CN111890951A - Intelligent electric automobile trajectory tracking and motion control method - Google Patents

Intelligent electric automobile trajectory tracking and motion control method Download PDF

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CN111890951A
CN111890951A CN202010787928.4A CN202010787928A CN111890951A CN 111890951 A CN111890951 A CN 111890951A CN 202010787928 A CN202010787928 A CN 202010787928A CN 111890951 A CN111890951 A CN 111890951A
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CN111890951B (en
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赵海艳
刘晓斌
赵津杨
刘万
陈伟轩
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Jilin University
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    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
    • B60L15/00—Methods, circuits, or devices for controlling the traction-motor speed of electrically-propelled vehicles
    • B60L15/20—Methods, circuits, or devices for controlling the traction-motor speed of electrically-propelled vehicles for control of the vehicle or its driving motor to achieve a desired performance, e.g. speed, torque, programmed variation of speed
    • 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
    • B60W50/00—Details of control systems for road vehicle drive control not related to the control of a particular sub-unit, e.g. process diagnostic or vehicle driver interfaces
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
    • B60L2240/00—Control parameters of input or output; Target parameters
    • B60L2240/10—Vehicle control parameters
    • B60L2240/12—Speed
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
    • B60L2240/00—Control parameters of input or output; Target parameters
    • B60L2240/10—Vehicle control parameters
    • B60L2240/24—Steering angle
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
    • B60L2240/00—Control parameters of input or output; Target parameters
    • B60L2240/10—Vehicle control parameters
    • B60L2240/32—Driving direction
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
    • B60L2240/00—Control parameters of input or output; Target parameters
    • B60L2240/40—Drive Train control parameters
    • B60L2240/42—Drive Train control parameters related to electric machines
    • B60L2240/423—Torque
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
    • B60L2240/00—Control parameters of input or output; Target parameters
    • B60L2240/60—Navigation input
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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
    • B60W50/00—Details of control systems for road vehicle drive control not related to the control of a particular sub-unit, e.g. process diagnostic or vehicle driver interfaces
    • B60W2050/0001—Details of the control system
    • B60W2050/0019—Control system elements or transfer functions
    • B60W2050/0028—Mathematical models, e.g. for simulation
    • B60W2050/0031—Mathematical model of the vehicle
    • B60W2050/0034—Multiple-track, 2D vehicle model, e.g. four-wheel model
    • 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
    • Y02T10/60—Other road transportation technologies with climate change mitigation effect
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Abstract

The invention discloses an intelligent electric automobile track tracking and motion control method, which comprises the following steps: judging whether the vehicle can work in a lateral stable range or not according to the given target track and the current road condition; if the work is in the stable area, executing the step 2; if the lateral stability range is exceeded, executing the step 3; step 2, executing a working mode 1, and performing vehicle trajectory tracking and stability control by adopting vehicle trajectory tracking and yaw stability double closed-loop control: according to the expected running track and the vehicle kinematic model, calculating an expected course angle of the vehicle; establishing a three-degree-of-freedom vehicle dynamics model, designing a track tracking controller, inputting a difference value between an expected course angle of a vehicle and a course angle at the previous moment into the track tracking controller, and solving to obtain a front wheel rotation angle and a longitudinal speed; designing a yaw stability controller by adopting a model predictive control algorithm and then distributing the moment; and 3, executing the working mode 2, and performing drift control on vehicle track tracking.

Description

Intelligent electric automobile trajectory tracking and motion control method
Technical Field
The invention relates to a control method belonging to the field of chassis optimization control of four-wheel hub drive electric automobiles, in particular to an intelligent vehicle track tracking and stability control method based on switching control, which relates to kinematics and dynamics control of a vehicle and can improve the maneuverability, stability and comfort of the vehicle.
Background
With the social demands on vehicle intellectualization, low carbon and light weight, the development of intelligent electric vehicles becomes an important way for improving vehicle safety, reducing environmental pollution and saving energy, and the traditional active safety control system cannot meet the current complex traffic environment, so the development of an electric control system of the intelligent electric vehicle is particularly important. Conventional control methods primarily consider kinematic tracking control of the vehicle separately from dynamic vehicle stability control. The track tracking research method of the intelligent vehicle generally comprises the steps of planning an ideal reference track according to state information and surrounding environment information of the vehicle, and then tracking the ideal reference track through transverse and longitudinal control of the intelligent vehicle. The stability control of the vehicle is mainly focused on the control of the yaw moment of the vehicle, a layered control method is adopted, the strategy of controlling distribution after integrated control is firstly adopted, and the stability and the comfort of the vehicle are improved.
However, there are still significant disadvantages to the current research on tracking and dynamics control of intelligent electric vehicles, including:
1. at present, a track tracking controller and a motion controller can better track an expected track under specific conditions to ensure the stability of a vehicle, but research on the influence factors such as comprehensive adhesion conditions and vehicle speed is less, and the stability and riding comfort of the vehicle are difficult to ensure under a high-speed complex road surface.
2. Currently, the research on intelligent vehicle track tracking and dynamics control mostly adopts a scheme that a track tracking controller and a vehicle yaw stability controller are designed separately, and the two controllers have a common actuating mechanism (such as a steering motor and a driving motor), so that control conflict can be caused, and particularly, the driving safety of a vehicle can be reduced when the vehicle runs under complex road conditions or at high speed.
3. At present, the trajectory tracking control and the vehicle dynamics control mainly stay in controller design based on a model, the dependence degree on environment and parameter selection is high, and the trajectory tracking under the new state condition cannot be well adapted under the condition of sudden change of the environment.
4. At present, vehicle trajectory tracking is mainly researched under simple working conditions and complex working conditions respectively and aims at the simple working conditions: the low-freedom dynamic model adopts linear decoupling control; aiming at complex working conditions: the high-freedom dynamic model adopts a nonlinear strong coupling control algorithm. In actual life, the vehicle can not be guaranteed to work under simple working conditions or complex working conditions, and obviously, the track tracking controller at the present stage can not meet the requirement of vehicle track tracking under all working conditions.
Disclosure of Invention
In order to solve the problems in the prior art and ensure the track tracking effect of the vehicle and the safety of the vehicle to the maximum extent, the invention aims to provide an intelligent method for tracking and controlling the track of the electric vehicle, in particular to a method for dividing working conditions according to whether the vehicle works in a lateral dynamics stable range or not and carrying out double-closed-loop track tracking control and stability control in the lateral dynamics stable range; and (4) exceeding the lateral dynamics stability range, and designing track tracking control based on a drift algorithm.
In order to achieve the purpose, the invention adopts the following technical scheme:
an intelligent electric vehicle trajectory tracking and motion control method comprises the following steps:
step 1, judging whether a vehicle can work in a lateral stable range or not according to a given target track and a current road condition; if the work is in the stable area, executing the step 2; if the lateral stability range is exceeded, executing the step 3;
step 2, executing a working mode 1, and performing vehicle trajectory tracking and stability control by adopting vehicle trajectory tracking and yaw stability double closed-loop control: according to the expected running track and the vehicle kinematic model, calculating an expected course angle of the vehicle; establishing a three-degree-of-freedom vehicle dynamics model, designing a track tracking controller, inputting a difference value between an expected course angle of a vehicle and a course angle at the previous moment into the track tracking controller, and solving to obtain a front wheel rotation angle and a longitudinal speed; designing a yaw stability controller by adopting a model predictive control algorithm, taking the front wheel turning angle and the longitudinal speed obtained by solving the trajectory tracking controller as the state input of the yaw stability controller, and then carrying out torque distribution to realize the control of the intelligent vehicle;
and 3, executing a working mode 2, and performing drift control on vehicle track tracking: calculating the position change of the center of mass of the vehicle in a drifting state, establishing a drifting control vehicle dynamic model for tracking the vehicle track, and designing a track tracking controller for drifting control.
Further, in the step 2, establishing the upper-degree-of-freedom vehicle dynamics model includes the following steps:
establishing a three-degree-of-freedom vehicle dynamic equation:
Figure BDA0002622697290000021
Figure BDA0002622697290000022
Figure BDA0002622697290000023
in the formula: v. ofx、vyRepresenting the longitudinal speed and the lateral speed of the vehicle, respectively; flf、FlrRespectively expressed as longitudinal forces related to the longitudinal rigidity and the slip ratio of the front wheel and the rear wheel of the vehicle and the tire; fcf、FcrRespectively representing the lateral force of the front wheel and the rear wheel of the vehicle relative to the cornering stiffness and the cornering angle of the tire; gamma is the yaw velocity of the vehicle; a. b is the distance from the vehicle mass center to the front axle and the rear axle respectively;fis a front wheel corner; i iszIs the moment of inertia;
the tire force is expressed as:
Flf＝Clfsf
Flr＝Clrsr
Figure BDA0002622697290000031
Figure BDA0002622697290000032
in the formula: clf、ClrExpressed as the longitudinal stiffness of the front and rear tires of the vehicle, respectively; ccf、CcrExpressed as the lateral stiffness of the front and rear tires of the vehicle, respectively; sf、srExpressed as the longitudinal slip ratio of the front and rear tires of the vehicle, respectively.
Further, in the step 2, designing and obtaining a trajectory tracking controller includes the following steps:
the control model designed for the trajectory tracking controller is as follows:
Figure BDA0002622697290000033
the state variable is
Figure BDA0002622697290000034
u＝[f]To control the quantity, the control output is
Figure BDA0002622697290000035
The discretized state space model is described as:
Figure BDA0002622697290000036
defining a prediction time domain as p, a control time domain as m, wherein p is more than m;
at time k + p, the vehicle state is x (k + p) ═ F (x (k), u (k +1), …, u (k + m), …, u (k + p-1));
when the sampling time is larger than the control time domain, keeping the control input unchanged until a prediction time domain u (k + m-1) ═ u (k + m +1) ═ … u (k + p-1);
thus defining the optimal control input at time k:
Figure BDA0002622697290000037
the corresponding predicted output at time k:
Figure BDA0002622697290000041
the reference input sequence of the system is:
Figure BDA0002622697290000042
at the k-th sampling time, y (k) is used as an initial value predicted by the control system, namely y (k | k) ═ y (k);
the objective function is:
J＝Q||Y(k+1|k)-R(k+1)||2+R||U(k)||2
wherein the content of the first and second substances,Q＝diag(τQ1,τQ2,…,τQp) A weight coefficient for controlling the attitude of the vehicle;R＝diag(τR1,τR2,…,τRp) Is the weight coefficient of the control input.
Further, in the step 2, designing the yaw stability controller by using the model predictive control algorithm includes the following steps:
1) building a two-degree-of-freedom vehicle dynamics model:
Figure BDA0002622697290000043
Figure BDA0002622697290000044
wherein β is the centroid slip angle; γ is the yaw rate of the vehicle body; fyf,FyrRespectively representing two-degree-of-freedom vehicle model front wheelLateral force and rear wheel lateral force; m represents the mass of the whole vehicle; v represents a longitudinal vehicle speed; l isf,LrRespectively representing the distance from the center of mass of the vehicle to the front axle and the distance from the center of mass of the vehicle to the rear axle; mzIs the vehicle yaw moment; fZRepresents the vertical force load of the tire; i isZIs the moment of inertia;
2) and designing the yaw stability controller based on the two-degree-of-freedom vehicle dynamic model.
Further, the step 2) of designing the yaw stability controller based on the two-degree-of-freedom vehicle dynamics model comprises the following processes:
the two-degree-of-freedom vehicle dynamics model is arranged to obtain a state space model of the system:
Figure BDA0002622697290000051
Figure BDA0002622697290000052
the yaw angular velocity gamma and the centroid sideslip angle beta are taken as state variables x ═ beta, gamma of the system]TThe controlled quantity is u ═ 2-f,Tfl,Tfr,Trl,Trr]TThe system outputs y ═ beta, gamma, respectively, for the steering angle of the front wheels, and the torque commands of the four wheel drive motors]T；
Discretizing by using an Euler formula to obtain a discrete time system model as follows:
x(k+1)＝f(x(k),u(k))·Ts+x(k),
y(k)＝C·x(k),
where k denotes the sampling instant, TsFor sampling time, matrix
Figure BDA0002622697290000053
Deriving p-step prediction states and prediction outputs:
Figure BDA0002622697290000054
Figure BDA0002622697290000055
y(k+1|k)＝C·x(k)
further, the optimization objectives of the yaw stability controller include:
(a) the main optimization objectives are:
Figure BDA0002622697290000056
in the formula, Q1,Q2Is the weighting factor in the optimization objective;
(b) the larger the motor torque means the more energy is consumed from the battery, the consumed energy is reduced, and the square sum of the control quantity is as small as possible:
Figure BDA0002622697290000061
in the formula, R1,R2Is the weighting factor in the optimization objective;
(c) in order to reduce the variation of the control action, maintaining smooth steering and motor drive behavior, the variation of the control quantity is reduced:
Figure BDA0002622697290000062
in the formula, S1,S2Is the weighting factor in the optimization objective;
(d) and (3) motor saturation constraint:
-Temax≤Ti(k+j|k)≤Temax,
i＝fl,fr,rl,rr,j＝1,2,...,m-1.
further, the step 3 of executing the working mode 2 includes the following steps:
3.1) data training: performing drifting operation through a testing platform combining a driving simulator and vehicle dynamics simulation software, recording vehicle state parameters and tire coefficients, and constructing a training data set;
and 3.2) calculating the change of the position of the mass center of the vehicle in a drifting state through the vehicle state parameters and the tire coefficients, establishing a drifting control vehicle dynamics model for tracking the vehicle track, designing a track tracking controller based on model predictive control, inputting the difference value of the expected course angle of the vehicle and the course angle at the previous moment into the track tracking controller, and solving to obtain the corner and the longitudinal speed of the front wheel.
Compared with the prior art, the invention has the beneficial effects that:
1. based on the switching control idea, the working condition which can be processed by the controller is transited from a simple working condition to a complex working condition, and the vehicle trajectory tracking and stability controller under the full working condition is provided. The track following performance of the vehicle and the stability of the vehicle can be guaranteed when the vehicle runs normally and under emergency conditions. The road tracking performance, safety, maneuverability, stability and comfort of the vehicle are improved.
2. When the vehicle runs in the lateral stability range, a track tracking control and stability control double-closed-loop controller is adopted, the design of the double-closed-loop controller is different from the traditional scheme that a track tracking controller and a vehicle yaw stability controller are separately designed, the tracking performance, the safety performance, the operation performance and the stability of the vehicle can be comprehensively considered on the whole, the occurrence of actuator conflict caused by separate design is avoided, and the running safety of the vehicle is ensured.
3. The drift control of the intelligent vehicle is a mode combining data drive and a model, the problem of high dependence degree on environment and parameter selection caused by pure model-based design is avoided, and the safety of the vehicle can be ensured under dangerous working conditions or emergency working conditions.
4. When the vehicle is stably controlled, the torque is redistributed, and various factors such as tire load, motor load, energy saving and the like are considered, so that the intelligent vehicle can improve the service life of the actuator and save energy while ensuring the safety.
Drawings
The embodiments of the present invention will be further described with reference to the accompanying drawings, and the description of the invention will be more clearly understood. Wherein:
FIG. 1 is a flow chart of the intelligent electric vehicle trajectory tracking and motion control method of the present invention;
FIG. 2 is a schematic block diagram of trajectory tracking and yaw stability control dual closed loop control;
FIG. 3 is a schematic view of a linear two degree-of-freedom vehicle model;
FIG. 4 is a graph of tire longitudinal force versus slip ratio;
FIG. 5 is a graph of tire longitudinal force versus vertical force;
fig. 6 is a schematic diagram of a drift control system development process.
Detailed Description
For the purpose of illustrating the technical contents, constructional features, objects and the like of the present invention in detail, the present invention will be fully explained with reference to the accompanying drawings.
The general working framework of the invention adopts the idea of switching control, as shown in fig. 1, an intelligent electric vehicle track tracking and motion control method follows the following steps:
step 1: judging whether the vehicle can work in a lateral stable range or not according to the given target track and the current road condition; if the work is in the stable area, executing the step 2; if the lateral stability range is exceeded, executing the step 3;
step 2: executing the working mode 1, and performing vehicle trajectory tracking and stability control by adopting a vehicle trajectory tracking and yaw stability double closed-loop controller, as shown in fig. 2: according to the expected running track and the vehicle kinematic model, calculating an expected course angle of the vehicle; establishing a three-degree-of-freedom vehicle dynamics model, designing a track tracking controller, inputting a difference value between an expected course angle of a vehicle and a course angle at the previous moment into the track tracking controller, and solving to obtain a front wheel rotation angle and a longitudinal speed; and designing a yaw stability controller by adopting a model prediction control algorithm, taking the front wheel turning angle and the longitudinal speed obtained by solving the trajectory tracking controller as the state input of the yaw stability controller, and then carrying out torque distribution to realize the control of the intelligent vehicle.
The specific implementation is as follows:
step 2.1: and establishing a three-degree-of-freedom vehicle dynamics model, designing a track tracking controller, inputting the difference value of the expected course angle of the vehicle and the course angle at the previous moment into the track tracking controller, and solving to obtain the rotation angle and the longitudinal speed of the front wheel.
Establishing a three-degree-of-freedom vehicle dynamics model:
in the process of track tracking, a desired track is referred to by a geodetic coordinate system, and in order to obtain the absolute position of the vehicle in the geodetic coordinate system, the following kinematic equation is obtained by considering the conversion relation between the vehicle body coordinate system and the geodetic coordinate system:
Figure BDA0002622697290000081
Figure BDA0002622697290000082
Figure BDA0002622697290000083
in the formula: x is the abscissa of the centroid under the geodetic coordinate system, Y is the ordinate of the centroid under the geodetic coordinate system,
Figure BDA0002622697290000084
is the angle between the driving direction of the vehicle and the X axis, i.e. the heading angle, v, of the vehiclexIs the longitudinal speed, v, of the vehicleyIs the vehicle lateral velocity.
Establishing a three-degree-of-freedom vehicle dynamic equation:
Figure BDA0002622697290000085
in the formula: v. ofx、vyRepresenting the longitudinal speed and the lateral speed of the vehicle, respectively; flf、FlrExpressed as longitudinal stiffness of the front and rear wheels and the tyre of the vehicle, respectivelyLongitudinal force related to slip rate; fcf、FcrRespectively representing the lateral force of the front wheel and the rear wheel of the vehicle relative to the cornering stiffness and the cornering angle of the tire; gamma is the yaw velocity of the vehicle; a. b is the distance from the vehicle mass center to the front axle and the rear axle respectively;fis a front wheel corner; i iszIs the moment of inertia.
Cornering angle of tire:
Figure BDA0002622697290000086
Figure BDA0002622697290000087
the simplified tire force can therefore be expressed as equation (7) with the fitted curve as shown in fig. 4, 5:
Figure BDA0002622697290000088
in the formula: clf、ClrExpressed as the longitudinal stiffness of the front and rear tires of the vehicle, respectively; ccf、CcrExpressed as the lateral stiffness of the front and rear tires of the vehicle, respectively; sf、srExpressed as the longitudinal slip ratio of the front and rear tires of the vehicle, respectively.
Designing a trajectory tracking controller:
obtaining a control model designed for a trajectory tracking controller:
Figure BDA0002622697290000091
the state variable here is
Figure BDA0002622697290000092
u＝[f]To control the quantity, the control output is
Figure BDA0002622697290000093
The expression of the continuous time state space equation of the prediction model obtained by sorting the formula (8) is shown as (9):
Figure BDA0002622697290000094
for the subsequent controller design, the continuous state space model is discretized, and the sampling time is selected to be TsThe discretized state space model is described as equation (10) at 0.02 s:
Figure BDA0002622697290000095
in the invention, a prediction time domain is defined as p, a control time domain is defined as m, and p is more than m. The dynamics of the vehicle in the [ k +1, k + p ] prediction time domain can be obtained based on the current state of the vehicle and a prediction model. That is, at time k + p, the vehicle state is:
x (k + p) ═ F (x (k), u (k +1), …, u (k + m), …, u (k + p-1)). When the sampling time is greater than the control time domain, the control input is held constant until the prediction time domain u (k + m-1) ═ u (k + m +1) ═ … u (k + p-1).
Thus defining the optimal control input at time k:
Figure BDA0002622697290000096
corresponding predicted output at time k
Figure BDA0002622697290000101
The reference input sequence definition of the system is as shown in equation (13):
Figure BDA0002622697290000102
at the k-th sampling time, y (k) is used as an initial value predicted by the control system, i.e., y (k | k) ═ y (k). The state variable and the input of the controlled system can be calculated and updated according to the state variable value and the system input at the current moment, the first item of the obtained control sequence is used as the system input to act on the next moment, the optimization problem is solved by combining the output of the controlled system at the next moment, the rolling optimization of the control sequence is realized by repeating the steps, and the state at the future moment is solved.
Obtaining a desired course angle of the vehicle:
for better tracking of the trajectory, it is necessary to make the longitudinal velocity lateral velocity, the yaw rate and the current vehicle position track the desired values, and at the same time, the control action is not too large, and the objective function of the vehicle body controller is obtained as shown in the following formula:
J＝Q||Y(k+1|k)-R(k+1)||2+R||U(k)||2(14)
herein, theQ＝diag(τQ1,τQ2,…,τQp) To control the weight coefficient of the vehicle attitude,R＝diag(τR1,τR2,…,τRp) Is the weight coefficient of the control input. When in useQWhen the weight coefficient is large, the system focuses on considering the tracking performance of the vehicle;Rrelatively large, the system focuses on considering the energy of the vehicle.
Step 2.2: and designing a yaw stability controller by adopting a model prediction control algorithm, taking the front wheel turning angle and the longitudinal speed obtained by solving the trajectory tracking controller as the state input of the yaw stability controller, and then carrying out torque distribution to realize the control of the intelligent vehicle.
The invention is mainly researched based on the yaw dynamics of the vehicle, and mainly considers the motion of two degrees of freedom of vehicle rolling and yaw. Therefore, assuming that the vehicle speed is a constant value, the vehicle is simplified to a two-degree-of-freedom bicycle model, as shown in fig. 5.
Firstly, two-degree-of-freedom vehicle dynamics model building
As shown in fig. 3, the controller model is obtained with the vehicle mass center slip angle and the yaw rate as state variables, and the drive torques of the four wheels and the front wheel rotation angles as inputs.
Figure BDA0002622697290000111
Wherein β is the centroid slip angle, representing the angle between the vehicle longitudinal axis and the vehicle speed vector direction, and γ is the yaw rate of the vehicle body. β and γ represent two degrees of freedom that simplify the two-degree-of-freedom vehicle dynamics model. Fyf,FyrRespectively representing the lateral force of a front wheel and the lateral force of a rear wheel of a two-degree-of-freedom vehicle model, m representing the mass of the whole vehicle, V representing the longitudinal vehicle speed, and Lf,LrRepresenting the distance of the vehicle's center of mass to the front axle and the center of mass to the rear axle, respectively. MzIs the vehicle yaw moment, expressed as follows.
Figure BDA0002622697290000112
Wherein d represents a single-axle left and right tread, FxijI ═ f, r; j ═ l, r. represents the longitudinal forces of the left front wheel, the left rear wheel, the right front wheel, and the right rear wheel.
The vertical force loading of the tire due to the effects of longitudinal acceleration, lateral acceleration, roll, pitch, etc. can be described as:
Figure BDA0002622697290000113
Figure BDA0002622697290000114
Figure BDA0002622697290000115
Figure BDA0002622697290000116
wherein h iscgRepresenting the height of the center of mass of the vehicle, g representing the acceleration of gravity, axRepresenting longitudinal acceleration, ayRepresenting the longitudinal acceleration.
The system model obtained by sorting is as follows:
Figure BDA0002622697290000117
wherein, FZRepresents the vertical force load of the tire, and is calculated by equations (17) to (20).
Designing a yaw stability controller based on a formula (21):
and (3) obtaining a state space model of the system by arranging a formula (21):
Figure BDA0002622697290000121
the yaw angular velocity gamma and the centroid sideslip angle beta are taken as state variables x ═ beta, gamma of the system]TThe controlled quantity is u ═ 2-f,Tfl,Tfr,Trl,Trr]TThe system outputs y ═ beta, gamma, respectively, for the steering angle of the front wheels, and the torque commands of the four wheel drive motors]T。
Discretizing by using an Euler formula to obtain a discrete time system model as follows:
Figure BDA0002622697290000122
where k denotes the sampling instant, TsFor sampling time, matrix
Figure BDA0002622697290000123
According to basic principles and relevant theories of model predictive control, p-step prediction states and prediction outputs can be deduced:
Figure BDA0002622697290000124
Figure BDA0002622697290000125
y(k+1|k)＝C·x(k)
(24)
the MPC control algorithm can effectively solve the multi-target multi-constraint problem, can be expressed as a multi-target equation with a weighting matrix, and obtains multi-dimensional optimization variables including a front wheel steering angle, a four-wheel driving torque and the like.
Optimizing the target:
(a) to maintain vehicle stability and good handling, it is necessary to make the system output track the desired model, and therefore, the main optimization objective is
Figure BDA0002622697290000131
In the formula Q1,Q2Are the weighting coefficients in the optimization objective.
(b) The larger the motor torque means the more energy is consumed from the battery, the less energy is consumed, the sum of squares of the control amount is as small as possible,
Figure BDA0002622697290000132
in the formula R1,R2Are the weighting coefficients in the optimization objective.
(c) In order to reduce the variation in the control action, maintain smooth steering and motor drive behavior, reduce the variation in the control amount,
Figure BDA0002622697290000133
in the formula S1,S2Are the weighting coefficients in the optimization objective.
And (3) motor saturation constraint:
Figure BDA0002622697290000134
and step 3: and executing a working mode 2, and performing drift control on vehicle track tracking by adopting a double closed-loop control structure:
due to factors such as wet and slippery road surface, when a vehicle makes a sharp turn at a high speed, stability of lateral dynamics cannot be guaranteed, at the moment, the vehicle generates strong input coupling and yaw/sideslip instability, and it is not feasible to perform trajectory tracking by considering a lateral stability method. Therefore, the vehicle posture is not considered under the working condition, and the tracking of the vehicle track is focused. While operating entirely outside the vehicle stability limits, drift control can achieve precise control of both vehicle side-slip and travel path simultaneously. The automatic driving drift control algorithm can expand the available state space of the vehicle to be out of a limit range, so that the tracking of the vehicle track is realized. Therefore, only the tracking of the trajectory is considered in such operating conditions, and the stability of the vehicle is no longer sought.
Step 3.1: and a test engineer performs drifting operation on circular roads with different road surface adhesion coefficients and different radiuses through a test platform combining a driving simulator and vehicle dynamics simulation software. During the performance of these operations, vehicle state information, such as tire data and front wheel steering angle data, of the vehicle is recorded and used to construct a training data set. These are post-processed for training vehicle dynamics models and tire models under drift control.
The invention takes a road surface adhesion coefficient (ice and snow road surface) as an example, gives a training data process, and a training data set is generated by directly connecting in series in the virtual test environment. The drift state (| β | ≈ 30-40 degrees) is reached after a few seconds and remains unchanged during the rest of the test. First, a drift operation is performed in circles of different radii, R ═ 10: 10: 100]. Thereafter, to facilitate the generalization of the proposed system to drift control of other centroid slip angle ranges, these tests were repeated for the other two sets of centroid slip angle ranges: moderate drift | beta | approximately equals 15-25 degrees, and slight drift | beta | approximately equals 10-15 degrees. And finishing the acquisition and training of data.
Step 3.2: after data training is completed, tire parameters and tire-road friction coefficients are fitted, the position change of the center of mass of the vehicle in a drifting state is calculated, a new three-degree-of-freedom drift control dynamics model for tracking the vehicle track is established based on the position change, a track tracking controller is designed, the difference value of the expected course angle of the vehicle and the course angle at the previous moment is input into the track tracking controller, the turning angle and the longitudinal speed of a front wheel are obtained through solving, then step 2.2 is executed, the inner ring yaw stability controller is designed, and the stability of the dynamics of the vehicle is realized.
Wherein a given path and a desired centroid slip angle are tracked using steering angle and rear axle drive torque; the lateral displacement error and the centroid slip angle are selected as control variables, and the expected course angular velocity and the comprehensive yaw angular velocity are obtained based on the dynamic analysis of the lateral displacement error and the centroid slip angle.
And 3, returning to the step 1 after the step 3 is finished, and sequentially circulating to finish the intelligent electric automobile track tracking and motion control.

Claims (7)

1. An intelligent electric vehicle trajectory tracking and motion control method is characterized by comprising the following steps:
step 1, judging whether a vehicle can work in a lateral stable range or not according to a given target track and a current road condition; if the work is in the stable area, executing the step 2; if the lateral stability range is exceeded, executing the step 3;
step 2, executing a working mode 1, and performing vehicle trajectory tracking and stability control by adopting vehicle trajectory tracking and yaw stability double closed-loop control: according to the expected running track and the vehicle kinematic model, calculating an expected course angle of the vehicle; establishing a three-degree-of-freedom vehicle dynamics model, designing a track tracking controller, inputting a difference value between an expected course angle of a vehicle and a course angle at the previous moment into the track tracking controller, and solving to obtain a front wheel rotation angle and a longitudinal speed; designing a yaw stability controller by adopting a model predictive control algorithm, taking the front wheel turning angle and the longitudinal speed obtained by solving the trajectory tracking controller as the state input of the yaw stability controller, and then carrying out torque distribution to realize the control of the intelligent vehicle;
and 3, executing a working mode 2, and performing drift control on vehicle track tracking: calculating the position change of the center of mass of the vehicle in a drifting state, establishing a drifting control vehicle dynamic model for tracking the vehicle track, and designing a track tracking controller for drifting control.
2. The intelligent electric vehicle trajectory tracking and motion control method of claim 1, wherein in the step 2, establishing the upper degree of freedom vehicle dynamics model comprises the following steps:
establishing a three-degree-of-freedom vehicle dynamic equation:
Figure FDA0002622697280000011
Figure FDA0002622697280000012
Figure FDA0002622697280000013
in the formula: v. ofx、vyRepresenting the longitudinal speed and the lateral speed of the vehicle, respectively; flf、FlrRespectively expressed as longitudinal forces related to the longitudinal rigidity and the slip ratio of the front wheel and the rear wheel of the vehicle and the tire; fcf、FcrRespectively representing the lateral force of the front wheel and the rear wheel of the vehicle relative to the cornering stiffness and the cornering angle of the tire; gamma is the yaw velocity of the vehicle; a. b is the distance from the vehicle mass center to the front axle and the rear axle respectively;fis a front wheel corner; i iszIs the moment of inertia;
the tire force is expressed as:
Flf＝Clfsf
Flr＝Clrsr
Figure FDA0002622697280000014
Figure FDA0002622697280000015
in the formula: clf、ClrRespectively represented as front and rear tyres of a vehicleA longitudinal stiffness; ccf、CcrExpressed as the lateral stiffness of the front and rear tires of the vehicle, respectively; sf、srExpressed as the longitudinal slip ratio of the front and rear tires of the vehicle, respectively.
3. The intelligent electric vehicle trajectory tracking and motion control method according to claim 1, wherein in the step 2, designing the trajectory tracking controller and obtaining the trajectory tracking controller comprises the following steps:
the control model designed for the trajectory tracking controller is as follows:
Figure FDA0002622697280000021
the state variable is
Figure FDA0002622697280000022
u＝[f]To control the quantity, the control output is
Figure FDA0002622697280000023
The discretized state space model is described as:
Figure FDA0002622697280000024
defining a prediction time domain as p, a control time domain as m, wherein p is more than m;
at time k + p, the vehicle state is x (k + p) ═ F (x (k), u (k +1), …, u (k + m), …, u (k + p-1));
when the sampling time is larger than the control time domain, keeping the control input unchanged until a prediction time domain u (k + m-1) ═ u (k + m +1) ═ … u (k + p-1);
thus defining the optimal control input at time k:
Figure FDA0002622697280000025
the corresponding predicted output at time k:
Figure FDA0002622697280000026
the reference input sequence of the system is:
Figure FDA0002622697280000031
at the k-th sampling time, y (k) is used as an initial value predicted by the control system, namely y (k | k) ═ y (k);
the objective function is:
J＝Q||Y(k+1|k)-R(k+1)||2+R||U(k)||2
wherein the content of the first and second substances,Q＝diag(τQ1,τQ2,…,τQp) A weight coefficient for controlling the attitude of the vehicle;R＝diag(τR1,τR2,…,τRp) Is the weight coefficient of the control input.
4. The intelligent electric vehicle trajectory tracking and motion control method as claimed in claim 1, wherein in the step 2, designing the yaw stability controller by using the model predictive control algorithm comprises the following steps:
1) building a two-degree-of-freedom vehicle dynamics model:
Figure FDA0002622697280000032
Figure FDA0002622697280000033
wherein β is the centroid slip angle; γ is the yaw rate of the vehicle body; fyf,FyrRespectively representing the lateral force of a front wheel and the lateral force of a rear wheel of the two-degree-of-freedom vehicle model; m represents the mass of the whole vehicle; v represents a longitudinal vehicle speed; l isf,LrRespectively representing the distance from the center of mass of the vehicle to the front axle and the distance from the center of mass of the vehicle to the rear axle; mzIs the vehicle yaw moment; fZRepresents the vertical force load of the tire; i isZIs the moment of inertia;
2) and designing the yaw stability controller based on the two-degree-of-freedom vehicle dynamic model.
5. The intelligent electric vehicle trajectory tracking and motion control method of claim 4, wherein the step 2) of designing the yaw stability controller based on the two-degree-of-freedom vehicle dynamics model comprises the following processes:
the two-degree-of-freedom vehicle dynamics model is arranged to obtain a state space model of the system:
Figure FDA0002622697280000034
Figure FDA0002622697280000035
the yaw angular velocity gamma and the centroid sideslip angle beta are taken as state variables x ═ beta, gamma of the system]TThe controlled quantity is u ═ 2-f,Tfl,Tfr,Trl,Trr]TThe system outputs y ═ beta, gamma, respectively, for the steering angle of the front wheels, and the torque commands of the four wheel drive motors]T；
Discretizing by using an Euler formula to obtain a discrete time system model as follows:
x(k+1)＝f(x(k),u(k))·Ts+x(k),
y(k)＝C·x(k),
where k denotes the sampling instant, TsFor sampling time, matrix
Figure FDA0002622697280000041
Deriving p-step prediction states and prediction outputs:
Figure FDA0002622697280000042
Figure FDA0002622697280000043
y(k+1|k)＝C·x(k)。
6. the intelligent electric vehicle trajectory tracking and motion control method of claim 5, wherein the optimization objectives of the yaw stability controller comprise:
(a) the main optimization objectives are:
Figure FDA0002622697280000044
in the formula, Q1,Q2Is the weighting factor in the optimization objective;
(b) the larger the motor torque means the more energy is consumed from the battery, the consumed energy is reduced, and the square sum of the control quantity is as small as possible:
Figure FDA0002622697280000045
in the formula, R1,R2Is the weighting factor in the optimization objective;
(c) in order to reduce the variation of the control action, maintaining smooth steering and motor drive behavior, the variation of the control quantity is reduced:
Figure FDA0002622697280000051
in the formula, S1,S2Is the weighting factor in the optimization objective;
(d) and (3) motor saturation constraint:
-Temax≤Ti(k+j|k)≤Temax,
i＝fl,fr,rl,rr,j＝1,2,...,m-1。
7. the intelligent electric vehicle trajectory tracking and motion control method according to claim 1, wherein the step 3 of executing the working mode 2 comprises the steps of:
3.1) data training: performing drifting operation through a testing platform combining a driving simulator and vehicle dynamics simulation software, recording vehicle state parameters and tire coefficients, and constructing a training data set;
and 3.2) calculating the change of the position of the mass center of the vehicle in a drifting state through the vehicle state parameters and the tire coefficients, establishing a drifting control vehicle dynamics model for tracking the vehicle track, designing a track tracking controller based on model predictive control, inputting the difference value of the expected course angle of the vehicle and the course angle at the previous moment into the track tracking controller, and solving to obtain the corner and the longitudinal speed of the front wheel.
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