicle trajectory tracking PID control method, Chinese patent CN108860146A proposes a speed control method, system and related devices for dual-drive vehicles, and Chinese patent CN108319144A proposes a robot trajectory Tracking control method and system.

As far as the existing research results are concerned, most of the research on the longitudinal speed tracking control of intelligent vehicles regards the control accuracy as the only index at the beginning of the design, and pursues the accuracy excessively. Although this idea and method can obtain better system performance, it reduces the acceptance of the driver (occupant) to the system, especially under the conditions of extremely unreasonable target speeds such as frequent and drastic changes in the target speed. Under the traditional control method, the vehicle frequently feels frustrated, which will make the human driver extremely distrustful of the system. In addition, under this working condition, the driving/braking system will also operate frequently at the same time, resulting in increased tire wear and waste of energy. In addition, there are few related results that consider adding the driver's personality characteristics to the longitudinal speed tracking control of the smart car, and combine the driver's characteristics with the longitudinal speed tracking control method of the smart car.

Contents of the invention

The object of the present invention is to address the deficiencies of the above-mentioned prior art, to provide an adaptive driver type smart car longitudinal speed tracking control system and control method, which uses the driver type parameter as a parameter to ensure the smart car longitudinal speed tracking Control accuracy and avoid frequent unnecessary movements of the longitudinal dynamic system. At the same time, considering the differences in individual drivers' personalities, while solving the problem of the control accuracy of the longitudinal speed of the smart car, the personalized coordinated control of the adaptive driver (occupant) characteristics of the longitudinal speed control of the smart car is realized.

The purpose of the present invention is achieved by the following technical solutions:

An adaptive driver type intelligent vehicle longitudinal speed tracking control system, which is composed of a data acquisition unit 1, a data processing unit 2, a logical operation unit 3, a driver type input unit 4 and a wire control system 5;

The data acquisition unit 1 is connected to the data processing unit 2, the data processing unit 2 and the driver type input unit 4 are connected to the logic operation unit 3, the logic operation unit 3 is connected to the wire control system 5, and the calculation results are output to the wire control system 5. The wire control system 5 is used to control the acceleration or deceleration of the vehicle.

The control method of the intelligent vehicle longitudinal speed tracking control system of the above-mentioned adaptive driver type comprises the following steps:

A, the data acquisition unit 1 collects the vehicle speed signal information in real time through the CAN line;

B. The data processing unit 2 preprocesses the vehicle speed signal information collected in step A through the Gaussian filter method;

C. Manually input the driver type to the driver type input unit 4 and automatically read the system target vehicle speed;

D. The logical operation unit 3 judges whether the vehicle needs to be accelerated or decelerated;

E. Enter the corresponding acceleration control module or deceleration control module according to the judgment result of step D;

F. Output the calculation result according to step E to the corresponding wire control system 5 as an output signal.

Further, in step C, the value input range for the driver type is a closed interval [0, 2]. Specifically, a value of 0 indicates an extremely conservative driver, and a value of 2 indicates an extremely aggressive driver, ranging from a value of 0 to Value 2 driver type linearly transitions from extreme conservative to extreme aggressive.

Further, step D, judging whether the vehicle needs to be accelerated or decelerated includes the following two steps:

D1. Determine whether the following formula is established. If established, the system neither enters the acceleration control module to start the acceleration control program, nor enters the deceleration control module to start the deceleration control program. If not established, then execute step D2;

V _tar −V _act =0

In the formula, V _tar represents the target speed of the vehicle, and V _act represents the actual speed of the vehicle.

D2, judge whether the following formula is established, if established, the system enters the acceleration module to start the acceleration control program, if not established, then enters the deceleration module to start the deceleration control program;

V _tar -V _act ≥ 0.

Further, entering the corresponding acceleration control module or deceleration control module according to the judgment result of step D in the step E includes the following modes:

Mode 1, enter the acceleration control module, the acceleration control program adopts the PID control method based on fuzzy logic, the difference between the driver type parameter and the target vehicle speed is used as the input of the fuzzy logic controller, and the obtained output is used as the traditional PID controller The coefficient of the proportional link in .

Specifically, the driver's basic domain of discourse is transformed into the fuzzy domain of discourse, and the non-uniform quantization method is used to select the word set {cautious, common, active} for the driver's characteristics, that is, {CA, CO, AC}. For the actual speed vs. ideal speed error select the word set {little, middle, fully}, ie {LI, MI, FU}.

Furthermore, the triangle with simple mathematical expression, less calculation amount and high sensitivity is selected as the membership function of input and output.

Further, according to the effect and influence of PID tuning parameters on the output characteristics and control system, combined with expert experience and laboratory test data, different driver characteristic inputs and actual speed and ideal speed error inputs of the longitudinal control system, combined with vehicle dynamics characteristics, the following parameter self-tuning principles are summarized:

a. When the driver's characteristics are relatively aggressive and the speed tracking error is above the medium size, in order to speed up the response speed of the system, a larger P value should be taken to reduce the time constant of the system;

b. When the driver's characteristics are relatively aggressive and the speed tracking error is small, in order to speed up the response speed of the system and avoid excessive overshoot, a moderate P value should be selected;

c. When the driver type is relatively normal and the speed tracking error is above the medium size, a moderate P value should be selected to satisfy the riding experience of the normal driver as far as possible;

d. When the driver type is relatively normal and the speed tracking error is small, in order to avoid excessive overshoot, a smaller P value should be selected;

e. When the driver type is more cautious and the speed tracking error is below the medium size, a smaller P value should be selected to avoid overshoot and satisfy the driver's riding experience as much as possible;

f. When the driver type is more cautious and the speed tracking error is small, a medium-sized P value should be selected.

Further, the area center of gravity method is used for defuzzification.

Specifically, the integral link coefficient of the PID controller takes a value of 0.1, and the differential link coefficient takes a value of 0.05.

Mode 2. Enter the deceleration control module, set the maximum output brake master cylinder pressure to 10Mpa, use the traditional PID controller, take the coefficient of 6 for the proportional link, take the coefficient of 0.1 for the integral link, and take the coefficient of 0.05 for the differential link.

Compared with prior art, the beneficial effect of the present invention is:

The adaptive driver-type smart car longitudinal speed tracking control method of the present invention can provide drivers with different characteristics with a highly acceptable smart car longitudinal speed tracking control strategy, improve car driving safety and improve ride experience, and at the same time, the acceleration in the design The switching strategy of braking and braking avoids frequent actions of the longitudinal dynamics system at unnecessary moments, improves the safety of the longitudinal control of the vehicle and reduces energy consumption.

Description of drawings

Fig. 1 is the schematic flow chart of the control method of the intelligent vehicle longitudinal speed tracking control system of the self-adaptive driver type of the present invention;

Fig. 2 is a schematic diagram of the system module of the present invention;

Fig. 3 is a schematic diagram of the acceleration control module of the present invention.

In the figure, 1. Data acquisition unit 2. Data processing unit 3. Logic operation unit 4. Driver type input unit 5. Wire control system.

Detailed ways

The present invention will be further described below in conjunction with embodiment, should be understood that these embodiments are only used to illustrate the present invention and are not intended to limit the scope of the present invention, after having read the present invention, those skilled in the art can modify various equivalent forms of the present invention All fall within the scope defined by the appended claims of this application.

As shown in Figure 1 and Figure 2, an adaptive driver-type intelligent vehicle longitudinal speed tracking control system includes a data acquisition unit 1, a data processing unit 2, a logic operation unit 3, and a driver (passenger) type input unit 4 , Wire control system 5. The data acquisition unit 1 is connected to the data processing unit 2, the data processing unit 2 and the driver (passenger) type input unit 4 are connected to the logical operation unit 3, the logical operation unit 3 is connected to the wire control system 5, and the logical operation unit 3 converts the operation result The output is to the control-by-wire system 5, and the control-by-wire system controls the acceleration or deceleration of the vehicle.

The control method of the intelligent vehicle longitudinal speed tracking control system of the above-mentioned adaptive driver type comprises the following steps:

A. The data acquisition unit collects vehicle speed information in real time;

B. The data processing unit preprocesses the information collected in step A;

C, manually input the driver (passenger) type to the driver type input unit 4 and automatically read the system target vehicle speed;

D. The logical operation unit 3 judges whether the vehicle needs to be accelerated or decelerated;

E. Enter the corresponding acceleration control module or deceleration control module according to the judgment result of step D;

F. Output the calculation result according to step E as an output signal to the corresponding wire control system.

Further, the collection of the vehicle speed signal in step A is obtained through the CAN line.

Further, in the preprocessing of the vehicle speed signal in the step B, the Gaussian filtering method is selected as the filtering method for the vehicle speed signal.

Further, the value input range for the driver type in the step C is a closed interval [0, 2]. Specifically, a value of 0 indicates an extremely conservative driver (passenger), and a value of 2 indicates an extremely aggressive driver , from the value 0 to the value 2, the driver (passenger) type linearly transitions from the extreme conservative type to the extreme aggressive type.

Further, the value input range for the driver type in the step C is a closed interval [0, 2]. Specifically, a value of 0 indicates an extremely conservative driver (passenger), and a value of 2 indicates an extremely aggressive driver , from the value 0 to the value 2, the driver (passenger) type linearly transitions from the extreme conservative type to the extreme aggressive type.

Further, the step D judging whether the vehicle needs to be accelerated or decelerated includes the following two steps:

D1. Determine whether the following formula is established. If established, the system neither enters the acceleration control module to start the acceleration control program, nor enters the deceleration control module to start the deceleration control program. If not established, then execute step D2;

V _tar −V _act =0

In the formula, V _tar represents the target speed of the vehicle, and V _act represents the actual speed of the vehicle.

D2, judge whether the following formula is established, if established, the system enters the acceleration module to start the acceleration control program, if not established, then enters the deceleration module to start the deceleration control program;

V _tar -V _act ≥ 0

Further, in the step E, entering the corresponding acceleration control module or deceleration control module according to the judgment result of step D mainly includes the following modes:

Mode 1. Entering the acceleration control module, the acceleration control program adopts the PID control method based on fuzzy logic, takes the difference between the driver type (passenger) parameter and the target vehicle speed as the input of the fuzzy logic controller, and uses the obtained output result as the traditional The coefficient of the proportional link in the PID controller.

Furthermore, the driver (passenger) basic domain of discourse is transformed into the fuzzy domain of discourse, and the method of non-uniform quantization is adopted. The quantification of the driver’s characteristics is shown in Table 1, and the quantification of the error between the actual speed and the ideal speed is shown in Table 2. , select the word set {cautious, common, active} for driver characteristics, namely {CA, CO, AC}. For the actual speed vs. ideal speed error select the word set {little, middle, fully}, ie {LI, MI, FU}.

Table 1

quantization level -1 0 1 variation range [0 0.4) [0.4 1.6] (1.6 2]

Table 2

quantization level -1 0 1 variation range [0 1) [1 4] (4inf)

Further, the triangle with simple mathematical expression, less calculation amount and high sensitivity is selected as the membership function of input and output. The parameters of the membership function of the input triangle are shown in Table 3 and Table 4, and the membership function of the output triangle is as follows Table 5 shows.

table 3

driver type Params cautious trimf [-0.8 0 0.8] common trimf [0.2 1 1.8] active trimf [1.2 2 2.8]

Table 4

error type Params little trimf [-2 0 2] middle trimf [1 2.5 4] fully trimf [3 5 1e+09]

table 5

P type Params little trimf [-2 0 2] middle trimf [1 2.5 4] fully trimf [3 5 7]

Further, according to the effect and influence of PID tuning parameters on the output characteristics and control system, combined with expert experience and laboratory test data, different driver characteristic inputs and actual speed and ideal speed error inputs of the longitudinal control system, combined with vehicle dynamics characteristics, the following parameter self-tuning principles are summarized:

a. When the driver’s characteristics are relatively aggressive and the speed tracking error is above the medium size, in order to speed up the response speed of the system, a larger P value should be taken to reduce the time constant of the system;

b. When the driver's characteristics are relatively aggressive and the speed tracking error is small, in order to speed up the response speed of the system and avoid excessive overshoot, a moderate P value should be selected;

c. When the driver type is relatively normal and the speed tracking error is above the medium size, a moderate P value should be selected to satisfy the riding experience of the normal driver as much as possible;

d. When the driver type is relatively normal and the speed tracking error is small, in order to avoid excessive overshoot, a smaller P value should be selected;

e. When the driver type is more cautious and the speed tracking error is below the medium size, a smaller P value should be selected to avoid overshoot and satisfy the driver's riding experience as much as possible;

f. When the driver type is more cautious and the speed tracking error is small, a medium-sized P value should be selected.

In this control system, the two inputs and one output of the fuzzy controller are divided into three levels, and the following nine fuzzy inference rules can be obtained by combining the above-mentioned setting rules and the summary of expert experience. Written as a fuzzy reasoning statement as follows:

1. If (Driver is cautious) and (Error is little) than (P is little)

2. If (Driver is cautious) and (Error is middle) than (P is little)

3. If (Driver is cautious) and (Error is fully) than (P is middle)

4. If (Driver is common) and (Error is little) than (P is little)

5. If (Driver is common) and (Error is middle) than (P is middle)

6. If (Driver is common) and (Error is fully) than (P is middle)

7. If (Driver is active) and (Error is little) than (P is middle)

8. If (Driver is active) and (Error is middle) than (P is fully)

9. If (Driver is active) and (Error is fully) than (P is fully)

Further, the area center of gravity method is used for defuzzification.

Specifically, the integral link coefficient of the PID controller takes a value of 0.1, and the differential link coefficient takes a value of 0.05.

Mode 2. Enter the deceleration control module, set the maximum output brake master cylinder pressure to 10Mpa, use the traditional PID controller, take the coefficient of 6 for the proportional link, take the coefficient of 0.1 for the integral link, and take the coefficient of 0.05 for the differential link.

Claims (5)

1. The intelligent vehicle longitudinal speed tracking control method of the self-adaptive driver type is characterized by comprising the following steps of:

A. the data acquisition unit (1) acquires vehicle speed signal information in real time through a CAN line;

B. the data processing unit (2) preprocesses the vehicle speed signal information acquired in the step A through a Gaussian filtering method;

C. manually inputting a driver type to a driver type input unit (4) and automatically reading a system target vehicle speed;

D. the logic operation unit (3) judges whether the vehicle needs acceleration or deceleration;

E. d, entering a corresponding acceleration control module or deceleration control module according to the judgment result of the step D;

and D, entering a corresponding acceleration control module according to the judgment result of the step D, wherein the acceleration control module specifically comprises: entering an acceleration control module, adopting a PID control method based on fuzzy logic, taking the difference value between the type parameter of a driver and the target vehicle speed as the input of a fuzzy logic controller, and taking the obtained output result as the coefficient of a proportional link in a traditional PID controller;

the method comprises the following steps: transforming the basic domain range of the driver into a fuzzy domain, adopting a non-uniform quantization method to quantize the characteristic of the driver and the quantization condition of the actual speed and the ideal speed error, selecting a corresponding word set for the characteristic of the driver, the actual speed and the ideal speed error, selecting a triangle as a membership function of input and output, defuzzifying the obtained output result as a coefficient of a proportional link in a traditional PID controller according to the self-setting principle and fuzzy reasoning rules of different characteristic input and actual speed and ideal speed error input parameters of the driver of a longitudinal control system;

the parameter self-tuning principle comprises the following steps:

a. when the characteristics of the driver are more aggressive and the speed tracking error is above a medium level, a larger P value is needed to be taken in order to accelerate the response speed of the system, so that the time constant of the system is reduced;

b. when the characteristics of the driver are more aggressive and the speed tracking error is smaller, a P value with a moderate value is selected in order to accelerate the response speed of the system and avoid excessive overshoot;

c. when the driver is normal in type and the speed tracking error is above the medium value, a P value with a moderate value is selected to meet the riding feeling of the normal driver as much as possible;

d. when the driver type is normal and the speed tracking error is small, a P value with a small value should be selected in order to avoid excessive overshoot;

e. when the driver is cautious in type and the speed tracking error is below the medium magnitude, the P value with smaller value is selected to avoid overshoot and meet the riding feeling of the driver as much as possible;

f. when the driver type is cautious and the speed tracking error is small, a medium magnitude P value should be selected;

F. and E, outputting the calculation result according to the step E as an output signal to a corresponding drive-by-wire system (5).

2. An intelligent vehicle longitudinal speed tracking control method of adaptive driver type according to claim 1, characterized in that: and C, the numerical value input range of the driver type is a closed range [0,2], wherein the numerical value 0 represents an extremely conservative type driver, the numerical value 2 represents an extremely aggressive type driver, and the driver type from the numerical value 0 to the numerical value 2 linearly transits from the extremely conservative type to the extremely aggressive type.

3. An intelligent vehicle longitudinal speed tracking control method of adaptive driver type according to claim 1, characterized in that: step D, judging whether the vehicle needs acceleration or deceleration or not comprises the following two steps:

d1, judging whether the following formula is established, if so, the system does not enter an acceleration control module to start an acceleration control program or does not enter a deceleration control module to start a deceleration control program, and if not, executing a step D2;

V _tar -V _act ＝0

wherein V is _tar Indicating a target vehicle speed of the vehicle, V _act Representing an actual speed of the vehicle;

d2, judging whether the following formula is established, if so, entering an acceleration module to start an acceleration control program, and if not, entering a deceleration module to start a deceleration control program;

V _tar -V _act ≥0。

4. the method for controlling the longitudinal speed tracking of the intelligent vehicle according to claim 1, wherein step E, entering the corresponding deceleration control module according to the determination result of step D is specifically: and (3) entering a deceleration control module, setting the maximum output brake master cylinder pressure to be 10Mpa, adopting a traditional PID controller, taking a coefficient of 6 in a proportional link, taking a coefficient of 0.1 in an integral link, and taking a coefficient of 0.05 in a differential link.

5. An adaptive driver-type intelligent vehicle longitudinal speed tracking control system for executing the adaptive driver-type intelligent vehicle longitudinal speed tracking control method according to claim 1, characterized in that: the system consists of a data acquisition unit (1), a data processing unit (2), a logic operation unit (3), a driver type input unit (4) and a drive-by-wire system (5);

the data acquisition unit (1) is connected with the data processing unit (2), the data processing unit (2) and the driver type input unit (4) are connected with the logic operation unit (3), the logic operation unit (3) is connected with the drive-by-wire system (5), the operation result is output to the drive-by-wire system (5), and the drive-by-wire system (5) is used for controlling acceleration or deceleration of the vehicle.