CN113335376B - Inter-wheel differential anti-dragging control method for steering of distributed independent driving electric automobile - Google Patents
Inter-wheel differential anti-dragging control method for steering of distributed independent driving electric automobile Download PDFInfo
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- CN113335376B CN113335376B CN202110718145.5A CN202110718145A CN113335376B CN 113335376 B CN113335376 B CN 113335376B CN 202110718145 A CN202110718145 A CN 202110718145A CN 113335376 B CN113335376 B CN 113335376B
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B62—LAND VEHICLES FOR TRAVELLING OTHERWISE THAN ON RAILS
- B62D—MOTOR VEHICLES; TRAILERS
- B62D5/00—Power-assisted or power-driven steering
- B62D5/04—Power-assisted or power-driven steering electrical, e.g. using an electric servo-motor connected to, or forming part of, the steering gear
- B62D5/0418—Electric motor acting on road wheel carriers
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B62—LAND VEHICLES FOR TRAVELLING OTHERWISE THAN ON RAILS
- B62D—MOTOR VEHICLES; TRAILERS
- B62D5/00—Power-assisted or power-driven steering
- B62D5/04—Power-assisted or power-driven steering electrical, e.g. using an electric servo-motor connected to, or forming part of, the steering gear
- B62D5/0457—Power-assisted or power-driven steering electrical, e.g. using an electric servo-motor connected to, or forming part of, the steering gear characterised by control features of the drive means as such
- B62D5/046—Controlling the motor
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B62—LAND VEHICLES FOR TRAVELLING OTHERWISE THAN ON RAILS
- B62D—MOTOR VEHICLES; TRAILERS
- B62D6/00—Arrangements for automatically controlling steering depending on driving conditions sensed and responded to, e.g. control circuits
- B62D6/001—Arrangements for automatically controlling steering depending on driving conditions sensed and responded to, e.g. control circuits the torque NOT being among the input parameters
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
- Y02T10/00—Road transport of goods or passengers
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- Y02T10/72—Electric energy management in electromobility
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Abstract
The invention discloses a differential anti-dragging control method for a steering time differential of a distributed independent driving electric automobile, which is characterized by comprising the following steps of: s1, acquiring the wheel speed of a driving wheel when an automobile turns; s2, taking the wheel speed of one driving wheel as a reference wheel speed, and reversely pushing the decision expected wheel speeds of the other driving wheels according to an Ackermann steering model; s3, controlling the output torque of each motor driving the wheels by a PI controller, wherein the input of the PI controller is the difference value between the real-time wheel speed of the driving wheels and the decision expected wheel speed, the output of the PI controller is the output torque coefficient of the motor, the proportion parameter of the PI controller is preset, and the integral parameter of the PI controller is controlled by a fuzzy controller; the output of the fuzzy controller is an integral parameter, and the input of the fuzzy controller is the tire pressure change rate of the driving wheel and the yaw rate change rate of the vehicle. According to the invention, through the combination of the PI controller and the fuzzy control, the wheel speed of each wheel is controlled by the tire pressure change rate of the driving wheel and the yaw rate change rate of the vehicle, so that the stability of the vehicle is ensured.
Description
Technical Field
The invention relates to a wheel speed control method of an electric automobile, in particular to a differential anti-dragging control method between wheels when the electric automobile is driven in a distributed independent mode.
Background
In the ideal state, the relation between the turning radius of each wheel and the rotation speed of the driving wheel can be used for distributing driving moment through a mechanical differential mechanism for the traditional vehicle and realizing the differential function. For a distributed independently driven electric wheel car, the rotation speed and torque control of the two side driving wheels is more complex and has flexibility of rotation speed and torque distribution due to the lack of constraint of a mechanical differential mechanism. The four wheels are driven independently and freely, and even under the condition of completed wheel speed control planning, the phenomenon of dragging among wheels is still unavoidable. Once the phenomenon of dragging among wheels occurs, the slip rate of a single tire and the cornering stiffness of the tire deviate from the original estimated range, so that the original wheel speed control scheme or the torque output control method is deviated, and the running stability of the whole vehicle is caused.
Disclosure of Invention
Aiming at the defects in the prior art, the invention aims to provide a differential anti-dragging control method between wheels during steering of a distributed independent driving electric automobile, which can prevent dragging phenomenon between wheels and ensure the running stability of the automobile.
The technical scheme of the invention is as follows: a differential anti-dragging control method for a steering time wheel of a distributed independent driving electric automobile comprises the following steps:
s1, acquiring the wheel speed of a driving wheel when an automobile turns;
s2, taking the wheel speed of one driving wheel as a reference wheel speed, and reversely pushing the decision expected wheel speed of the driving wheel according to an Ackermann steering model;
s3, controlling the output torque of each motor driving the wheels by a PI controller, wherein the input of the PI controller is the difference value between the real-time wheel speed of the driving wheels and the decision expected wheel speed, the output of the PI controller is the output torque coefficient of the motor, the proportion parameter of the PI controller is preset, and the integral parameter of the PI controller is controlled by a fuzzy controller; the output of the fuzzy controller is an integral parameter, and the input of the fuzzy controller is the tire pressure change rate of the driving wheel and the yaw rate change rate of the vehicle.
Further, in the step S2, when the wheel speed of one driving wheel is taken as the reference wheel speed, the wheel speed change rate of each driving wheel is calculated, and the real-time wheel speed of the driving wheel with the minimum wheel speed change rate of all driving wheels is taken as the reference wheel speed.
Further, in the step S2, a plurality of time periods are set, in each time period, the wheel speed of one driving wheel is taken as a reference wheel speed, the expected wheel speed of the driving wheel is reversely pushed according to the Ackermann steering model, and the average value of the expected wheel speeds of the time periods is taken as the decision expected wheel speed of the driving wheel.
Further, the real-time wheel speed of the driving wheel with the smallest wheel speed change rate in all the driving wheels is taken as the reference wheel speed in each time period.
Further, the driving wheel air pressure change rate, the vehicle yaw rate change rate, and the integration parameter are all set to five fuzzy sets.
Further, the fuzzy sets of the tire pressure change rate of the driving wheel are set as P1, P2, P3, P4 and P5, the fuzzy sets of the yaw rate change rate of the vehicle are set as A1, A2, A3, A4 and A5, the fuzzy sets of the integral parameters are set as I1, I2, I3, I4 and I5, and the fuzzy inference rules of the integral parameters are as follows: when the tire pressure change rate of the driving wheel is P1 and the vehicle yaw rate change rate is A1, A2, or A3, the integral parameter is I3; when the tire pressure change rate of the driving wheel is P1 and the yaw rate change rate of the vehicle is A4, the integral parameter is I4; when the tire pressure change rate of the driving wheel is P1 and the yaw rate change rate of the vehicle is A5, the integral parameter is I5; when the tire pressure change rate of the driving wheel is P2 and the yaw rate change rate of the vehicle is A1, the integral parameter is I2; when the tire pressure change rate of the driving wheel is P2 and the vehicle yaw rate change rate is A2 or A3, the integral parameter is I3; when the tire pressure change rate of the driving wheel is P2 and the vehicle yaw rate change rate is A4 or A5, the integral parameter is I5; when the tire pressure change rate of the driving wheel is P3 and the vehicle yaw rate change rate is A1 or A2, the integral parameter is I2; when the tire pressure change rate of the driving wheel is P3 and the yaw rate change rate of the vehicle is A3, the integral parameter is I3; when the tire pressure change rate of the driving wheel is P3 and the yaw rate change rate of the vehicle is A4, the integral parameter is I4; when the tire pressure change rate of the driving wheel is P3 and the yaw rate change rate of the vehicle is A5, the integral parameter is I5; when the tire pressure change rate of the driving wheel is P4 or P5 and the vehicle yaw rate change rate is A1, the integral parameter is I1; when the tire pressure change rate of the driving wheel is P4 or P5 and the vehicle yaw rate change rate is A2, the integral parameter is I2; when the tire pressure change rate of the driving wheel is P4 or P5 and the vehicle yaw rate change rate is A3, the integral parameter is I3; when the tire pressure change rate of the driving wheel is P4 and the vehicle yaw rate change rate is A4 or A5, the integral parameter is I4; when the tire pressure change rate of the driving wheel is P5 and the yaw rate change rate of the vehicle is A4, the integral parameter is I3; when the driving wheel air pressure change rate is P5 and the vehicle yaw rate change rate is A5, the integral parameter is I4.
Further, the domain of the tire pressure change rate of the driving wheel is set as [ -1,4], the membership function of the fuzzy set P1 is a triangle function, the domain of the corresponding tire pressure change rate of the driving wheel is set as [ -1,0], the membership of the driving wheel P1 is 1 when the tire pressure change rate of the driving wheel is-1, and the membership of the driving wheel P1 is 0 when the tire pressure change rate of the driving wheel is 0; the membership function of the fuzzy set P2 is a triangle function, and the discourse domain of the corresponding tire pressure change rate of the driving wheel is [ -0.5,0.5]; the membership function of the fuzzy set P3 is a triangle function, and the discourse domain of the corresponding driving wheel tire pressure change rate is [0,2]; the membership function of the fuzzy set P4 is a triangle function, and the discourse domain of the corresponding driving wheel tire pressure change rate is [1,4]; the membership function of the fuzzy set P5 is a trapezoidal function, the discourse domain of the corresponding tire pressure change rate of the driving wheel is [2.5,5], the membership of the driving wheel P5 is 0 when the tire pressure change rate of the driving wheel is 2.5, and the membership of the driving wheel P5 is 1 when the tire pressure change rate of the driving wheel is [3,4 ].
Further, the domain of the change rate of the yaw rate of the vehicle is set as [ -8,8], the membership function of the fuzzy set A1 is a trapezoidal function, the domain of the change rate of the yaw rate of the corresponding vehicle is [ -8, -3], the membership of A1 is 1 when the change rate of the yaw rate of the vehicle is [ -8, -7], and the membership of A1 is 0 when the change rate of the yaw rate of the vehicle is-3; the membership function of the fuzzy set A2 is a triangle function, and the discourse domain of the corresponding change rate of the yaw rate of the vehicle is [ -5, -1]; the membership function of the fuzzy set A3 is a triangle function, and the discourse domain of the corresponding vehicle yaw rate change rate is [ -3,3]; the membership function of the fuzzy set A4 is a triangle function, and the corresponding domain of the change rate of the yaw rate of the vehicle is [1,5]; the membership function of the fuzzy set A5 is a trapezoidal function, the discourse domain of the corresponding vehicle yaw rate change rate is [3,8], the A5 membership is 0 when the driving wheel tire pressure change rate is 3, and the A5 membership is 1 when the vehicle yaw rate change rate is [7,8 ].
Further, the domain of the integral parameter is set as [0.8,1.2], the membership functions of the fuzzy sets I1, I2, I3, I4 and I5 are triangle functions, the domain of the integral parameter corresponding to the fuzzy set I1 is [0.8,0.85], the membership of the I1 is 1 when the integral parameter is 0.8, and the membership of the I1 is 0 when the integral parameter is 0.85; the domain of the integral parameter corresponding to the fuzzy set I2 is [0.8,0.9]; the domain of the integral parameter corresponding to the fuzzy set I3 is [0.85,1.15], the domain of the integral parameter corresponding to the fuzzy set I4 is [1.1,1.2], the domain of the integral parameter corresponding to the fuzzy set I5 is [1.15,1.2], the degree of membership of I5 is 0 when the integral parameter is 1.15, and the degree of membership of I5 is 1 when the integral parameter is 1.2.
Further, when the fuzzy controller outputs the integral parameters, an accurate value is obtained by adopting a maximum membership method according to the fuzzy set of the integral parameters.
Compared with the prior art, the invention has the advantages that:
the output of the PI controller is proportional to the integral of the input error signal. The method is mainly used for eliminating static difference and improving the no-difference degree of a system (the system refers to a controlled vehicle hub motor in the invention). The intensity of the integration depends on the integration time constant T, the greater the T, the weaker the integration and vice versa.
In terms of the present invention, in practical applications, the wheel speed adjustment of the vehicle is performed in consideration of the actual vehicle structural parameters, that is, parameters of the controlled system (the system refers to the controlled vehicle in-wheel motor in the present invention) do not change significantly with time, and the parameter change of the PI controller is mainly adjusted according to the time-varying signals input by the system (that is, signals changing with time t, steering wheel angle and pedal force input by the driver in the present invention, and system state variables, that refers to the yaw rate change rate, and the tire pressure change rate), so that steady-state errors changing with time easily occur, resulting in vibration of the system output around the actual expectations, thereby affecting the control expectations. The steady-state error of the system is adjusted in real time through the parameter self-tuning (fuzzy algorithm), namely the parameter I is adjusted timely, the system error is just relieved, and the method is particularly important in the aspect of realizing the system stability.
Drawings
Fig. 1 is a schematic flow chart of a differential anti-dragging control method for steering time of a distributed independent driving electric automobile.
FIG. 2 is a schematic representation of an Ackermann steering model.
FIG. 3 is a graph showing membership function of tire pressure change rate of a driving wheel.
FIG. 4 is a graphical illustration of membership functions for the rate of change of vehicle yaw rate.
Fig. 5 is a membership function diagram of the integration parameter I.
Detailed Description
The invention is further illustrated, but is not limited, by the following examples.
Referring to fig. 1, the method for controlling differential anti-dragging between wheels of a distributed independent driving electric automobile in steering according to the embodiment includes the following steps:
s1, acquiring the wheel speed of a driving wheel when an automobile turns; the invention adopts an absolute value encoder, and the distributed independent driving electric automobile is assumed to be four-wheel driving, and the four-wheel hub motor encoder obtains real-time four-wheel vehicle speed. Information such as position, angle, and number of turns is provided in angular increments, each angular increment being assigned a unique code. Taking a fixed step length t and solving the last moment t 1 Angle theta of (2) 1 And the next time t 2 Angle theta of (2) 2 The deviation increment delta theta between step sizes, and the real-time angular speed is omega;
wherein, the liquid crystal display device comprises a liquid crystal display device,
Δθ＝θ 2 -θ 1
s2, reversely pushing expected wheel speeds of the other driving wheels according to an Ackermann steering model by taking the wheel speed of one driving wheel as a reference wheel speed;
calculating four wheels by combining real-time angular velocity omega with wheel speed at last momentThe real-time change rate of the wheel speed is set to be a fixed value delta t, and the wheel speed at the last moment is omega 1 The wheel speed at the next moment is omega 2 The real-time wheel speed change rate alpha is drawn;
thereby obtaining the real-time wheel speed change rates of four wheels, which are respectively alpha i ，i＝1，2，3，4。
In the process of steering the vehicle, according to an Ackermann steering model shown in fig. 2, the steering angle of the inner front wheel and the outer front wheel is delta in And delta out The steering angle of the vehicle is delta, L and W are the wheelbase and the wheel track of the vehicle respectively, R 0 For turning radius of vehicle, R in And R is out The steering radiuses of the inner driving wheel and the outer driving wheel are respectively v is the running speed of the vehicle, namely the speed of the middle point of the rear wheel axis around the steering center O.
According to the Ackermann vehicle steering model, the motion state of the electric automobile can be analyzed. In the ackerman steering geometric relationship, the circle center of the path of each wheel is approximately intersected with the instantaneous steering center of the extension line of the rear axle, so that the steering is smoother, and the following relationship can be obtained:
R 0 ＝L/tanδ (1)
r 0 ＝L/sinδ (2)
R in ＝R 0 -W/2 (3)
R out ＝R 0 +W/2 (4)
at this time, the rotational speed relationship of the driving wheels at both sides is:
ω＝v/R 0 (7)
v rin ＝ωL/tanδ in (8)
v rout ＝ωL/tanδ out (9)
v fin ＝ωL/sinδ in (10)
v fout ＝ωL/sinδ out (11)
the minimum change rate of the wheel speed is considered, which means that the wheel speed is the minimum relative sliding with the ground and is closest to the stable wheel speed.
Wheel speed omega with minimum wheel speed change rate ξ1 Four-wheel expected wheel speed for ensuring vehicle stability by reversely pushing according to Ackerman steering model as reference wheel speed
The expected wheel speed of the driving wheel with the minimum wheel speed change rate is the wheel speed of the driving wheel.
For example, if the rate of change of the right rear wheel speed is the smallest in the current vehicle condition, the right rear wheel speed is used
The expected speed of the other three wheels is derived according to the reference wheel speed, and the specific formula is as follows
The fixed step length delta t is drawn up, the calculation is repeated, and the wheel speed omega with the minimum wheel speed change rate at the next moment is obtained ξ Further calculating the expected wheel speed of four wheels at the next moment; repeating the steps, calculating a plurality of (3, 4 times) different expected wheel speeds in a shorter time period,taking the average value as the decision expected wheel speed
S3, controlling output torque of each motor driving the wheels by a PI controller, wherein the input of the PI controller is a difference value between the real-time wheel speed of the driving wheels and the decision expected wheel speed, the output of the PI controller is an output torque coefficient of the motor, and the integral parameter of the PI controller is controlled by a fuzzy controller.
Compared with the traditional centralized driving automobile, the distributed independent driving electric automobile omits the traditional transmission devices such as a clutch, a speed reducer, a gearbox, a differential mechanism and the like. Each driving vehicle is directly driven to rotate by a corresponding motor, and according to the wheel speed estimated in real time, the output torque of an independent wheel is adjusted by using a PI controller of an independent hub motor mcu as a target value so as to realize the estimated ideal vehicle speed. The difference between the real-time wheel speed and the expected wheel speed is reduced to ensure that the dragging coefficient gradually tends to be infinitesimal, and meanwhile, the stability of the whole vehicle is required to be ensured. The invention is realized by monitoring the change rate of the yaw rate of the vehicle and the change rate of the tire pressure of the tire.
The proportional parameter of the PI controller is preset, and the integral parameter of the PI controller is controlled by the fuzzy controller; the output of the fuzzy controller is an integral parameter, and the input of the fuzzy controller is the tire pressure change rate of the driving wheel and the yaw rate change rate of the vehicle.
Specifically, a yaw rate measurer of an ESP system arranged on a vehicle body is used for obtaining the real-time vehicle yaw rate, a fixed step length t is taken, and the time t is from the last time 1 Yaw rate α of (2) 1 And the next time t 2 Yaw rate α of (2) 2 Calculating the real-time yaw rate change rate alpha by the inter-step deviation increment delta alpha T 。
Wherein, the liquid crystal display device comprises a liquid crystal display device,
the tire pressure detecting device is installed in the inner hub, so that the cornering stiffness of the tire is deduced, and the cornering stiffness is transmitted into a can system through mcu in real time. Thereby calculating the real-time tire pressure change rate delta P.
Calculating the I parameter value by adopting a fuzzy algorithm, wherein the inputs of the fuzzy algorithm are the tire pressure change rate delta P and the real-time yaw rate change rate alpha T Rate of change. Considering that the tire pressure reflects the running state of each wheel of the vehicle in real time and is related to the real-time vehicle pose, the tire pressure change rate delta P is taken as the input of a controller; the yaw rate can be directly measured in real time by a yaw rate detector, and the invention considers that the yaw rate is an important parameter of the running state of the vehicle, and the change rate alpha of the real-time yaw rate T The rate of change is also used as an input to this controller. Setting the range of the tire air pressure change rate delta P to [ -1,4]The fuzzy sets are divided into 5 fuzzy sets: negative (P1), medium (P2), small positive (P3), medium (P4) and large positive (P5). Real-time yaw rate change rate alpha T The range of the domain of the change rate is set to be [ -8,8]The fuzzy sets are divided into 5 fuzzy sets: negative large (A1), negative small (A2), zero (A3), positive small (A4), and positive large (A5). The argument range of the I parameter value is set to [0.8,1.2]]The fuzzy sets are divided into 5 fuzzy sets: smaller than (I1), slightly smaller than (I2), approximately (I3), slightly larger than (I4), and larger than (I5). Through simulation analysis, the range of the value input by the fuzzy algorithm is equal to the range of the corresponding domain, so that the scale transformation is not needed, and the tire pressure change rate delta P and the real-time yaw rate change rate alpha are carried out according to the design membership function T Is blurred by the exact amount of (a).
As shown in fig. 3, the membership function of the fuzzy set P1 of the tire air pressure change rate Δp is a triangle function, the corresponding domain of the tire air pressure change rate of the driving wheel is [ -1,0], the membership of the driving wheel P1 is 1 when the tire air pressure change rate of the driving wheel is-1, and the membership of the driving wheel P1 is 0 when the tire air pressure change rate of the driving wheel is 0; membership functions of the fuzzy sets P2, P3 and P4 are triangle functions, the domain of the tire pressure change rate of the driving wheels corresponding to the fuzzy set P2 is [ -0.5,0.5], the domain of the tire pressure change rate of the driving wheels corresponding to the fuzzy set P3 is [0,2], and the domain of the tire pressure change rate of the driving wheels corresponding to the fuzzy set P4 is [1,4]; the membership function of the fuzzy set P5 is a trapezoidal function, the discourse domain of the corresponding tire pressure change rate of the driving wheel is [2.5,5], the membership of the driving wheel P5 is 0 when the tire pressure change rate of the driving wheel is 2.5, and the membership of the driving wheel P5 is 1 when the tire pressure change rate of the driving wheel is [3,4 ].
As shown in fig. 4, the real-time yaw rate of change α T The membership function of the fuzzy set A1 is a trapezoidal function, and the corresponding domain of the change rate of the yaw rate of the vehicle is [ -8, -3]The change rate of the yaw rate of the vehicle is [ -8, -7]When the A1 membership degree is 1, and when the change rate of the vehicle yaw rate is-3, the A1 membership degree is 0; membership functions of the fuzzy sets A2, A3 and A4 are triangle functions, and the discourse domain of the change rate of the yaw rate of the vehicle corresponding to the fuzzy set A2 is [ -5, -1]The discourse domain of the change rate of the yaw rate of the vehicle corresponding to the fuzzy set A3 is [ -3,3]The discourse domain of the change rate of the yaw rate of the vehicle corresponding to the fuzzy set A4 is [1,5]]The method comprises the steps of carrying out a first treatment on the surface of the Membership function of fuzzy set A5 is trapezoidal function, and corresponding discourse domain of vehicle yaw rate change rate is [3,8]]When the tire pressure change rate of the driving wheel is 3, the membership degree of A5 is 0, and the change rate of the vehicle yaw rate is [7,8]]The A5 membership was 1.
The fuzzy inference rules are shown in table 1 below.
The basic idea of the fuzzy inference rule is that: the greater the rate of change of yaw rate, the more unstable the vehicle tends to be, and the greater the I parameter value at that time; from the viewpoint of vehicle acceleration safety, the larger the tire pressure variation value, the more the magnitude of the longitudinal acceleration needs to be suppressed, and the smaller the I parameter value. Referring to fig. 5, membership functions of fuzzy sets I1, I2, I3, I4 and I5 of the integral parameter I are triangle functions, the domain of integral parameter corresponding to the fuzzy set I1 is [0.8,0.85], the membership of I1 is 1 when the integral parameter is 0.8, and the membership of I1 is 0 when the integral parameter is 0.85; the domain of the integral parameter corresponding to the fuzzy set I2 is [0.8,0.9]; the domain of integral parameters corresponding to the fuzzy set I3 is [0.85,1.15], the domain of integral parameters corresponding to the fuzzy set I4 is [1.1,1.2], the domain of integral parameters corresponding to the fuzzy set I5 is [1.15,1.2], the degree of membership of I5 is 0 when the integral parameters are 1.15, and the degree of membership of I5 is 1 when the integral parameters are 1.2. And determining a fuzzy set of the I parameters through a fuzzy rule, obtaining an I parameter accurate result by adopting a maximum membership method to finish parameter determination, and finally controlling the output of the hub motor by a PI controller.
Claims (6)
1. The differential anti-dragging control method for the steering time of the distributed independent driving electric automobile is characterized by comprising the following steps of:
s1, acquiring the wheel speed of a driving wheel when an automobile turns;
s2, setting a plurality of time periods, wherein in each time period, the real-time wheel speed of the driving wheel with the minimum wheel speed change rate in all driving wheels is used as a reference wheel speed, the expected wheel speed of the driving wheel is reversely pushed according to an Ackermann steering model, and the average value of the expected wheel speeds of the time periods is used as a decision expected wheel speed of the driving wheel;
s3, controlling the output torque of each motor driving the wheels by a PI controller, wherein the input of the PI controller is the difference value between the real-time wheel speed of the driving wheels and the decision expected wheel speed, the output of the PI controller is the output torque coefficient of the motor, the proportion parameter of the PI controller is preset, and the integral parameter of the PI controller is controlled by a fuzzy controller; the output of the fuzzy controller is an integral parameter, the input of the fuzzy controller is a driving wheel tire pressure change rate and a vehicle yaw rate change rate, and the driving wheel tire pressure change rate, the vehicle yaw rate change rate and the integral parameter are all set to be five fuzzy sets.
2. The distributed independent driving electric vehicle steering time differential anti-drag control method according to claim 1, wherein the fuzzy sets of the tire pressure change rate of the driving wheel are set as P1, P2, P3, P4 and P5, the fuzzy sets of the vehicle yaw rate change rate are set as A1, A2, A3, A4 and A5, the fuzzy sets of the integral parameters are set as I1, I2, I3, I4 and I5, and the fuzzy reasoning rule of the integral parameters is: when the tire pressure change rate of the driving wheel is P1 and the vehicle yaw rate change rate is A1, A2, or A3, the integral parameter is I3; when the tire pressure change rate of the driving wheel is P1 and the yaw rate change rate of the vehicle is A4, the integral parameter is I4; when the tire pressure change rate of the driving wheel is P1 and the yaw rate change rate of the vehicle is A5, the integral parameter is I5; when the tire pressure change rate of the driving wheel is P2 and the yaw rate change rate of the vehicle is A1, the integral parameter is I2; when the tire pressure change rate of the driving wheel is P2 and the vehicle yaw rate change rate is A2 or A3, the integral parameter is I3; when the tire pressure change rate of the driving wheel is P2 and the vehicle yaw rate change rate is A4 or A5, the integral parameter is I5; when the tire pressure change rate of the driving wheel is P3 and the vehicle yaw rate change rate is A1 or A2, the integral parameter is I2; when the tire pressure change rate of the driving wheel is P3 and the yaw rate change rate of the vehicle is A3, the integral parameter is I3; when the tire pressure change rate of the driving wheel is P3 and the yaw rate change rate of the vehicle is A4, the integral parameter is I4; when the tire pressure change rate of the driving wheel is P3 and the yaw rate change rate of the vehicle is A5, the integral parameter is I5; when the tire pressure change rate of the driving wheel is P4 or P5 and the vehicle yaw rate change rate is A1, the integral parameter is I1; when the tire pressure change rate of the driving wheel is P4 or P5 and the vehicle yaw rate change rate is A2, the integral parameter is I2; when the tire pressure change rate of the driving wheel is P4 or P5 and the vehicle yaw rate change rate is A3, the integral parameter is I3; when the tire pressure change rate of the driving wheel is P4 and the vehicle yaw rate change rate is A4 or A5, the integral parameter is I4; when the tire pressure change rate of the driving wheel is P5 and the yaw rate change rate of the vehicle is A4, the integral parameter is I3; when the driving wheel air pressure change rate is P5 and the vehicle yaw rate change rate is A5, the integral parameter is I4.
3. The distributed independent driving electric automobile steering time differential anti-dragging control method according to claim 1, wherein the domain of the tire pressure change rate of the driving wheels is set to be [ -1,4], the membership function of the fuzzy set P1 is a triangle function, the domain of the corresponding tire pressure change rate of the driving wheels is [ -1,0], the membership of the driving wheels P1 is 1 when the tire pressure change rate of the driving wheels is-1, and the membership of the driving wheels P1 is 0 when the tire pressure change rate of the driving wheels is 0; the membership function of the fuzzy set P2 is a triangle function, and the discourse domain of the corresponding tire pressure change rate of the driving wheel is [ -0.5,0.5]; the membership function of the fuzzy set P3 is a triangle function, and the discourse domain of the corresponding driving wheel tire pressure change rate is [0,2]; the membership function of the fuzzy set P4 is a triangle function, and the discourse domain of the corresponding driving wheel tire pressure change rate is [1,4]; the membership function of the fuzzy set P5 is a trapezoidal function, the discourse domain of the corresponding tire pressure change rate of the driving wheel is [2.5,5], the membership of the driving wheel P5 is 0 when the tire pressure change rate of the driving wheel is 2.5, and the membership of the driving wheel P5 is 1 when the tire pressure change rate of the driving wheel is [3,4 ].
4. The distributed independent driving electric vehicle steering time wheel differential anti-dragging control method according to claim 1, wherein the domain of the change rate of the yaw rate of the vehicle is [ -8,8], the membership function of the fuzzy set A1 is a trapezoidal function, the domain of the change rate of the yaw rate of the corresponding vehicle is [ -8, -3], the membership of A1 is 1 when the change rate of the yaw rate of the vehicle is [ -8, -7], and the membership of A1 is 0 when the change rate of the yaw rate of the vehicle is-3; the membership function of the fuzzy set A2 is a triangle function, and the discourse domain of the corresponding change rate of the yaw rate of the vehicle is [ -5, -1]; the membership function of the fuzzy set A3 is a triangle function, and the discourse domain of the corresponding vehicle yaw rate change rate is [ -3,3]; the membership function of the fuzzy set A4 is a triangle function, and the corresponding domain of the change rate of the yaw rate of the vehicle is [1,5]; the membership function of the fuzzy set A5 is a trapezoidal function, the discourse domain of the corresponding vehicle yaw rate change rate is [3,8], the A5 membership is 0 when the driving wheel tire pressure change rate is 3, and the A5 membership is 1 when the vehicle yaw rate change rate is [7,8 ].
5. The distributed independent driving electric automobile steering time wheel differential anti-dragging control method according to claim 1, wherein the domain of the integral parameter is [0.8,1.2], the membership functions of the fuzzy sets I1, I2, I3, I4 and I5 are triangle functions, the domain of the integral parameter corresponding to the fuzzy set I1 is [0.8,0.85], the membership of the I1 is 1 when the integral parameter is 0.8, and the membership of the I1 is 0 when the integral parameter is 0.85; the domain of the integral parameter corresponding to the fuzzy set I2 is [0.8,0.9]; the domain of the integral parameter corresponding to the fuzzy set I3 is [0.85,1.15], the domain of the integral parameter corresponding to the fuzzy set I4 is [1.1,1.2], the domain of the integral parameter corresponding to the fuzzy set I5 is [1.15,1.2], the degree of membership of I5 is 0 when the integral parameter is 1.15, and the degree of membership of I5 is 1 when the integral parameter is 1.2.
6. The method for controlling differential anti-dragging between wheels when the distributed independent driving electric automobile turns according to claim 5, wherein the fuzzy controller obtains an accurate value by adopting a maximum membership method according to a fuzzy set of the integral parameter when outputting the integral parameter.
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