Reconfigurable distributed driving multi-axle vehicle trajectory tracking method and carrying device

Abstract

The invention discloses a reconfigurable distributed driving multi-axis vehicle trajectory tracking method and a carrying device. The method comprises the following steps: acquiring longitudinal and transverse movement speeds and yaw velocity of a vehicle body in real time; acquiring position information and target path information of a vehicle, and matching a trajectory tracking target point; establishing a vehicle three-degree-of-freedom dynamic model for trajectory tracking; according to a longitudinal dynamic model of the vehicle, a sliding mode control method is adopted to solve longitudinal force needed by the whole vehicle, and the effect of tracking the specified speed is achieved; a tracking error model is obtained, and the rotation angle of each wheel and the yaw moment of the whole vehicle are calculated based on a nonlinear model predictive control algorithm; distributing the longitudinal tire force of each wheel according to the definition of the tire adhesion rate and the requirements of the longitudinal force and the yawing moment of the whole vehicle output by the upper-layer controller, and converting the longitudinal tire force into motor torque; and controlling a steering system and a wheel motor of the vehicle to execute the wheel steering angle and torque instruction. The control stability and trajectory tracking precision of the vehicle can be improved.

Description

Technical Field

[0001] The present invention belongs to the field of vehicle dynamics control, and in particular relates to a reconfigurable distributed drive multi-axis vehicle trajectory tracking method and a carrier. Background Art

[0002] Autonomous driving technology is one of the hottest technologies today, and it has unique advantages in improving road safety, enhancing travel convenience, and promoting the new energy economy. Especially for heavy commercial transport vehicles with fixed routes and specific purposes, the integration of autonomous driving technology will significantly enhance the efficiency of traffic operations, optimize the use of road space, reduce transportation costs, and help ease urban traffic pressure.

[0003] The core algorithm system of autonomous driving covers three key dimensions: environmental perception, decision planning and control execution. As the downstream of the autonomous driving algorithm, trajectory tracking control has a decisive influence on the entire system. Accurate and safe trajectory tracking capability is the key to ensure that autonomous vehicles can drive smoothly along the preset path, avoid obstacles and reach their destination safely.

[0004] The accuracy of trajectory tracking and speed tracking of existing autonomous driving needs to be further improved. Summary of the invention

[0005] Purpose of the invention: The present invention provides a reconfigurable distributed drive multi-axis vehicle trajectory tracking method and a carrier, which can be integrated into the autonomous driving technology of commercial vehicles to improve trajectory tracking and speed tracking effects.

[0006] Reconfigurable distributed drive multi-axle vehicles have natural advantages as actuators for trajectory tracking. Distributed drive multi-axle electric vehicles with all-wheel steering replace the mechanical connection of traditional centralized drive vehicles with a wire control system, which can achieve independent control and rapid response of steering and torque of each wheel, which provides a more convenient platform for vehicle trajectory tracking control. In addition, due to the variable number of axles of reconfigurable distributed drive multi-axle vehicles, the total number of axles of the vehicle can be adjusted accordingly according to load requirements. This feature improves the carrying capacity of the vehicle and further saves freight costs.

[0007] Technical solution: A reconfigurable distributed drive multi-axis vehicle trajectory tracking method and a carrier device according to the present invention include the following steps:

[0008] (1) Obtain the longitudinal and lateral motion speeds and yaw angular velocity of the vehicle body in real time;

[0009] (2) Obtain the vehicle’s location information, target path information, and match the appropriate trajectory to track the target point;

[0010] (3) Establish a three-degree-of-freedom dynamic model of the vehicle for trajectory tracking. The three degrees of freedom include the longitudinal, lateral, and yaw directions of the vehicle;

[0011] (4) Based on the longitudinal dynamics model of the vehicle, the sliding mode control method is used to solve the longitudinal force required by the vehicle to achieve the effect of tracking the specified speed;

[0012] (5) Based on the Frenet coordinate system and the vehicle dynamics model, the tracking error model is obtained, and the wheel angle and the vehicle yaw moment are calculated based on the nonlinear model predictive control algorithm;

[0013] (6) Based on the definition of tire adhesion and the requirements of the longitudinal force and yaw moment of the vehicle output by the upper-level controller, the quadratic programming method is used to optimally distribute the longitudinal tire force of each wheel and convert it into motor torque.

[0014] (7) Control the vehicle's steering system and wheel motors to execute wheel steering angle and torque commands.

[0015] Furthermore, the step (3) comprises:

[0016] like Figure 2 As shown in the figure, the lateral dynamics and yaw dynamics model of the whole vehicle is as follows:

[0017]

[0018] The lateral force of the wheel is expressed by the following formula:

[0019]

[0020] T is the coefficient matrix. When the axis i exists, t i is a positive integer 1, otherwise it is 0. The expression of α is as follows:

[0021]

[0022] The symbols and numbers in the above formula are explained: j = 1 and j = 2 represent the left and right wheels respectively, i represents the i-th axle counting from the front end of the vehicle, m is the total mass of the vehicle, v y is the lateral velocity at the center of mass of the vehicle in the body coordinate system, v x is the longitudinal velocity at the center of mass of the vehicle in the body coordinate system, ω r is the yaw rate of the vehicle, I is the moment of inertia of the vehicle about the z-axis, l i represents the distance from the i-th axis to the center of mass of the vehicle, F yij Indicates the lateral force provided by a single tire, F yi represents the lateral force provided by a single axle, α is the side slip angle of the wheel, δ is the wheel turning angle, d is the wheelbase, M zis the additional direct yaw moment of the vehicle, k is the tire cornering stiffness, represents the differential of yaw rate with respect to time t;

[0023] The longitudinal dynamics model of the whole vehicle is as follows:

[0024]

[0025] Among them, F x is the sum of the longitudinal forces acting on the vehicle, C D Indicates the drag coefficient, A f is the frontal area of ​​the vehicle, ρ0 is the air density, and d1 is the interference term.

[0026] Furthermore, the step (4) comprises:

[0027] The velocity tracking error is defined as:

[0028] e v =v x -v xd

[0029] Introduce the integral term into the sliding mode function:

[0030]

[0031] The Lyapunov function is defined as:

[0032]

[0033] In order to satisfy the Lyapunov stability condition, that is, The control rate of the longitudinal force of the designed vehicle is

[0034]

[0035] Explain the symbols and numbers in the above formula: c, k2, ε, D are all positive constants, v xd is the target longitudinal velocity, v x is the actual longitudinal velocity, e v is the difference between the actual longitudinal velocity and the expected velocity, s is the sliding surface function, and in order to mitigate the chattering phenomenon that is easily generated by ordinary sliding mode controllers, the hyperbolic tangent function tanh is used instead of the ordinary switching function:

[0036]

[0037] Furthermore, the vehicle trajectory tracking using NMPC in step (5) includes:

[0038] The vehicle tracking error model is obtained through the Frenet coordinate system

[0039]

[0040] Taking the derivative of both sides of the above equations, we get:

[0041]

[0042] Combined with the vehicle's lateral dynamics model:

[0043]

[0044] The state variables of the system are:

[0045]

[0046] The control amount is

[0047] u=[δ 11 δ 12 δ 21 δ 22 δ 31 δ 32 δ 41 δ 42 δ 51 δ 52 δ 61 δ 62 ΔM z ] T

[0048] According to the above derivation, the state space equation of the vehicle power system can be obtained:

[0049]

[0050] Set the path tracking stage cost function to

[0051]

[0052] Then at time t, the optimal control problem of NMPC can be expressed as follows:

[0053]

[0054] u min ≤u(t+i)≤u max

[0055] The symbols and numbers in the above formula are explained: N is the prediction time domain, J represents the cost function, and the weight matrix of the cost function is Q, R1, R2, is the yaw angle of the vehicle, e d represents the error between the vehicle's center of mass and the target point, is the difference between the vehicle's yaw angle and the desired yaw angle, θ ris the rate of change of the heading angle of the target point, k1 is the curvature of the path point, is the rate of change of the horizontal coordinate at the path point in the Frenet coordinate system, Δu represents the rate of change of the control amount, u min and u max are the maximum and minimum values ​​of the controlled quantities respectively, l(x,u), g(x,u), and f(x,u) represent functions of x and u.

[0056] Furthermore, the step (6) comprises:

[0057] The longitudinal force and yaw moment of the vehicle are obtained from steps (4) and (5), and their relationship with each tire force is:

[0058]

[0059] in,

[0060]

[0061]

[0062] w=[F x11 F x12 F x21 F x22 F x31 F x32 F x41 F x42 F x51 F x52 F x61 F x62 ] T

[0063] The tire adhesion is defined as:

[0064] ρ=T1Ew

[0065] in

[0066] T1=diag((t1,t1),(t2,t2),(t3,t3),(t4,t4),(t5,t5),(t6,t6))

[0067]

[0068] The optimization goal is to ensure that the longitudinal force of the vehicle generated by the longitudinal force of each tire is consistent with the direct yaw moment and the command of the upper controller, and to minimize the total adhesion of the tire:

[0069] minJ=||T1Ew|| 2

[0070] st.h(w)=v

[0071]

[0072] Where v = [F xd ΔM d ] T , after obtaining the longitudinal force of each tire, convert it into motor torque command:

[0073] T ij =F xij r / g r

[0074] Explain the symbols and numbers in the above formula: F xij is the longitudinal force of a single tire in the tire coordinate system, F xi It represents the mapping of the sum of the longitudinal forces of the tires on a single axle to the longitudinal axis in the vehicle coordinate system, μ is the adhesion coefficient of the road surface, and F zij Indicates the vertical load of the tire, T min and T max They are the minimum and maximum torque of the motor, g r is the reduction ratio of the motor, r is the tire radius, and h(w) represents the function of w.

[0075] Beneficial effects: Compared with the prior art, the beneficial effects of the present invention are as follows: 1. Taking into account the variable number of axles of the reconfigurable multi-axis distributed drive vehicle, the present invention adopts a coefficient matrix method to enable the vehicle dynamics model to adapt to the change of the number of axles;

[0076] 2. The present invention proposes a trajectory tracking integrated control strategy based on NMPC and sliding mode variable structure, which can improve the trajectory tracking accuracy while ensuring driving stability to a certain extent; 3. The present invention can be integrated into the automatic driving system of commercial vehicles to improve the tracking effect. BRIEF DESCRIPTION OF THE DRAWINGS

[0077] Figure 1 is a flow chart of an embodiment of the present invention;

[0078] Figure 2 It is an application object diagram of an embodiment of the present invention. DETAILED DESCRIPTION

[0079] The present invention will be further described in detail below in conjunction with the accompanying drawings.

[0080] Reconfigurable multi-axle distributed drive vehicle, the number of axles can be changed as needed, and each wheel has steering and driving functions. It is also equipped with radar, GPS, etc., and can realize automatic driving.

[0081] In the process of automatic driving of reconfigurable multi-axis distributed drive vehicles, the decision-making and planning layer gives the specified target trajectory information, and the controller replaces the human in the lower layer to complete a series of driving operations of the vehicle, so that the vehicle follows the specified trajectory as much as possible and maintains the desired speed. Making full use of the actuator redundancy characteristics of multi-axis commercial vehicles for multi-actuator coordinated control is conducive to improving the trajectory tracking effect. In order to adapt to the characteristics of the variable number of axes of reconfigurable multi-axis distributed drive vehicles and ensure the effects of trajectory tracking and speed tracking, it is necessary to establish an accurate dynamic model and design corresponding control strategies to distribute the steering angle information and torque information obtained by the controller to each wheel until the end of the automatic driving process.

[0082] Figure 2 A six-axle commercial vehicle is shown, which is an example of a reconfigurable multi-axle distributed drive vehicle in the present invention. The invention first uses the vehicle-mounted IMU to obtain the yaw rate information, and uses the state estimation module to obtain the longitudinal and lateral speed information of the vehicle; then uses the vehicle-mounted GPS module to obtain the vehicle position in real time, and finds the target point of trajectory tracking in the upper-level planning path based on it; then builds a reconfigurable longitudinal and lateral dynamic model of the vehicle based on the vehicle body state information; then designs a tracking error model, converts the trajectory tracking problem into an NMPC optimization problem, converts the speed error model into the control rate of the sliding mode controller, and solves the steering angle of each wheel and the yaw moment of the whole vehicle; finally, designs a tire force distribution strategy, obtains the torque command of each wheel motor, and publishes the steering angle and torque information to the corresponding actuator for execution until the automatic driving process ends.

[0083] With the help of Figure 2 The scenario describes the implementation process of the present invention in detail. Figure 1 As shown, the energy-optimized braking speed optimization method for an intelligent networked electric vehicle described in the present invention specifically includes the following steps:

[0084] Step 1: Obtain the vehicle body motion state; use the vehicle-mounted sensors to obtain the longitudinal and lateral motion speeds and yaw angular velocity of the vehicle body in real time;

[0085] The information acquisition method is:

[0086] The longitudinal vehicle speed information is obtained by the on-board wheel speed sensor, which is installed on the vehicle and is an essential sensor for the vehicle; the yaw angular velocity is obtained by the on-board inertial measurement unit; and the vehicle lateral speed is obtained by the vehicle state estimation module.

[0087] Step 2: Target path point matching: Use the vehicle's GPS to obtain the vehicle's location information, extract the target path information from the trajectory planning layer, and match the appropriate trajectory to track the target point.

[0088] The information acquisition method is:

[0089] The vehicle's location information is obtained by the on-board GPS module; the target path information is obtained through the trajectory planning layer of the autonomous driving program.

[0090] The third step is adaptive dynamic modeling; a three-degree-of-freedom dynamic model of the vehicle is established for trajectory tracking, which includes the longitudinal, lateral and yaw directions of the vehicle and can adapt to changes in the number of vehicle axles.

[0091] The dynamic model of a multi-axle vehicle is:

[0092] Define j=1 and j=2 to represent the left and right wheels of the vehicle respectively, and i represents the i-th axle starting from the front end of the vehicle. The total mass of the vehicle is m, and the lateral velocity at the center of mass of the vehicle in the body coordinate system is v y , the longitudinal velocity at the center of mass of the vehicle in the body coordinate system is v x , the vehicle's center of mass sideslip angle is β, and the vehicle's yaw rate is ω r , the moment of inertia of the vehicle around the z-axis is I,l i represents the distance from the i-th axle to the center of mass of the vehicle, and the lateral force F provided by a single tire yij , the lateral force provided by a single shaft is F yi , the wheel slip angle is α, the wheel steering angle is δ, the wheelbase is d, and the additional direct yaw moment of the vehicle is M z The tire cornering stiffness is k, and the differential of the yaw rate with respect to time t is

[0093] The lateral dynamics and yaw dynamics models of the whole vehicle are as follows:

[0094]

[0095] The lateral force of the wheel is expressed by the following formula:

[0096]

[0097] T is the coefficient matrix. When the axis i exists, t i is a positive integer 1, otherwise it is 0. The expression of α is as follows:

[0098]

[0099] Define the total longitudinal force on the vehicle as F x , the drag coefficient is C D , the frontal area of ​​the vehicle is A f , the air density is ρ0, and the interference term is d1.

[0100] The longitudinal dynamics model of the whole vehicle is as follows:

[0101]

[0102] The fourth step is to design the speed tracking controller. According to the longitudinal dynamics model of the vehicle, the sliding mode control method is used to solve the longitudinal force required by the vehicle to achieve the effect of tracking the specified speed.

[0103] Define c, k2, ε, and D as positive constants, and the target longitudinal velocity is v xd , the actual longitudinal velocity is v x , the difference between the actual longitudinal velocity and the expected velocity is e v , the sliding surface function is s, and the symbol of the hyperbolic tangent function is tanh.

[0104] The velocity tracking error is defined as:

[0105] e v =v x -v xd

[0106] Introduce the integral term into the sliding mode function:

[0107]

[0108] The Lyapunov function is defined as:

[0109]

[0110] In order to satisfy the Lyapunov stability condition, that is, The control rate of the longitudinal force of the designed vehicle is

[0111]

[0112] The fifth step is to design the trajectory tracking controller; obtain the tracking error model based on the Frenet coordinate system and the vehicle dynamics model, and calculate the wheel angle and the yaw moment of the vehicle based on the nonlinear model predictive control algorithm.

[0113] Define the prediction time domain as N, the cost function as J, the weight matrix of the cost function as Q, R1, R2, and the yaw angle of the vehicle as The error between the vehicle center of mass and the target point is e d , the difference between the vehicle's yaw angle and the desired yaw angle is The rate of change of the heading angle of the target point is θ r , the curvature of the path point is k, the change rate of the horizontal coordinate at the path point in the Frenet coordinate system is s, the change rate of the control amount is Δu, and the maximum and minimum values ​​of the control amount are u respectively min and u max , l(x,u), g(x,u), and f(x,u) are functions of x and u.

[0114] The vehicle tracking error model is obtained through the Frenet coordinate system

[0115]

[0116] Taking the derivative of both sides of the above equations, we get:

[0117]

[0118] Combined with the vehicle's lateral dynamics model:

[0119]

[0120] The state variables of the system are:

[0121]

[0122] The control amount is

[0123] u=[δ 11 δ 12 δ 21 δ 22 δ 31 δ 32 δ 41 δ 42 δ 51 δ 52 δ 61 δ 62 ΔM z ] T

[0124] According to the above derivation, the state space equation of the vehicle power system can be obtained:

[0125]

[0126] Set the path tracking stage cost function to

[0127]

[0128] Then at time t, the optimal control problem of NMPC can be expressed as follows:

[0129]

[0130] u min ≤u(t+i)≤u max

[0131] The sixth step is optimal distribution of tire force. Based on the definition of tire adhesion and the requirements of the longitudinal force and yaw moment of the vehicle output by the upper-level controller, the quadratic programming method is used to optimally distribute the longitudinal tire force of each wheel and convert it into motor torque.

[0132] Define the longitudinal force of a single tire in the tire coordinate system as F xijThe sum of the longitudinal forces of the tires on a single axis is mapped to the longitudinal axis in the vehicle coordinate system as F xi , the road adhesion coefficient is μ, and the vertical load of the tire is F zij The minimum and maximum torques of the motor are T min and T max , the motor reduction ratio is g r , the tire radius is r, and h(w) is a function of w.

[0133] The longitudinal force and yaw moment of the vehicle are obtained from the fourth and fifth steps, and their relationship with each tire force is:

[0134]

[0135] in,

[0136]

[0137]

[0138] w=[F x11 F x12 F x21 F x22 F x31 F x32 F x41 F x42 F x51 F x52 F x61 F x62 ] T

[0139] The tire adhesion is defined as:

[0140] ρ=T1Ew

[0141] in

[0142] T1=diag((t1,t1),(t2,t2),(t3,t3),(t4,t4),(t5,t5),(t6,t6))

[0143]

[0144] The optimization goal is to ensure that the longitudinal force of the vehicle generated by the longitudinal force of each tire is consistent with the direct yaw moment and the command of the upper controller, and to minimize the total adhesion of the tire:

[0145] minJ=||T1Ew|| 2

[0146] st.h(w)=v

[0147]

[0148] Where v = [F xd ΔM d ] T After obtaining the longitudinal force of each tire, it is converted into a motor torque command:

[0149] T ij =F xij r / g r

[0150] End: The multi-axle electric vehicle operates according to the steering angle and torque given by the integrated controller until the autonomous driving mode is exited.

[0151] It will be understood by those skilled in the art that, unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as those generally understood by those skilled in the art to which this application belongs. It should also be understood that terms such as those defined in common dictionaries should be understood to have meanings consistent with the meanings in the context of the prior art, and will not be interpreted with idealized or overly formal meanings unless defined as herein.

[0152] The meaning of "and / or" described in this application means that the situations where each exists alone or both exist at the same time are included.

[0153] Based on the above ideal embodiments of the present invention, the relevant staff can make various changes and modifications without departing from the technical concept of the present invention through the above description. The technical scope of the present invention is not limited to the contents of the specification, and its technical scope must be determined according to the scope of the claims.

Claims

1. A reconfigurable distributed drive multi-axis vehicle trajectory tracking method, characterized in that: The following steps are involved: (1) Obtain the longitudinal and lateral motion speeds and yaw angular velocity of the vehicle body in real time; (2) Obtain the vehicle's location information, target path information, and match the trajectory to track the target point; (3) Establish a three-degree-of-freedom dynamic model of the vehicle for trajectory tracking. The three degrees of freedom include the longitudinal, lateral, and yaw directions of the vehicle; (4) Based on the longitudinal dynamics model of the vehicle, the sliding mode control method is used to solve the longitudinal force required by the vehicle to achieve the effect of tracking the specified speed; (5) Based on the Frenet coordinate system and the vehicle dynamics model, the tracking error model is obtained, and the wheel angle and the vehicle yaw moment are calculated based on the nonlinear model predictive control algorithm; (6) Based on the definition of tire adhesion and the requirements of the longitudinal force and yaw moment of the vehicle output by the upper controller, the quadratic programming method is used to optimally distribute the longitudinal tire force of each wheel and convert it into motor torque; (7) Control the vehicle's steering system and wheel motors to execute wheel steering angle and torque commands.

2. The reconfigurable distributed drive multi-axis vehicle trajectory tracking method according to claim 1, characterized in that: In the step (3): The lateral dynamics and yaw dynamics models of the whole vehicle are as follows: The lateral force of the wheel is expressed by the following formula: T is the coefficient matrix. When the axis i exists, t i is a positive integer 1, otherwise it is 0; the expression of α is as follows: The symbols and numbers in the above formula are explained: j = 1 and j = 2 represent the left and right wheels respectively, i represents the i-th axle counting from the front end of the vehicle, m is the total mass of the vehicle, v y is the lateral velocity at the center of mass of the vehicle in the body coordinate system, v x is the longitudinal velocity at the center of mass of the vehicle in the body coordinate system, ω r is the yaw rate of the vehicle, I is the moment of inertia of the vehicle about the z-axis, l i represents the distance from the i-th axis to the center of mass of the vehicle, F yij Indicates the lateral force provided by a single tire, F yi represents the lateral force provided by a single axle, α is the side slip angle of the wheel, δ is the wheel turning angle, d is the wheelbase, M z is the additional direct yaw moment of the vehicle, k is the tire cornering stiffness, represents the differential of yaw rate with respect to time t; The longitudinal dynamics model of the whole vehicle is as follows: Among them, F x is the sum of the longitudinal forces acting on the vehicle in the body coordinate system, C D Indicates the drag coefficient, A f is the frontal area of ​​the vehicle, ρ0 is the air density, and d1 is the interference term.

3. The reconfigurable distributed drive multi-axis vehicle trajectory tracking method according to claim 2 is characterized in that: The step (4) comprises: The velocity tracking error is defined as: yes v =v x -v xd Introduce the integral term into the sliding mode function: Define the Lyapunov function as: In order to satisfy the Lyapunov stability condition, that is, The control rate of the longitudinal force of the designed vehicle is Explain the symbols and numbers in the above formula: c, k2, ε, D are all positive constants, v xd is the target longitudinal velocity, v x is the actual longitudinal velocity, e v is the difference between the actual longitudinal velocity and the expected velocity, s is the sliding surface function, and in order to mitigate the chattering phenomenon that is easily generated by ordinary sliding mode controllers, the hyperbolic tangent function tanh is used instead of the ordinary switching function:

4. The reconfigurable distributed drive multi-axis vehicle trajectory tracking method according to claim 3 is characterized in that: The step (5) comprises: The vehicle tracking error model is obtained through the Frenet coordinate system Taking the derivative of both sides of the above equations, we get: Combined with the vehicle's lateral dynamics model: The state variables of the system are: The control amount is u=[δ 11 d 12 d 21 d 22 d 31 d 32 d 41 d 42 d 51 d 52 d 61 d 62 DM z ] T The state-space equations of the vehicle dynamics system are: Set the path tracking stage cost function to Then at time t, the optimal control problem of nonlinear model predictive control NMPC is expressed as follows: u min ≤u(t+i)≤u max The symbols and numbers in the above formula are explained: N is the prediction time domain, J represents the cost function, and the weight matrix of the cost function is Q, R1, R2, is the yaw angle of the vehicle, e d represents the error between the vehicle's center of mass and the target point, is the difference between the vehicle's yaw angle and the desired yaw angle, θ r is the rate of change of the heading angle of the target point, k1 is the curvature of the path point, is the rate of change of the horizontal coordinate at the path point in the Frenet coordinate system, Δu represents the rate of change of the control amount, u min and u max are the maximum and minimum values ​​of the controlled quantities respectively, l(x,u), g(x,u), and f(x,u) represent functions of x and u.

5. The reconfigurable distributed drive multi-axis vehicle trajectory tracking method according to claim 4 is characterized in that: The step (6) comprises: The longitudinal force and yaw moment of the vehicle are obtained from steps (4) and (5), and their relationship with each tire force is as follows: in, w=[F x11 F x12 F x21 F x22 F x31 F x32 F x41 F x42 F x51 F x52 F x61 F x62 ] T The tire adhesion is defined as: ρ=T1Ew in T1=diag((t1,t1),(t2,t2),(t3,t3),(t4,t4),(t5,t5),(t6,t6)) The optimization goal is to ensure that the longitudinal force of the vehicle generated by the longitudinal force of each tire is consistent with the direct yaw moment and the command of the upper controller, and to minimize the total adhesion of the tire: my J=||T1Ew|| 2 st.h(w)=v Where v = [F xd ΔM d ] T , after obtaining the longitudinal force of each tire, convert it into motor torque command: T ij =F xij r / g r Explain the symbols and numbers in the above formula: F xij is the longitudinal force of a single tire in the tire coordinate system, F xi It represents the mapping of the sum of the longitudinal forces of the tires on a single axle to the longitudinal axis in the vehicle coordinate system, μ is the adhesion coefficient of the road surface, and F zij Indicates the vertical load of the tire, T min and T max They are the minimum and maximum torque of the motor, g r is the reduction ratio of the motor, r is the tire radius, and h(w) represents the function of w.

6. A transport device, comprising a vehicle body, characterized in that: Also includes: Sensors are used to obtain the longitudinal and lateral movement speeds, yaw angular velocity and position information of the vehicle body in real time; The controller controls the steering system and wheel motors of the vehicle body according to the detection information of the sensor to execute the adaptive trajectory tracking method of the reconfigurable distributed drive multi-axle vehicle as described in any one of claims 1 to 5.

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