CN111002976A - Intelligent vehicle crosswind-resistant control method based on fuzzy self-adaptive PID control - Google Patents

Intelligent vehicle crosswind-resistant control method based on fuzzy self-adaptive PID control Download PDF

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CN111002976A
CN111002976A CN201910889921.0A CN201910889921A CN111002976A CN 111002976 A CN111002976 A CN 111002976A CN 201910889921 A CN201910889921 A CN 201910889921A CN 111002976 A CN111002976 A CN 111002976A
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vehicle
control
crosswind
error
steering
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CN111002976B (en
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姚雪莲
朱燕
杨艺
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Jiangsu University of Technology
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Jiangsu University of Technology
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    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W30/00—Purposes of road vehicle drive control systems not related to the control of a particular sub-unit, e.g. of systems using conjoint control of vehicle sub-units, or advanced driver assistance systems for ensuring comfort, stability and safety or drive control systems for propelling or retarding the vehicle
    • B60W30/02—Control of vehicle driving stability
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W2520/00—Input parameters relating to overall vehicle dynamics
    • B60W2520/10—Longitudinal speed

Abstract

The invention discloses an intelligent vehicle crosswind-resistant control method based on fuzzy self-adaptive PID control, which is characterized in that when a vehicle runs at a high speed and is interfered by crosswind, a target route of the vehicle is obtained; comparing the current state of the vehicle with a target route to obtain the yaw rate and the centroid sideslip angle error of the vehicle; obtaining error change rates of the yaw angular velocity and the centroid slip angle according to the yaw angular velocity and the centroid slip angle error of the vehicle; determining the control quantity of the vehicle wheel turning angle based on a fuzzy self-adaptive PID controller according to the error and the error change rate; and sending the wheel rotation angle control quantity to a steering motor controller so that the steering motor controller applies steering torque to a steering column according to the wheel rotation angle target value to complete vehicle steering, thereby controlling the lateral force of the tire. The invention adopts the fuzzy self-adaptive PID to control the vehicle when the vehicle is interfered by the crosswind, so that the vehicle has certain anti-interference capability to the crosswind, and the operation stability of the vehicle running is improved.

Description

Intelligent vehicle crosswind-resistant control method based on fuzzy self-adaptive PID control
Technical Field
The invention relates to the technical field of intelligent control, in particular to an intelligent vehicle crosswind resistance control method based on fuzzy self-adaptive PID control.
Background
The intelligent vehicle is characterized in that advanced sensors (radar, camera), controllers, actuators and other devices are added on the basis of a common vehicle, intelligent information exchange with people, vehicles, roads and the like is realized through a vehicle-mounted sensing system and an information terminal, the vehicle has intelligent environment sensing capability, the running safety and dangerous states of the vehicle can be automatically analyzed, the vehicle can reach a destination according to the intention of people, and finally the purpose of operation by replacing people is realized.
The automobile is often interfered by crosswind in the high-speed running process, and the pneumatic lateral force and the pneumatic yaw moment caused by the crosswind can change the lateral deviation characteristic of tires, even cause the unstable steering such as 'sharp turning' and the like of the automobile, so that the automobile deviates from a preset running route, and sideslip or side turn can be caused, and the running safety of the automobile is seriously influenced. When the vehicle suddenly breaks into side interference, the driver can cause traffic accidents because of improper operation due to unskilled technology or unskilled reaction, and the crosswind response state of the vehicle running at high speed increases the operation burden of the driver if the driver is completely dependent on the adjustment of the driver, so that serious potential safety hazard exists. At present, the research on the control of the stability of the crosswind of the automobile is still in a starting stage, the existing research mainly focuses on the selection of the shape and the structure of the automobile body, and the active control research on the stability of the crosswind of the automobile is almost blank. In addition, the experimental sites specially used for the research of the stability of the crosswind of the automobile are very few. Under the background, the active control technology for researching the stability of the crosswind of the high-speed automobile is particularly important for improving the active safety of the automobile.
Disclosure of Invention
The invention provides an intelligent vehicle crosswind resisting control method based on fuzzy self-adaptive PID control, aiming at the problems in the background art, the fuzzy self-adaptive PID is used for controlling the vehicle when the vehicle is interfered by crosswind, so that the vehicle has certain anti-interference capability on the crosswind, and the driving control stability of the vehicle can be effectively improved.
In order to achieve the purpose, the invention adopts the technical scheme that:
a method for controlling anti-crosswind of an intelligent vehicle based on fuzzy self-adaptive PID control comprises the following steps:
(1) when the vehicle runs at high speed and is interfered by crosswind, carrying out route planning according to a steering wheel corner sensor and a velometer to obtain a target route of the vehicle;
(2) according to the current state (the yaw rate and the centroid slip angle) of the vehicle measured by the vehicle running state sensor, comparing the current state of the vehicle with a target route to obtain the yaw rate and the centroid slip angle error of the vehicle;
(3) obtaining the error change rate of the yaw angular velocity and the centroid slip angle of the vehicle according to the yaw angular velocity and the centroid slip angle error of the vehicle;
(4) determining the control quantity of the wheel angle of the vehicle based on a fuzzy self-adaptive PID controller according to the error and the error change rate;
(5) transmitting the wheel rotation angle control quantity to a steering motor controller so that the steering motor controller applies steering torque to a steering column according to the wheel rotation angle target value to complete vehicle steering, and therefore the lateral force of the tire is controlled;
(6) and repeating the steps to finish the vehicle crosswind control.
Further, on the basis of the anti-crosswind control method, an intelligent vehicle anti-crosswind control device based on fuzzy self-adaptive PID control is designed, and comprises the following control devices:
① comparing unit for planning route according to front wheel corner and vehicle speed to obtain ideal yaw rate and centroid side-slip angle of vehicle, then acting the executing mechanism on the vehicle model according to the executing unit to obtain actual vehicle state, and comparing with vehicle state of the ideal tracking model to obtain yaw rate and centroid side-slip angle error of vehicle;
② control unit including fuzzy controller, PID controller;
a fuzzy controller: three parameters K of the PID controller are obtained according to the deviation and the deviation change rate of the mass center slip angle and the yaw angular velocity obtained by the current state of the vehicle and the ideal state of the vehicleP、Ki、Kd；
A PID controller: for dependent on the output K of the fuzzy controllerP、Ki、KdObtaining the control quantity of the wheel rotation angle of the vehicle;
③ execution unit, which is used to send the wheel angle control quantity determined by the control unit to the steering motor controller, to make the steering motor controller apply the steering torque to the steering column according to the wheel angle control quantity to complete the wheel steering, to realize the crosswind control.
Further, said KP、Ki、KdThe calculation method comprises the following steps:
a. when the error e is larger, K is used for accelerating the response speed of the systemPAnd KdSet to a larger value; at the same time, KiThe smaller the response time, the larger overshoot of the system response can be avoided;
b. when error e and rate of change ecIn medium and medium time, K is used for reducing overshoot of system response and ensuring certain response speed of systemPTaking a smaller value; at the same time, KdTo a lesser extent, KiTaking a proper value;
c. when the error e is small, K is used for improving the steady-state performance of the control systemP，KiTaking a large value; at the same time, take an appropriate KdThe oscillation of the output response around a steady-state value is reduced, and the anti-interference capability of the control system is improved.
Further, the method for calculating the control amount of the wheel angle of the vehicle is:
Figure RE-GDA0002380091970000031
δr＝Kbδf+u(t)
wherein: u (t) is a feedback control quantity of the controller, KpIs a proportionality coefficient, KiIs the integral coefficient, KdE (t) is a yaw angular velocity at the moment t and a mass center slip angle error value; kbIs a feedforward proportional control coefficient; deltarIs the rear wheel steering angle; deltafIs the corner of the front wheel.
Compared with the prior art, the invention has the following beneficial effects:
1. automobiles are highly susceptible to crosswinds where air flow is very good, such as when traveling at high speeds, in tunnels, near the sea, etc., which can lead to rollover in severe cases. The invention adopts the fuzzy self-adaptive PID to control the vehicle when the vehicle is interfered by the crosswind, so that the vehicle has certain anti-interference capability to the crosswind, and the operation stability of the vehicle running is improved.
2. The traditional PID control has poor adaptive capacity and poor control effect on time-varying and nonlinear systems, and when system parameters change, the control performance can change greatly, which may cause system instability in serious cases. The fuzzy self-adaptive PID control system has the advantages of simple principle, convenient use, strong robustness, good flexibility and control precision.
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Other features, objects and advantages of the invention will become more apparent upon reading of the detailed description of non-limiting embodiments with reference to the following drawings:
FIG. 1 is a flow chart of an intelligent vehicle crosswind resistance control method based on fuzzy adaptive PID control.
FIG. 2 is a block diagram of a crosswind control system built under a Matlab/simulink compiling environment according to the present invention.
Fig. 3 is a view showing a simulation of yaw rate according to the present invention.
In the figure: 1-comparison unit, 2-control unit, 3-execution unit, 4-fuzzy controller, 5-PID controller, 6-vehicle ideal tracking model, 7-vehicle model, 8-steering wheel angle sensor, 9-velometer, 10-steering motor, 11-steering column.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
Referring to fig. 1, a method for controlling crosswind resistance of an intelligent vehicle based on fuzzy adaptive PID control comprises the following steps:
A. when the vehicle runs at high speed and is interfered by crosswind, the turning angle of the front wheel of the vehicle is obtained according to a steering wheel turning angle sensor (8), the actual running speed of the vehicle is obtained according to a velometer (9), so that the expected yaw angular speed and the centroid slip angle of an ideal tracking model (6) of the vehicle are obtained, and route planning is carried out on the basis of the expected yaw angular speed and the centroid slip angle, so that the target route of the vehicle is obtained;
B. comparing the current state of the vehicle with a target route according to the current state of a vehicle model (7) measured by a vehicle running state sensor to obtain the yaw rate and the centroid sideslip angle error of the vehicle;
C. obtaining a corresponding error change rate according to the yaw angular velocity and the centroid slip angle error of the vehicle;
D. according to the error and the error change rate, the fuzzy controller (4) obtains three parameters K of the PID controller (5)P、Ki、Kd；
E. Three parameters K of the PID controller (5) obtained based on the fuzzy controller (4)P、Ki、KdThe PID controller obtains and determines the wheel rotation angle control quantity;
F. and then the wheel rotation angle control quantity is sent to the steering motor (10), so that the steering motor (10) applies steering torque to the steering column (11) according to the wheel rotation angle control quantity to complete vehicle steering, and therefore the lateral force of the tire is controlled.
G. And D, repeating the steps A-F to finish the control of the side wind of the vehicle.
Furthermore, in the step D, three parameters K of the PID controller (5) are calculatedP、Ki、KdThe method comprises the following steps:
a. when the deviation e is large, K is used for accelerating the response speed of the systemPAnd KdSet to a larger value; at the same time, KiThe smaller the response time, the larger overshoot of the system response can be avoided;
b. when deviation e and rate of change ecIn medium and medium time, K is used for reducing overshoot of system response and ensuring certain response speed of systemPTaking a smaller value; at the same time, KdTo a lesser extent, KiTaking a proper value;
c. when the deviation e is small, K is used for improving the steady-state performance of the control systemP，KiTaking a large value; at the same time, take an appropriate KdReduce output response oscillation around steady state value and increaseAnd controlling the anti-interference capability of the system.
Further, in the step E, the method of calculating the required wheel angle control amount includes:
Figure RE-GDA0002380091970000051
δr＝Kbδf+u(t)
wherein: u (t) is a feedback control quantity of the controller, KpIs a proportionality coefficient, KiIs the integral coefficient, KdE (t) is a yaw angular velocity at the moment t and a mass center slip angle error value; kbIs a feedforward proportional control coefficient; deltarIs the rear wheel steering angle; deltafIs the corner of the front wheel.
Further, an intelligent vehicle crosswind resisting control device based on fuzzy self-adaptive PID control comprises: a comparison unit (1), a control unit (2) and an execution unit (3);
comparative unit (1): the route planning system is used for planning a route according to vehicle body states such as the corners of front wheels, the speed and the like to obtain an ideal yaw angular velocity and a centroid side slip angle of the vehicle; then acting the vehicle model (7) according to an actuating mechanism of the actuating unit (3) to obtain the actual vehicle body state, and comparing the actual vehicle body state with the vehicle body state of the vehicle ideal tracking model (1) to obtain the yaw angular velocity and the mass center slip angle error of the vehicle;
control unit (2): comprises a fuzzy controller (4) and a PID controller (5);
fuzzy controller (4): for obtaining three parameters K of the PID controller (5) according to the deviation and the deviation change rate of the current state (7) of the vehicle and the state of the vehicle body of the ideal tracking model (1) of the vehicleP、Ki、Kd；
PID controller (5): for determining the output K of the fuzzy controller (4)P、Ki、KdObtaining the control quantity of the wheel rotation angle of the vehicle;
execution unit (3): the control unit is used for sending the wheel rotation angle control quantity determined by the control unit (2) to the steering motor controller (10), so that the steering motor controller (10) applies steering torque to the steering column (11) according to the wheel rotation angle control quantity to complete wheel steering, and crosswind control is achieved.
Example (b):
the four-wheel steering system controller is designed by fuzzy adaptive PID control, and adverse effects on a running vehicle under crosswind interference are eliminated.
1. Contrast unit
1.1 ideal following model of vehicle
Establishing an ideal following model of the vehicle, adopting a linear two-degree-of-freedom front wheel steering vehicle model as the ideal following model, and calculating an ideal mass center slip angle and a yaw velocity of the vehicle;
1.1.1 vehicle ideal centroid cornering angle model
βideal＝Gβd□δf*(1)
Adding a first-order inertia link, correcting a centroid slip angle, wherein the ideal centroid slip angle is as follows:
Figure RE-GDA0002380091970000061
in the formula: gβdAs a proportionality coefficient, ideally, GβdTaking 0; t is tβIs an inertial link constant; deltaf *Is a front wheel corner;
1.1.2 model of ideal yaw rate of vehicle
Figure RE-GDA0002380091970000062
The maximum yaw rate at the tire adhesion limit is:
Figure RE-GDA0002380091970000063
adding a first-order inertia link, and correcting the yaw velocity, wherein the ideal yaw velocity is as follows:
Figure RE-GDA0002380091970000071
in the formula: l is the wheelbase;
Figure RE-GDA0002380091970000072
is a factor of automobile stability; deltaf *Is a front wheel corner; v. ofxIs the vehicle center speed; k is a radical offFront wheel cornering stiffness; k is a radical ofrIs rear wheel cornering stiffness; mu is the road surface adhesion coefficient; t is trIs an inertial link constant;
the state space expression of the automobile ideal following model can be obtained according to the expressions (2) and (5):
Figure RE-GDA0002380091970000073
in the formula:
Figure RE-GDA0002380091970000074
in the formula βdIs an ideal centroid slip angle; r isdAn ideal yaw rate; t is tβ、trThe inertia time constants are all inertia time constants, and the range of empirical values is generally 0.1-0.25; l is the wheelbase;
Figure RE-GDA0002380091970000075
is a factor of automobile stability; deltaf *Is a front wheel corner; v. ofxIs the vehicle center speed.
1.2 vehicle model
Establishing a two-degree-of-freedom automobile model based on the cross wind interference, and calculating the actual mass center slip angle and yaw angular velocity in the driving process of the automobile;
Figure RE-GDA0002380091970000076
in the formula: m is the mass of the automobile; v isThe running speed of the automobile, β is the centroid slip angle, r is the yaw rate of the automobile, YfAnd YrThe lateral bias force of the front wheel and the rear wheel; y iswAnd NwDisturbance force and yaw moment caused by cross wind respectively; i iszThe moment of inertia of the automobile around the center of gravity; lfAnd lrThe distances from the center of the automobile to the front and rear axles respectively;
it is derived as a state space equation according to equation (7):
Figure RE-GDA0002380091970000077
wherein the state variables are:
x＝[β r]T
wherein β is the centroid slip angle, r is the yaw rate
The control quantity is selected as follows:
u＝[δfδr]T
in the formula: deltafIs the angle of rotation of the front wheel, deltarTo the rear wheel turning angle
Figure RE-GDA0002380091970000081
In the formula: cfAnd CrThe angular stiffness coefficient of the front wheel and the rear wheel; m is the mass of the automobile; v is the vehicle speed; i iszThe moment of inertia of the automobile around the center of gravity; lwIs a force arm; a is the distance from the center of mass of the automobile to the front axle; and b is the distance from the mass center of the automobile to the rear axle.
2. Control unit
2.1 fuzzy adaptive PID controller
And a control method combining proportional feedforward and fuzzy adaptive PID feedback is adopted to actively control the rear wheel steering angle:
δr＝Kbδf+u (10)
in the formula: kbIs a feedforward proportional control coefficient; deltafIs a front wheel corner; u is a feedback control quantity of the fuzzy self-adaptive PID controller;
Figure RE-GDA0002380091970000082
in the formula: k is a radical offAnd krRespectively the tire side inclination rigidity of the front and rear wheels of the automobile; m is the mass of the automobile; v is the vehicle speed; a is the distance from the center of mass of the automobile to the front axle; and b is the distance from the mass center of the automobile to the rear axle.
2.2 fuzzy controller input output
The mass center slip angle and the yaw angular velocity output value of the vehicle model and the mass center slip angle and the error of the yaw angular velocity output value and the error change rate of the ideal following model of the vehicle are used as the input of the fuzzy controller;
with 3 adjustable parameters K of the PID controllerP、Ki、KdAs an output of the fuzzy controller.
2.3 membership function of fuzzy controller
The fuzzy sets of input and output are { NB NMNS ZO PS PM PB }, the input membership degree is a Gaussian function, the output membership degree function is a trigonometric function, and the ranges of setting errors and error change rates are [ -1.2, 1.2 [ -1.2 [ ]]Output KP、KdAll the discourse ranges are [ -3, 3 [)]Output KiThe universe of discourse is [ -0.6,0.6]。
2.4 fuzzy control rules
When the deviation e is large, K is used for accelerating the response speed of the systemPAnd KdSet to a larger value; at the same time, KiThe smaller the response time, the larger overshoot of the system response can be avoided;
when deviation e and rate of change ecIn medium and medium time, K is used for reducing overshoot of system response and ensuring certain response speed of systemPTaking a smaller value; at the same time, KdTo a lesser extent, KiTaking a proper value;
when the deviation e is small, K is used for improving the steady-state performance of the control systemP，KiTaking a large value; at the same time, take an appropriate KdThe oscillation of the output response in the vicinity of a steady-state value is reduced, and the anti-interference capability of a control system is improved;
fuzzy reasoning is carried out by adopting a Mamdani method, and defuzzification is carried out by utilizing a gravity center method to adjust output KP、Ki、KdAnd a fuzzy rule table is established as shown in table 1.
TABLE 1KP、Ki、KdFuzzy rule of
Figure RE-GDA0002380091970000091
2.5PID controller
Figure RE-GDA0002380091970000092
Wherein: u (t) is a feedback control quantity, KpIs a proportionality coefficient, KiIs the integral coefficient, KdAnd e (t) is the yaw rate at the time t and the centroid slip angle error value.
3. Execution unit
And finally, the wheel rotation angle control quantity is sent to a steering motor, so that the steering motor applies steering torque to a steering column according to the wheel rotation angle control quantity to complete vehicle steering, and therefore the lateral force of the tire is controlled.
4. Simulation analysis
4.1 in a Matlab/Simulink environment, constructing a four-wheel steering system simulation model based on crosswind interference, and making the following assumptions in the process of establishing a four-wheel steering automobile dynamic model:
(1) the running speed v of the automobile is kept unchanged;
(2) the vehicle is symmetrical about a longitudinal axis;
(3) neglecting vertical motion and pitching motion of the automobile;
(4) neglecting air resistance;
(5) the roll axis of the vehicle body is fixed;
(6) the suspension is simplified to an equivalent damper and anti-roll spring.
4.2 simulation parameters
The relevant vehicle parameters were determined with reference to Changan car as shown in Table 2.
TABLE 2 vehicle parameters
Vehicle parameters (parameter name) Magnitude of the value
The mass of the whole vehicle is m/kg 1050
Moment of inertia I about the x-axisx/kg·m2 1500
Moment of inertia about z-axis Iz/kg·m2 1500
Distance l from center of automobile to front axlef/m 1.105
Distance l from center of automobile to rear axler/m 1.345
Front wheel angular stiffness coefficient CfN/rad 33020
Rear wheel angle stiffness coefficient CrN/rad 55830
Constant t of inertial elementβ 0.1
Constant t of inertial elementr 0.1
Wheelbase L/m 2.5
Front wheel tire sidewall deflection stiffness kf/N/m-1 -55000
Rear wheel tire sidewall deflection stiffness kr/N/m-1 -45000
Vehicle speed v/km/h 100
4.3 simulation results analysis
Selecting a typical double-moving-line test to verify the effectiveness of a control strategy, and setting the speed of the vehicle to be 100km/h and the road adhesion coefficient
Figure RE-GDA0002380091970000111
The corner input of a reference front wheel is an angle step signal, the peak value is 2 degrees, the constant value unilateral crosswind is achieved, the wind speed is 15m/s, and the total simulation time is 5 s. Fig. 3 is a comparison graph of response curves of the yaw rate of the vehicle during the PID control and the fuzzy adaptive PID control under the disturbance of the side wind, from which it can be seen that the yaw rate is closer to the yaw rate of the reference model based on the anti-side wind method of the fuzzy adaptive PID control when the front wheel angle is the step input.
The technical means disclosed in the invention scheme are not limited to the technical means disclosed in the above embodiments, but also include the technical scheme formed by any combination of the above technical features. All other embodiments, which can be obtained by a person skilled in the art without any inventive step based on the embodiments of the present invention, are within the scope of the present invention.

Claims (4)

1. An intelligent vehicle crosswind-resistant control method based on fuzzy self-adaptive PID control is characterized by comprising the following steps:
(1) when the vehicle runs at high speed and is interfered by crosswind, carrying out route planning according to a steering wheel corner sensor and a velometer to obtain a target route of the vehicle;
(2) comparing the current state of the vehicle with a target route according to the current state of the vehicle measured by a vehicle running state sensor to obtain the yaw angular speed and the centroid sideslip angle error of the vehicle;
(3) obtaining the error change rate of the yaw angular velocity and the centroid slip angle of the vehicle according to the yaw angular velocity and the centroid slip angle error of the vehicle;
(4) determining the control quantity of the wheel angle of the vehicle based on a fuzzy self-adaptive PID controller according to the error and the error change rate;
(5) transmitting the wheel rotation angle control quantity to a steering motor controller so that the steering motor controller applies steering torque to a steering column according to the wheel rotation angle target value to complete vehicle steering, and therefore the lateral force of the tire is controlled;
(6) and repeating the steps to finish the vehicle crosswind control.
2. The device of the intelligent vehicle crosswind resisting control method based on the fuzzy self-adaptive PID control as claimed in claim 1, characterized in that it comprises the following control devices:
① comparing unit for planning route according to front wheel corner and vehicle speed to obtain ideal yaw rate and centroid side-slip angle of vehicle, then acting the executing mechanism on the vehicle model according to the executing unit to obtain actual vehicle state, and comparing with vehicle state of the ideal tracking model to obtain yaw rate and centroid side-slip angle error of vehicle;
② control unit including fuzzy controller, PID controller;
a fuzzy controller: for obtaining according to the current state of the vehicle and the ideal state of the vehicleThe deviation and the deviation change rate of the centroid slip angle and the yaw angular velocity are obtained to obtain three parameters K of the PID controllerP、Ki、Kd；
A PID controller: for dependent on the output K of the fuzzy controllerP、Ki、KdObtaining the control quantity of the wheel rotation angle of the vehicle;
③ execution unit, which is used to send the wheel angle control quantity determined by the control unit to the steering motor controller, to make the steering motor controller apply the steering torque to the steering column according to the wheel angle control quantity to complete the wheel steering, to realize the crosswind control.
3. The intelligent vehicle crosswind-resistant control method based on fuzzy adaptive PID control as claimed in claim 2, characterized in that, the K isP、Ki、KdThe calculation method comprises the following steps:
a. when the error e is larger, K is used for accelerating the response speed of the systemPAnd KdSet to a larger value; at the same time, KiThe smaller the response time, the larger overshoot of the system response can be avoided;
b. when error e and rate of change ecIn medium and medium time, K is used for reducing overshoot of system response and ensuring certain response speed of systemPTaking a smaller value; at the same time, KdTo a lesser extent, KiTaking a proper value;
c. when the error e is small, K is used for improving the steady-state performance of the control systemP，KiTaking a large value; at the same time, take an appropriate KdThe oscillation of the output response around a steady-state value is reduced, and the anti-interference capability of the control system is improved.
4. The intelligent vehicle crosswind-resistant control method based on the fuzzy adaptive PID control as claimed in claim 3, characterized in that the control quantity calculating method of the vehicle wheel turning angle is:
Figure FDA0002208389790000021
δr＝Kbδf+u(t)
wherein: u (t) is a feedback control quantity of the controller, KpIs a proportionality coefficient, KiIs the integral coefficient, KdE (t) is a yaw angular velocity at the moment t and a mass center slip angle error value; kbIs a feedforward proportional control coefficient; deltarIs the rear wheel steering angle; deltafIs the corner of the front wheel.
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CN111645653A (en) * 2020-05-12 2020-09-11 摩登汽车(盐城)有限公司 Method and system for correcting vehicle crosswind deviation
CN113359741A (en) * 2021-06-18 2021-09-07 青岛农业大学 Automatic row guiding system of corn combine harvester
CN114852171A (en) * 2022-04-25 2022-08-05 上海仙途智能科技有限公司 Vehicle and steering control method and device thereof, storage medium and terminal
CN115035000A (en) * 2022-08-10 2022-09-09 山东国晟环境科技有限公司 Road raise dust image identification method and system

Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103112452A (en) * 2011-10-11 2013-05-22 现代摩比斯株式会社 Method for compensating side-wind based on camera sensor of lkas in motor-driven power steering
CN104076687A (en) * 2014-06-04 2014-10-01 江苏大学 Decoupling control method for active suspension and electric power steering integration system
DE102016204512A1 (en) * 2015-03-20 2016-09-22 Continental Teves Ag & Co. Ohg Device and method for stability control of a vehicle when leaving a lane
CN108146430A (en) * 2017-11-08 2018-06-12 南京航空航天大学 A kind of Active suspension and active steering integrated system and its robust control method

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103112452A (en) * 2011-10-11 2013-05-22 现代摩比斯株式会社 Method for compensating side-wind based on camera sensor of lkas in motor-driven power steering
CN104076687A (en) * 2014-06-04 2014-10-01 江苏大学 Decoupling control method for active suspension and electric power steering integration system
DE102016204512A1 (en) * 2015-03-20 2016-09-22 Continental Teves Ag & Co. Ohg Device and method for stability control of a vehicle when leaving a lane
CN108146430A (en) * 2017-11-08 2018-06-12 南京航空航天大学 A kind of Active suspension and active steering integrated system and its robust control method

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
周鹏: "四轮转向汽车的操纵稳定性控制策略研究", 《中国优秀硕士学位论文全文数据库 工程科技Ⅱ辑》 *

Cited By (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111645653A (en) * 2020-05-12 2020-09-11 摩登汽车(盐城)有限公司 Method and system for correcting vehicle crosswind deviation
CN111645653B (en) * 2020-05-12 2021-08-31 摩登汽车(盐城)有限公司 Method and system for correcting vehicle crosswind deviation
CN111618861A (en) * 2020-06-12 2020-09-04 重庆科技学院 Double-follow-up intelligent arm control method based on four-axis structure
CN113359741A (en) * 2021-06-18 2021-09-07 青岛农业大学 Automatic row guiding system of corn combine harvester
CN114852171A (en) * 2022-04-25 2022-08-05 上海仙途智能科技有限公司 Vehicle and steering control method and device thereof, storage medium and terminal
CN114852171B (en) * 2022-04-25 2023-08-15 上海仙途智能科技有限公司 Vehicle, steering control method and device thereof, storage medium and terminal
CN115035000A (en) * 2022-08-10 2022-09-09 山东国晟环境科技有限公司 Road raise dust image identification method and system

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