CN114475590B - Electric vehicle torque control method and system and readable storage medium - Google Patents
Electric vehicle torque control method and system and readable storage medium Download PDFInfo
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- B—PERFORMING OPERATIONS; TRANSPORTING
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Abstract
The invention relates to the technical field of electric automobiles, in particular to a torque control method and system for an electric automobile and a readable storage medium. The torque control method of the electric automobile comprises the following steps: acquiring the motion state information of the vehicle in real time in the motion process of the vehicle; updating the improved vehicle lateral dynamics control equation in real time according to the motion state information; and controlling the moment distribution of the vehicle by adopting the updated vehicle lateral dynamics control equation. According to the method, the time delay of network communication among all systems of the vehicle is considered, a new vehicle lateral dynamics control equation is redesigned, all time delay factors in the vehicle control system are considered in the vehicle lateral dynamics control equation, then the new vehicle lateral dynamics control equation is adopted to control the torque distribution of the vehicle, the influence of the time delay on the control system is reduced, and the vehicle control accuracy and stability are improved.
Description
Technical Field
The invention relates to the technical field of electric automobiles, in particular to a torque control method and system for an electric automobile and a readable storage medium.
Background
In recent years, with the rapid development of smart sensors, digital controllers, and in-vehicle network technologies in the automotive field, smart distributed electric vehicles have received attention due to their advantages in terms of safety, comfort, and structural flexibility. The fast and accurate torque response and measurement of intelligent distributed drive electric vehicles brings great potential for the motorization and intelligent updating of road electric vehicles. However, some key technical challenges such as high cost, reliability and safety issues have not been well solved, and these issues have also raised great interest in the industry and academia. Motion control of intelligent distributed drive electric vehicles has been one of the focuses of research, and various motion control techniques have been proposed to enhance the safety and reliability of vehicle motion.
Direct yaw moment control is an effective method for improving the safety and reliability of a vehicle, but a vehicle system has high complexity and nonlinear characteristics, and an intelligent distributed driving electric vehicle motion control system is essentially an information physical fusion system integrating digital computation, network communication and physical processes. Due to the influence of speed variation, model disturbance, external interference, network and the like, the performance of the control methods is influenced and is greatly limited in practical application. To address the robust stability control problem, there have been many studies on vehicle control. For example, on one hand, zhanghui et al considers the non-linear tire model and the change in longitudinal velocity in vehicle system modeling, obtains a linear parameter change model with norm bound uncertainty, and proposes a controller gain adjustment method; rong et al comprehensively considers system parameter uncertainty, external interference, input saturation and actuator faults, and designs a dynamic output feedback controller to control vehicle motion; however, most of these studies assume that data exchange among sensors, controllers, actuators in the control system is based on an ideal on-board communication network, i.e., the effect of network-induced delay is not considered. On the other hand, commander and others have proved that the time-varying delay caused by the CAN bus CAN reduce the yaw motion control performance of the vehicle, and then the current electric vehicle control system does not consider the influence of the delay on the control accuracy, so the control accuracy of the current electric vehicle control system and method still needs to be improved.
Disclosure of Invention
The invention mainly solves the technical problem that the existing automobile control method is not high in precision because network communication delay is not considered during the existing electric automobile control.
An electric vehicle torque control method comprises the following steps:
acquiring the motion state information of the vehicle in real time in the motion process of the vehicle;
updating the improved vehicle lateral dynamics control equation in real time according to the motion state information;
controlling the moment distribution of the vehicle by adopting the updated vehicle lateral dynamics control equation;
wherein the improved vehicle lateral dynamics control equation is:
equation (1) is a closed loop control system, and in equation (1) above,u(k) Representing a yaw moment generated by longitudinal force of left and right wheels of the vehicle;ξ(k+1 represents vehicle lateral motion (1)k+1) state of the system matrix,
；ξ(k) Indicating lateral movement of a vehiclekThe system matrix in the state of the system,
；β、γrespectively a centroid slip angle and a yaw angular velocity;δ f (k) Indicates the turning angle of the front wheels of the vehicle,A aug 、B aug 、E aug respectively are parameter matrixes of a vehicle lateral dynamics control equation;
wherein the content of the first and second substances,
（2）
in the above-mentioned formula (2),twhich indicates the current time of day,Tit is indicated that one control period is,τ k representing the loop delay in the networked control of the electric automobile;
in the above-mentioned formula (3),t s represents the task execution time of the sensor and,t sc indicating the sensor communication queue time and the communication execution time,t c indicating the time of execution of the controller task,t ca indicating the controller communication queue time and communication execution time,t a representing the task execution time of the actuator;
in the above-mentioned formula (1),
in the above equation (4):
in the above-mentioned formulas (5) and (6),C f andC r for the equivalent cornering stiffness of the wheel,mis the mass of the vehicle and is,Vrepresenting a current vehicle speed;l f andl r respectively representing the distance between the centre of mass of the vehicle and the front and rear axles,I z representing the moment of inertia of the vehicle;
in the above-mentioned formula (4), the,
（7）
in the above-mentioned formula (7),erepresenting actual yaw rateβAnd centroid slip angleγTracking error with a target state;β ref in order to obtain the target yaw-rate,γ ref is the target centroid slip angle.
In one embodiment, further comprising:
controlling the vehicle by adopting a linear quadratic optimal controller based on the improved vehicle lateral dynamics control equation;
the performance index function is minimized by calculating the optimal control rate to minimize the influence of network delay on the vehicle.
In one embodiment, the minimizing the performance indicator function by calculating the optimal control rate to minimize the impact of network latency on the vehicle comprises:
the performance index function of the linear quadratic optimal controller is as follows:
in the above-mentioned formula (8),QandRthe performance index of the linear quadratic optimal controller is obtained;
order to
Solving to obtain the optimal control rateK k 。
An electric vehicle torque control system, comprising:
the information acquisition module is used for acquiring the motion state information of the vehicle in real time in the motion process of the vehicle;
the control module is used for updating the improved vehicle lateral dynamics control equation in real time according to the motion state information;
the moment distribution module is used for controlling the moment distribution of the vehicle by adopting the updated vehicle lateral dynamics control equation;
wherein the improved vehicle lateral dynamics control equation is:
equation (1) is a closed loop control system, and in equation (1) above,u(k) Representing a yaw moment generated by longitudinal force of left and right wheels of the vehicle;ξ(k+1 represents vehicle lateral motion (1)k+1) state of the system matrix,
；ξ(k) Indicating lateral movement of a vehiclekThe system matrix in the state of the system,
；β、γrespectively a centroid slip angle and a yaw angular velocity;δ f (k) Indicates the turning angle of the front wheels of the vehicle,A aug 、B aug 、E aug respectively are parameter matrixes of a vehicle lateral dynamics control equation;
wherein the content of the first and second substances,
（2）
in the above-mentioned formula (2),twhich indicates the current time of day,Tit is indicated that one control period is,τ k representing the loop delay in the networked control of the electric automobile;
in the above-mentioned formula (3),t s represents the task execution time of the sensor and,t sc indicating the sensor communication queue time and the communication execution time,t c indicating the time of execution of the controller task,t ca indicating the controller communication queue time and communication execution time,t a representing the execution time of the task of the actuator;
in the above-mentioned formula (1),
in the above equation (4):
in the above-mentioned formulas (5) and (6),C f andC r for the equivalent cornering stiffness of the wheel,mis the mass of the vehicle,Vrepresenting a current vehicle speed;l f andl r respectively representing the distance between the centre of mass of the vehicle and the front and rear axles,I z representing the moment of inertia of the vehicle;
in the above-mentioned formula (4),
（7）
in the above-mentioned formula (7),erepresenting actual yaw rateβAnd centroid slip angleγTracking error with a target state;β ref in order to obtain the target yaw rate,γ ref is the target centroid slip angle.
In one embodiment, further comprising:
a linear quadratic optimal controller for controlling the vehicle based on the improved vehicle lateral dynamics control equation;
and the calculation module is used for minimizing the performance index function by calculating the optimal control rate so as to reduce the influence of network delay on the vehicle to the minimum.
In one embodiment, the minimizing the performance indicator function by calculating the optimal control rate to minimize the impact of network latency on the vehicle comprises:
the performance index function of the linear quadratic optimal controller is as follows:
in the above-mentioned formula (8),QandRthe performance index of the linear quadratic optimal controller is obtained;
order to
Solving to obtain the optimal control rateK k 。
An electric vehicle comprises a torque control system as described above.
A computer readable storage medium having stored thereon a program executable by a processor to implement a method as described above.
According to the torque control method of the electric vehicle, the new vehicle lateral dynamics control equation is redesigned in consideration of the time delay of network communication among the systems of the vehicle, each time delay factor in the vehicle control system is considered in the vehicle lateral dynamics control equation, then the new vehicle lateral dynamics control equation is adopted to control the torque distribution of the vehicle, the influence of the time delay on the control system is reduced, and the vehicle control accuracy and stability are improved.
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FIG. 1 is a flowchart of an electric vehicle torque control method according to an embodiment of the present application;
FIG. 2 is a block diagram of a torque control system of an electric vehicle according to an embodiment of the present disclosure;
FIG. 3 is a schematic diagram illustrating delay components in vehicle network communications according to an embodiment of the present application;
fig. 4 is a vehicle control diagram according to an embodiment of the present application.
Detailed Description
The present invention will be described in further detail with reference to the following detailed description and accompanying drawings. Wherein like elements in different embodiments have been given like element numbers associated therewith. In the following description, numerous details are set forth in order to provide a better understanding of the present application. However, those skilled in the art will readily recognize that some of the features may be omitted or replaced with other elements, materials, methods in different instances. In some instances, certain operations related to the present application have not been shown or described in detail in order to avoid obscuring the core of the present application from excessive description, and it is not necessary for those skilled in the art to describe these operations in detail, so that they may be fully understood from the description in the specification and the general knowledge in the art.
Furthermore, the features, operations, or characteristics described in the specification may be combined in any suitable manner to form various embodiments. Also, the various steps or actions in the method descriptions may be transposed or transposed in order, as will be apparent to one of ordinary skill in the art. Thus, the various sequences in the specification and drawings are for the purpose of describing certain embodiments only and are not intended to imply a required sequence unless otherwise indicated where such sequence must be followed.
In the embodiment of the invention, the applicant firstly obtains through tests that uncertainty generated in networked control of the electric vehicle is mainly vehicle-mounted network induced delay, and the vehicle-mounted network induced delay comprises feedback channel delay (from a sensor to a controller) and forward channel delay (from the controller to an actuator). In order to eliminate the influence of time delay on the control precision and stability of the vehicle, the lateral dynamics control equation of the vehicle is redesigned, the motion state information of the vehicle is collected in real time in the motion process of the vehicle, and the lateral dynamics control equation of the vehicle is updated according to the motion state information so as to control the moment distribution of the vehicle, so that the precision and stability of vehicle control are improved.
The torque control method for the electric automobile is the key for guaranteeing the stability and safety of transverse movement. In particular to a torque control method in the process of a lane keeping auxiliary system. Steering control is the most common control method in lane keeping auxiliary systems, but under the influence of external environment and network delay of each part of a vehicle system, automobiles often have the problems of insufficient or over-steering, so that the lane keeping auxiliary systems are unstable. The moment control designed by the invention is added to improve the stability of the system, the steady state boundary and the limit boundary of the running of the automobile are divided by calculating the ideal value and the limit value of the yaw rate and the centroid slip angle, the moment control quantity is coordinated based on the boundary, and the expected additional yaw moment is tracked by distributing different brake pressure to each wheel. As shown in fig. 4, in the lane keeping process of the automobile, in order to make up for understeer or correct oversteer, a torque control method is introduced for compensation, and a Linear Quadratic Regulator (LQR) is designed to reduce the influence of network delay on transverse motion, so that the automobile is better controlled to steer smoothly.
The first embodiment is as follows:
referring to fig. 1, the present embodiment provides a torque control method for an electric vehicle, which includes:
step 101: and acquiring the motion state information of the vehicle in real time in the motion process of the vehicle.
In step 101, acquiring the motion state information of the vehicle in real time mainly includes: the front wheel angle of the vehicle, the yaw rate of the vehicle, the moment of inertia of the vehicle, etc., and it is necessary to acquire some parameter information of the vehicle itself, such as the mass of the vehicle itselfmEquivalent cornering coefficient of wheelC f AndC r and the like.
Step 102: and updating the improved vehicle lateral dynamics control equation in real time according to the motion state information.
Step 103: and controlling the moment distribution of the vehicle by adopting the updated vehicle lateral dynamics control equation.
The improved vehicle lateral dynamics control equation of the embodiment is as follows:
formula (1) is a closed loop control system, and in formula (1),
representing inputs to a closed-loop control system, in particular
For longitudinal force generation of left and right wheels of vehicleA resulting yaw moment.ξ(k+1 represents vehicle lateral motion (1)k+1) state of the system matrix,
。ξ(k) Indicating lateral movement of a vehiclekThe system matrix in the state of the system,
。β、γthe mass center slip angle and the yaw angular velocity are respectively two main state parameters of the transverse dynamics of the vehicle.δ f (k) Which indicates the angle of rotation of the front wheels of the vehicle,A aug 、B aug 、E aug respectively are parameter matrixes of a vehicle lateral dynamics control equation;
wherein the content of the first and second substances,
（2）
in the above-mentioned formula (2),twhich indicates the current time of day,Tit is indicated that one control period is,τ k representing the loop delay in the networked control of the electric automobile;
as shown in fig. 3, in the above formula (3),t s represents the task execution time of the sensor and,t sc indicating the sensor communication queue time and the communication execution time,t c indicating the time of execution of the controller task,t ca representing controller communication queuing time and communication execution time,t a the execution time of the task of the executor is shown,t s 、t sc 、t c 、t ca 、t a can be acquired by a sensor. Extend all toThe error formed by the time is input into a linear quadratic optimization controller, and the linear quadratic optimization controller controls the torque distribution of four wheels of the vehicle so as to control the vehicle.
In the above-mentioned formula (1),
in the above equation (4):
time-varying loop delays introduce uncertainty into the control system, e.g.A aug And are andB aug uncertainty in (1)
Expressed as:
the uncertainty term can be linearized with taylor expansion and then expressed as a multicellular model. The multilocular theory can describe uncertainty caused by network delay, and the multilocular theory is applied to a lateral dynamic model of a vehicle, so that a dynamic model facing a networked control system can be established. (explanation: now the time delay has been taken into account in the precession model, followed by the design of a robust controller for optimization, erroreAccumulating the error of each state for the error of the output quantity and the control quantity at each moment, and performing an index functionJThe function is designed as an erroreAnd control inputuIn the quadratic form of (1), search forJCan minimize the influence of network delay on the lateral motion of the vehicle. The objective of the linear quadratic optimization is to minimize the performance indicator function. )
In the above-mentioned formulas (5) and (6),C f andC r for the equivalent cornering stiffness of the wheel,mis the mass of the vehicle and is,Vrepresenting a current vehicle speed;l f andl r respectively representing the distance between the centre of mass of the vehicle and the front and rear axles,I z representing the moment of inertia of the vehicle;
in the above-mentioned formula (4), the,
（7）
in the above-mentioned formula (7),erepresenting actual yaw rateβAnd centroid slip angleγTracking error with a target state;β ref in order to obtain the target yaw-rate,γ ref is the target centroid slip angle.
Further, in the embodiment, a linear quadratic optimal controller (LQR controller) is adopted to control the vehicle based on the improved vehicle lateral dynamics control equation.
The performance index function is minimized by calculating the optimal control rate to minimize the influence of network delay on the vehicle.
In order to solve the uncertainty of the network control system and ensure the robustness of the system, the embodiment designs the network control system based onH ∞The robust LQR controller of (1). Function of performance indexJThe function is designed as a quadratic form of the error e and the control input u. In this embodiment, the performance index function is minimized by calculating the optimal control rate to minimize the influence of the network delay on the vehicle, and the performance index function of the linear quadratic optimal controller is:
in the above-mentioned formula (8),QandRthe performance index of the linear quadratic optimal controller is obtained;
order to
Solving to obtain the optimal control rateK k 。
Through tests, the control method of the embodiment can give consideration to both the operation stability and the control precision of the vehicle.
Example two:
referring to fig. 2, the present embodiment provides a torque control system for an electric vehicle, which includes an information obtaining module 201, an information obtaining module 202, and a control module 203.
The information acquisition module 201 is configured to acquire motion state information of the vehicle in real time during a vehicle motion process; for example, the information acquisition module 201 is used for acquiring information from various sensors on the vehicle and status information of the vehicle itself. The control module 202 is configured to update the improved vehicle lateral dynamics control equation in real time according to the motion state information; the torque distribution module 203 is configured to control a torque distribution of the vehicle using the updated vehicle lateral dynamics control equations.
In one embodiment, the electric vehicle torque control system further comprises: the linear quadratic optimal controller 204 is used for controlling the vehicle based on the improved vehicle lateral dynamics control equation; the calculation module 205 is configured to minimize the performance indicator function by calculating an optimal control rate to minimize the impact of network latency on the vehicle.
The preset control method in each module in the torque control system of the electric vehicle of this embodiment is the same as that in the first embodiment, and is not described herein again.
The control system of the embodiment can improve the control accuracy and stability of the vehicle.
Example three:
the embodiment provides an electric vehicle, which includes the torque control system provided in the second embodiment.
Example four:
the present embodiment provides a computer-readable storage medium, on which a program is stored, where the program can be executed by a processor to implement the torque control method for an electric vehicle provided in the first embodiment.
Those skilled in the art will appreciate that all or part of the functions of the various methods in the above embodiments may be implemented by hardware, or may be implemented by computer programs. When all or part of the functions of the above embodiments are implemented by a computer program, the program may be stored in a computer-readable storage medium, and the storage medium may include: a read only memory, a random access memory, a magnetic disk, an optical disk, a hard disk, etc., and the program is executed by a computer to realize the above functions. For example, the program may be stored in a memory of the device, and when the program in the memory is executed by the processor, all or part of the functions described above may be implemented. In addition, when all or part of the functions in the above embodiments are implemented by a computer program, the program may be stored in a storage medium such as a server, another computer, a magnetic disk, an optical disk, a flash disk, or a removable hard disk, and may be downloaded or copied to a memory of a local device, or may be version-updated in a system of the local device, and when the program in the memory is executed by a processor, all or part of the functions in the above embodiments may be implemented.
The present invention has been described in terms of specific examples, which are provided to aid understanding of the invention and are not intended to be limiting. For a person skilled in the art to which the invention pertains, several simple deductions, modifications or substitutions may be made according to the idea of the invention.
Claims (8)
1. The torque control method of the electric automobile is characterized by comprising the following steps:
acquiring the motion state information of the vehicle in real time in the motion process of the vehicle;
updating the improved vehicle lateral dynamics control equation in real time according to the motion state information;
controlling the moment distribution of the vehicle by adopting the updated vehicle lateral dynamics control equation;
wherein the improved vehicle lateral dynamics control equation is:
formula (1) is a closed loop control system, and in formula (1),u(k) Representing a yaw moment generated by longitudinal force of left and right wheels of the vehicle;ξ(k+1 represents vehicle lateral motion (1)k+1) state of the system matrix,ξ(k+1)=[β k+1 γ k+1] T ；ξ(k) Indicating lateral movement of a vehiclekThe system matrix in the state of the system,ξ(k)=[β k γ k ] T ；β、γrespectively a centroid slip angle and a yaw angular velocity;δ f (k) Which indicates the angle of rotation of the front wheels of the vehicle,A aug 、B aug 、E aug respectively are parameter matrixes of a vehicle lateral dynamics control equation;
wherein the content of the first and second substances,
（2）
in the above-mentioned formula (2),twhich indicates the current time of day,Tit is indicated that one control period is,τ k representing the loop delay in the networked control of the electric automobile;
in the above-mentioned formula (3),t s represents the task execution time of the sensor and,t sc indicating the sensor communication queue time and the communication execution time,t c indicating the time of execution of the controller task,t ca indicating the controller communication queue time and communication execution time,t a representing the task execution time of the actuator;
in the above-mentioned formula (1),
in the above equation (4):
in the above-mentioned formulas (5) and (6),C f andC r for the equivalent cornering stiffness of the wheel,mis the mass of the vehicle,Vrepresenting a current vehicle speed;l f andl r respectively representing the distance between the centre of mass of the vehicle and the front and rear axles,I z representing the moment of inertia of the vehicle;
in the above-mentioned formula (4),
（7）
in the above-mentioned formula (7),erepresenting actual yaw rateβAnd centroid slip angleγTracking error with a target state;β ref in order to obtain the target yaw rate,γ ref is the target centroid slip angle.
2. The torque control method of the electric vehicle according to claim 1, further comprising:
controlling the vehicle by adopting a linear quadratic optimal controller based on the improved vehicle lateral dynamics control equation;
the performance index function is minimized by calculating the optimal control rate to minimize the influence of network delay on the vehicle.
3. The torque control method for an electric vehicle according to claim 2, wherein the minimizing the performance indicator function by calculating the optimal control rate to minimize the influence of the network delay on the vehicle comprises:
the performance index function of the linear quadratic optimal controller is as follows:
in the above-mentioned formula (8),QandRthe performance index of the linear quadratic optimal controller is obtained;
order to
Solving to obtain the optimal control rateK k 。
4. An electric vehicle torque control system, comprising:
the information acquisition module is used for acquiring the motion state information of the vehicle in real time in the motion process of the vehicle;
the control module is used for updating the improved vehicle lateral dynamics control equation in real time according to the motion state information;
the moment distribution module is used for controlling the moment distribution of the vehicle by adopting the updated vehicle lateral dynamics control equation;
wherein the improved vehicle lateral dynamics control equation is:
formula (1) is a closed loop control system, and in formula (1),u(k) Representing a yaw moment generated by longitudinal force of left and right wheels of the vehicle;ξ(k+1 represents vehicle lateral motion (1)k+1）The system matrix in the state of the system,ξ(k+1)=[β k+1 γ k+1] T ；ξ(k) Indicating lateral movement of a vehiclekThe system matrix in the state of the system,ξ(k)=[β k γ k ] T ；β、γrespectively a centroid slip angle and a yaw angular velocity;δ f (k) Indicates the turning angle of the front wheels of the vehicle,A aug 、B aug 、E aug respectively are parameter matrixes of a vehicle lateral dynamics control equation;
wherein the content of the first and second substances,
（2）
in the above-mentioned formula (2),twhich indicates the current time of day,Tit is indicated that one control period is,τ k representing the loop delay in the networked control of the electric automobile;
in the above-mentioned formula (3),t s represents the task execution time of the sensor and,t sc representing the sensor communication queuing time and the communication execution time,t c indicating the time of execution of the controller task,t ca representing controller communication queuing time and communication execution time,t a representing the execution time of the task of the actuator;
in the above-mentioned formula (1),
in the above equation (4):
in the above-mentioned formulas (5) and (6),C f andC r for the equivalent cornering stiffness of the wheel,mis the mass of the vehicle,Vrepresenting a current vehicle speed;l f andl r respectively representing the distance between the centre of mass of the vehicle and the front and rear axles,I z representing the moment of inertia of the vehicle;
in the above-mentioned formula (4), the,
（7）
in the above-mentioned formula (7),erepresenting actual yaw rateβAnd centroid slip angleγTracking error with a target state;β ref in order to obtain the target yaw rate,γ ref is the target centroid slip angle.
5. The electric vehicle torque control system of claim 4, further comprising:
a linear quadratic optimal controller for controlling the vehicle based on the improved vehicle lateral dynamics control equation;
and the calculation module is used for minimizing the performance index function by calculating the optimal control rate so as to reduce the influence of network delay on the vehicle to the minimum.
6. The torque control system of an electric vehicle of claim 5, wherein minimizing the performance indicator function by calculating an optimal control rate to minimize the effect of network delay on the vehicle comprises:
the performance index function of the linear quadratic optimal controller is as follows:
in the above-mentioned formula (8),QandRthe performance index of the linear quadratic optimal controller is obtained;
order to
Solving to obtain the optimal control rateK k 。
7. An electric vehicle comprising a torque control system according to any one of claims 4 to 6.
8. A computer-readable storage medium, characterized in that the medium has stored thereon a program which is executable by a processor to implement the method according to any one of claims 1-3.
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| CN108944866A (en) * | 2018-07-06 | 2018-12-07 | 长春工业大学 | It is a kind of to improve the adaptive model predictive control algorithm turned to braking Collaborative Control |
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| CN112319610A (en) * | 2020-10-14 | 2021-02-05 | 华南理工大学 | Man-machine sharing steering control method for intelligent automobile |
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| CN108944866A (en) * | 2018-07-06 | 2018-12-07 | 长春工业大学 | It is a kind of to improve the adaptive model predictive control algorithm turned to braking Collaborative Control |
| CN109358621A (en) * | 2018-09-30 | 2019-02-19 | 山东省计算中心（国家超级计算济南中心） | Autonomous driving vehicle Trajectory Tracking Control method |
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