Trajectory tracking control method and system for double-vehicle splicing unmanned off-road motorcycle

Abstract

The invention discloses a trajectory tracking control method and system for a double-vehicle spliced unmanned off-road motorcycle. The method comprises the following steps: receiving an expected global path and speed information; collecting the current motion state of the vehicle through a sensor; the global path is converted to a vehicle body coordinate system, and expected trajectory points are generated based on prediction time domain sampling; constructing a kinematic differential model and linearizing the kinematic differential model into a time discrete prediction model; constructing a multi-constraint optimization problem by utilizing model predictive control, and solving to obtain an expected acceleration and front wheel turning angles of the inner and outer side bicycles; converting the expected rotation angle into left and right front wheel rotation angle instructions through a lower-layer controller, and distributing torque of left and right driving motors; and finally outputting a steering and driving control instruction to realize trajectory tracking. According to the method, the technical problem of robust and safe trajectory tracking is solved, and the self-adaptive capability of the double-vehicle spliced motorcycle to cope with a dynamic complex interaction obstacle environment in a field environment is improved.

Description

TECHNICAL FIELD

[0001] The present application belongs to the technical field of motorcycle trajectory tracking control, in particular to a double-motorcycle splicing unmanned off-road motorcycle trajectory tracking control method and system. BACKGROUND

[0002] Off-road motorcycles have strong maneuverability, and are of great significance for carrying out material carrying and manned maneuvering tasks in complex field environments, but they face the problem of limited carrying capacity. Splicing off-road motorcycles have great prospects in improving the loading capacity in material carrying and manned maneuvering tasks. In order to further reduce labor costs and improve autonomy and intelligence, splicing unmanned off-road motorcycles have great research and development significance. Under this background, trajectory tracking control technology is the "hands and feet" of splicing unmanned off-road motorcycles, directly determining the motion behavior of the motorcycle, and having a significant impact on its environmental adaptability and safety. However, there is currently a lack of research and application of splicing off-road motorcycles, especially in the trajectory tracking control of splicing unmanned off-road motorcycles. How to develop advanced trajectory tracking technology for its motion characteristics is an important challenge. SUMMARY

[0003] The present application aims to provide a double-motorcycle splicing unmanned off-road motorcycle trajectory tracking control method and system, which solves the problem of robust and safe trajectory tracking technology and improves the adaptive ability of double-motorcycle splicing motorcycles in dynamic complex interactive obstacle environments in field environments.

[0004] In order to achieve the purpose of the present application, on the one hand, the present application provides a double-motorcycle splicing unmanned off-road motorcycle trajectory tracking control method, comprising the following steps:

[0005] S1, receiving the expected global path information and the expected global speed information of the double-motorcycle splicing motorcycle;

[0006] S2, obtaining the motion state of the double-motorcycle splicing motorcycle at the current time by collecting sensor information of the double-motorcycle splicing motorcycle;

[0007] S3, based on the expected global path and the expected global speed of the double-motorcycle splicing motorcycle, sampling to obtain the expected trajectory point of the double-motorcycle splicing motorcycle;

[0008] S4, combining the motion state of the double-motorcycle splicing motorcycle at the current time, constructing a kinematic differential model, and on this basis, establishing a linear time-discrete prediction model;

[0009] S5, based on the linear time-discrete prediction model, using model predictive control method to construct trajectory tracking upper-layer control multi-constraint optimization problem to track the expected trajectory point of the double-motorcycle splicing motorcycle, and determining the expected acceleration and expected inner and outer single motorcycle front wheel steering angle of the vehicle.

[0010] S6, constructing a trajectory tracking lower controller and tracking the desired acceleration of the double-spliced motorcycle and the desired inner and outer single motorcycle front wheel turning angles, determining the lower steering and driving control instructions of the double-spliced motorcycle;

[0011] S7, controlling the motion of the double-spliced motorcycle by using the lower control instructions of the double-spliced motorcycle.

[0012] In another aspect, the application also provides a system of a trajectory tracking control method for a double-spliced unmanned off-road motorcycle, comprising the following modules:

[0013] A global path planning module for outputting a desired global path of the double-spliced motorcycle to a trajectory tracking upper control module;

[0014] A speed planning module for outputting a desired global speed of the double-spliced motorcycle to the trajectory tracking upper control module;

[0015] A data acquisition module for acquiring sensor information of the double-spliced motorcycle to obtain its motion state at the current time, and outputting to the trajectory tracking upper control module and the trajectory tracking lower control module;

[0016] A trajectory tracking upper control module for determining the desired acceleration of the double-spliced motorcycle and the desired inner and outer single motorcycle front wheel turning angles, and outputting to the trajectory tracking lower control module;

[0017] A trajectory tracking lower control module for tracking the desired acceleration of the double-spliced motorcycle and the desired inner and outer single motorcycle front wheel turning angles, determining the lower control instructions of the double-spliced motorcycle, and outputting to a steering and driving motor control module;

[0018] A steering and driving motor control module for tracking the lower control instructions of the double-spliced motorcycle to control the motion of the vehicle.

[0019] Compared with the prior art, the application has the following advantages: (1) the application uses model predictive control to construct a trajectory tracking control multi-constraint optimization mechanism for a double-spliced unmanned off-road motorcycle, introduces multi-dimensional motorcycle kinematic control constraints, and realizes robust and safe trajectory tracking capability; (2) the application is suitable for strong dynamic off-road environments, including dynamic obstacle avoidance and dynamic interaction control, and can improve the adaptive ability of the double-spliced motorcycle in dealing with dynamic complex interactive obstacle environments in off-road environments; (3) the application first proposes a trajectory tracking control method for a double-spliced unmanned off-road motorcycle.

[0020] To more clearly illustrate the functional characteristics and structural parameters of the application, the following further describes the application in conjunction with the drawings and specific embodiments. BRIEF DESCRIPTION OF DRAWINGS

[0021] The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this application, illustrate embodiments of the application and together with the description serve to explain the application. In the drawings:

[0022] Figure 1 is a step flow chart of the application;

[0023] Figure 2 is a schematic diagram of the upper control principle of the application. DETAILED DESCRIPTION

[0024] The technical solutions in the embodiments of the application will be clearly and completely described below with reference to the drawings in the embodiments of the application. Obviously, the described embodiments are only part of the embodiments of the application, rather than all the embodiments of the application. Based on the embodiments in the application, all other embodiments obtained by a person of ordinary skill in the art without creative work fall within the protection scope of the application.

[0025] A trajectory tracking control method for a double-motorcycle splicing unmanned off-road motorcycle, combining Figure 1 , comprises the following steps:

[0026] S1, receiving expected global path information and expected global speed information of the double-motorcycle splicing motorcycle;

[0027] S2, obtaining the motion state of the double-motorcycle splicing motorcycle at the current time by collecting sensor information of the double-motorcycle splicing motorcycle;

[0028] The motion state of the current time obtained by the S2 collection comprises: the speed and acceleration of the double-motorcycle splicing motorcycle rear axle center in the vehicle body coordinate system; the heading angle of the double-motorcycle splicing motorcycle; the turning angles of the left front wheel and the right front wheel of the double-motorcycle splicing motorcycle 、 .

[0029] S3, sampling the expected trajectory point of the double-motorcycle splicing motorcycle based on the expected global path and the expected global speed of the double-motorcycle splicing motorcycle;

[0030] S3-1, converting the obtained expected global path of the double-motorcycle splicing motorcycle to the vehicle body coordinate system to obtain the expected path of the double-motorcycle splicing motorcycle in the vehicle body coordinate system;

[0031] S3-2, taking the rear axle center of the double-motorcycle splicing motorcycle as the origin, and sampling the expected trajectory point of the double-motorcycle splicing motorcycle at a sampling time interval and the double-motorcycle splicing desired global speed obtained in step S1, the double-motorcycle splicing desired path in the body coordinate system is sampled along the longitudinal axis direction of the body coordinate system within the prediction time domain, and the sampling step number is 10 in this embodiment, so that the double-motorcycle splicing desired trajectory point is obtained wherein, is a prediction time domain step number index, is a prediction time domain step number, is a longitudinal coordinate of the desired trajectory point, is a lateral coordinate of the desired trajectory point, is a heading angle of the desired trajectory point;

[0032] S4, in combination with the motion state of the double-motorcycle splicing motorcycle at the current time, a kinematic differential model is constructed, and on this basis, a linearized time-discrete prediction model is established;

[0033] S4-1, a kinematic differential model of the double-motorcycle splicing motorcycle is constructed:

[0034] ;

[0035] wherein, , are first derivatives of the longitudinal and lateral positions of the rear axle center of the double-motorcycle splicing motorcycle in the body coordinate system, respectively; is a first derivative of the speed of the rear axle center of the double-motorcycle splicing motorcycle in the body coordinate system; is a first derivative of the heading angle of the double-motorcycle splicing motorcycle; is the speed of the rear axle center of the double-motorcycle splicing motorcycle in the body coordinate system; is the heading angle of the double-motorcycle splicing motorcycle; is the acceleration of the rear axle center of the double-motorcycle splicing motorcycle in the body coordinate system; = 1.5m, = 1m are the wheelbase and the track of the double-motorcycle splicing motorcycle, respectively; is the turning angle of the inner front wheel of the double-motorcycle splicing motorcycle when turning;

[0036] S4-2, based on the kinematic differential model of the double-motorcycle splicing motorcycle, the state variables , the output variables , and the control variables of the model are defined:

[0037] ;

[0038] wherein, are the longitudinal and lateral positions of the rear axle center of the double-motorcycle splicing motorcycle in the body coordinate system, respectively;

[0039] S4-3. Construct a linearized time discrete prediction model based on state variables, output variables, and control variables:

[0040] ;

[0041] in, To predict the first in the time domain One state variable, To predict the first in the time domain One output variable; To predict the first in the time domain One state variable, To predict the first in the time domain One control variable; It is the identity matrix; This represents the differential model constructed in step S4-1; , The first The state variables and control variables of a reference system operating point can be constructed using the control variables obtained from the previous time step and the corresponding state prediction trajectory. This is the state-output transition matrix. .

[0042] S5. Based on a linearized time-discrete prediction model, a multi-constraint optimization problem for upper-level control of trajectory tracking is constructed using model predictive control methods to track the desired trajectory points of a two-vehicle spliced ​​motorcycle, determining the desired acceleration of the vehicles and the desired inner and outer front wheel angles of the individual vehicles; combined with... Figure 2 ,

[0043] S5-1. Based on a linearized time-discrete prediction model, construct a multi-constraint optimization problem for upper-level control of trajectory tracking to track the desired trajectory points of a two-vehicle spliced ​​motorcycle:

[0044] ;

[0045] in, To optimize the objective; To predict the first in the time domain One output variable; To predict the first in the time domain A vector of points representing the expected trajectory of a vehicle; The weight matrix is ​​used to track the desired reference signal; To control the total number of steps in the time domain; To control the time-domain step index; To control the first in the time domain One control variable; The weight matrix is ​​used to suppress the magnitude of the control variables; To control the first in the time domain a control variable increment; a weight matrix for suppressing the control variable increment; a relaxation factor vector; a weight matrix for suppressing the relaxation factor from being too large; , a minimum value and a maximum value of the i-th state variable in the prediction horizon; , , a minimum value and a maximum value of the i-th output variable in the prediction horizon; , , a minimum value and a maximum value of the i-th control variable in the control horizon, i.e., the acceleration and the steering angle of the front wheel of the two-wheeled motorcycle, which are respectively taken as , , ; , a minimum value and a maximum value of the i-th control variable increment in the control horizon, i.e., the acceleration change and the steering angle change of the front wheel of the two-wheeled motorcycle, which are respectively taken as , , ; a maximum value of the relaxation factor vector; a control variable increment sequence composed of the control variable increments from the 1st to the i-th in the control horizon, which is defined as ; ;

[0046] S5-2, solve the trajectory tracking upper-layer control multi-constraint optimization problem to obtain a control vector increment sequence, and add the first group of vector elements in the control vector increment sequence to the control variables of the two-wheeled motorcycle at the previous moment to obtain the expected acceleration and the expected inside single-wheel front wheel steering angle of the two-wheeled motorcycle at the next moment;

[0047] S5-3, based on the expected inside single-wheel front wheel steering angle of the two-wheeled motorcycle at the next moment and the Ackerman steering constraint, calculate the expected outside single-wheel front wheel steering angle of the two-wheeled motorcycle:

[0048] .

[0049] S6, construct a trajectory tracking lower-layer controller and track the expected acceleration and the expected inside and outside single-wheel front wheel steering angles of the two-wheeled motorcycle to determine the lower-layer steering and driving control instructions of the two-wheeled motorcycle;

[0050] S6-1, converting the desired inner side and outer side single vehicle front wheel turning angle of the double vehicle spliced motorcycle obtained in step S5 into a double vehicle spliced motorcycle left front wheel and right front wheel turning angle 、 、 , and outputting the left and right sides to the single vehicle steering motor controllers respectively to perform turning angle tracking, to obtain left and right single vehicle steering motor steering torque, i.e., double vehicle spliced motorcycle lower layer steering control instructions, wherein the conversion rule is as follows:

[0051] ;

[0052] S6-2, constructing a left and right single vehicle driving motor controller to track the desired acceleration obtained in step S5 , to obtain left and right single vehicle driving motor driving torque, i.e., double vehicle spliced motorcycle lower layer driving control instructions:

[0053] ;

[0054] wherein, 、 are the left and right single vehicle driving motor driving torque respectively; 、 are the left and right single vehicle rear wheel radii respectively; is the total mass of the double vehicle spliced motorcycle.

[0055] S7, using the lower layer control instructions of the double vehicle spliced motorcycle to control the double vehicle spliced motorcycle motion.

[0056] A double vehicle spliced unmanned off-road motorcycle trajectory tracking control method system of the application comprises the following modules:

[0057] A global path planning module for outputting a double vehicle spliced motorcycle desired global path to a trajectory tracking upper layer control module; (there are various ways to obtain a desired global path in the prior art, such as A * algorithm, D * algorithm, RRT algorithm, etc., which are not described in detail as they are not the focus of the case);

[0058] A speed planning module for outputting a double vehicle spliced motorcycle desired global speed to a trajectory tracking upper layer control module; (there are various ways to obtain a desired global speed in the prior art, such as a global path curvature constraint maximum speed algorithm, an obstacle constraint maximum speed algorithm, etc., which are not described in detail as they are not the focus of the case);

[0059] ​A data acquisition module is configured to acquire sensor information of the two-wheeled spliced motorcycle to obtain a motion state of the two-wheeled spliced motorcycle at a current time, and output the motion state to a trajectory tracking upper control module and a trajectory tracking lower control module.

[0060] The trajectory tracking upper control module is configured to determine a desired acceleration of the two-wheeled spliced motorcycle and desired front wheel turning angles of the inner and outer single motorcycles, and output the desired acceleration and the desired front wheel turning angles to the trajectory tracking lower control module.

[0061] The trajectory tracking lower control module is configured to track the desired acceleration of the two-wheeled spliced motorcycle and the desired front wheel turning angles of the inner and outer single motorcycles, determine a lower control instruction of the two-wheeled spliced motorcycle, and output the lower control instruction to a steering and driving motor control module.

[0062] The steering and driving motor control module is configured to track the lower control instruction of the two-wheeled spliced motorcycle to control a motion of the vehicle.

[0063] It should be noted that, in the present document, the terms such as first and second are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Also, the terms "comprises", "comprising", or any other variations thereof are intended to cover non-exclusive inclusions, so that a process, method, article, or apparatus including a list of elements does not only include those elements, but also includes other elements not explicitly listed, or further includes elements inherent in such a process, method, article, or apparatus.

[0064] Although the embodiments of the present application have been shown and described, it should be understood by those ordinary skilled in the art that various changes, modifications, substitutions and variations can be made to the embodiments without departing from the principles and spirit of the present application, and the scope of the present application is defined by the appended claims and their equivalents.

Claims

1. A trajectory tracking and control method for a dual-vehicle splicing unmanned off-road motorcycle, characterized in that, Includes the following steps: S1. Receive the expected global path information and expected global speed information of the dual-vehicle splicing motorcycle; S2. Obtain the motion state of the dual-vehicle splicing motorcycle at the current moment by collecting sensor information; S3. Based on the expected global path and expected global speed of the dual-vehicle spliced ​​motorcycle, sample the expected trajectory points of the dual-vehicle spliced ​​motorcycle; S4. Combine the motion state of the two-vehicle splicing motorcycle at the current moment to construct a kinematic differential model, and on this basis, establish a linearized time discrete prediction model; S5. Based on the linear time discrete prediction model, the model predictive control method is used to construct a multi-constraint optimization problem for the upper-level control of trajectory tracking in order to track the expected trajectory points of the two-vehicle spliced ​​motorcycle and determine the expected acceleration of the vehicle and the expected inner and outer front wheel angles of the single vehicle. S6. Construct a trajectory tracking lower-level controller and track the desired acceleration of the dual-vehicle splicing motorcycle and the desired inner and outer front wheel angles of the single vehicle to determine the lower-level steering and drive control commands of the dual-vehicle splicing motorcycle. S7. The movement of the dual-vehicle splicing motorcycle is controlled using the lower-level control commands of the dual-vehicle splicing motorcycle.

2. The trajectory tracking control method for a double-bike spliced unmanned off-road motorcycle according to claim 1, characterized in that, The motion state acquired by S2 at the current moment includes: the velocity and acceleration of the rear axle center of the dual-vehicle splicing motorcycle in the vehicle coordinate system; the heading angle of the dual-vehicle splicing motorcycle; and the turning angles of the left and right front wheels of the dual-vehicle splicing motorcycle.

3. The trajectory tracking control method for a double-bike spliced unmanned off-road motorcycle according to claim 1, characterized in that, S3 specifically includes the following steps: S3-1. Transform the obtained expected global path of the two-vehicle spliced ​​motorcycle into the vehicle coordinate system to obtain the expected path of the two-vehicle spliced ​​motorcycle in the vehicle coordinate system. S3-2, taking the center of the rear axle of the two-wheeled spliced motorcycle as the origin, sampling the expected path of the two-wheeled spliced motorcycle in the body coordinate system in the longitudinal axis direction of the body coordinate system in the prediction time domain according to the sampling time interval and the expected global speed of the two-wheeled spliced motorcycle obtained in step S1, to obtain the expected trajectory point of the two-wheeled spliced motorcycle ; wherein, is the prediction time domain step index, is the prediction time domain step, is the longitudinal coordinate of the expected trajectory point, is the lateral coordinate of the expected trajectory point, is the heading angle of the expected trajectory point.

4. The trajectory tracking control method for a double-bike spliced unmanned off-road motorcycle according to claim 1, characterized in that, S4 specifically includes the following steps: S4-1. Construct a kinematic differential model of a dual-vehicle spliced ​​motorcycle; S4-2. Based on the kinematic differential model of the dual-vehicle spliced ​​motorcycle, define the state variables, output variables, and control variables of the model; S4-3. Based on the state variables, output variables, and control variables, construct a linearized time discrete prediction model.

5. The trajectory tracking control method for a double-bike spliced unmanned off-road motorcycle according to claim 4, characterized in that, The specific formula for S4-1 is as follows: ； in, , These are the first derivatives of the longitudinal and lateral positions of the rear axle center of the dual-vehicle splicing motorcycle in the vehicle coordinate system, respectively; The first derivative of the velocity of the rear axle center of the dual-vehicle splicing motorcycle in the vehicle coordinate system; The first derivative of the heading angle of the two-vehicle splicing motorcycle; The velocity of the rear axle center of the dual-vehicle splicing motorcycle in the vehicle coordinate system; The heading angle of a motorcycle with two vehicles joined together; The acceleration of the rear axle center of the dual-vehicle splicing motorcycle in the vehicle coordinate system; , These are the wheelbase and track width of the two-vehicle splicing motorcycle, respectively. This refers to the turning angle of the inner front wheel of the two-vehicle spliced ​​motorcycle when it turns.

6. The trajectory tracking control method of a double-bike spliced unmanned off-road motorcycle according to claim 5, characterized in that, The specific formula for S4-2 is as follows: ； wherein, are the longitudinal and lateral positions of the rear axle center of the two-wheeled motorcycle in the vehicle coordinate system, respectively, are state variables, are output variables, are control variables.

7. The trajectory tracking control method for a double-bike spliced unmanned off-road motorcycle according to claim 6, characterized in that, The specific formula for S4-3 is as follows: ； in, To predict the first in the time domain One state variable, To predict the first in the time domain One output variable; To predict the first in the time domain One state variable, To predict the first in the time domain One control variable; It is the identity matrix; This represents the differential model constructed in step S4-1; , The first The state variables and control variables of a reference system operating point can be constructed using the control variables obtained from the previous time step and the corresponding state prediction trajectory. This is the state-output transition matrix. .

8. The trajectory tracking control method for a double-bike spliced unmanned off-road motorcycle according to claim 7, characterized in that, S5 specifically includes the following steps: S5-1. Based on a linearized time discrete prediction model, construct a multi-constraint optimization problem for upper-level control of trajectory tracking to track the desired trajectory points of a two-vehicle spliced ​​motorcycle. S5-2, solve the trajectory tracking upper layer control multi-constraint optimization problem, obtain the control vector increment sequence, and add the first group of vector elements in the control vector increment sequence to the control variable of the double motorcycle at the last moment to obtain the expected acceleration of the double motorcycle at the next moment and the expected inside single-wheel front wheel steering angle ; S5-3, based on the next time two-wheeled spliced motorcycle expected inside single-wheel front wheel steering angle , and Ackerman steering constraint, calculate the expected outside single-wheel front wheel steering angle of the two-wheeled spliced motorcycle : 。 9. The trajectory tracking control method for a double-bike spliced unmanned off-road motorcycle according to claim 8, characterized in that, The specific formula for S5-1 is as follows: ； in, To optimize the objective; To predict the first in the time domain One output variable; To predict the first in the time domain A vector of points representing the expected trajectory of a vehicle; The weight matrix is ​​used to track the desired reference signal; To control the total number of steps in the time domain; To control the time-domain step index; To control the first in the time domain One control variable; The weight matrix is ​​used to suppress the magnitude of the control variables; To control the first in the time domain Increment of each control variable; The weight matrix is ​​used to suppress the increment of control variables; For the constraint relaxation factor vector; To suppress excessively large relaxation factors in the weight matrix; , To predict the first time domain The minimum and maximum values ​​of each state variable; , To predict the first time domain The minimum and maximum values ​​of each output variable; , To control the first time domain The minimum and maximum values ​​of the control variables; , To control the first time domain The minimum and maximum values ​​of the increments of each control variable; To constrain the maximum value of the relaxation factor vector; To control the time domain from the 1st to the th The sequence of control variable increments, consisting of 1 control variable increments, is defined as follows: .

10. The trajectory tracking and control method for a dual-vehicle splicing unmanned off-road motorcycle according to claim 9, characterized in that, S6 specifically includes the following steps: S6-1, Calculate the desired inner and outer single-vehicle front wheel angles of the dual-vehicle splicing motorcycle obtained in step S5. , Transformed into the left and right front wheel corners of a two-wheel splicing motorcycle , The commands are then output to the left and right single-vehicle steering motor controllers for angle tracking, resulting in the steering torque of the left and right single-vehicle steering motors. This is the lower-level steering control command for the dual-vehicle splicing motorcycle, and the conversion rules are as follows: ； S6-2. Construct the left and right single-vehicle drive motor controllers for the desired acceleration obtained in step S5. By tracking, the driving torque of the left and right single-vehicle drive motors is obtained, which is the lower-level drive control command of the dual-vehicle splicing motorcycle: ； in, , These represent the driving torque of the left and right single-vehicle drive motors, respectively. , These are the radii of the left and right rear wheels of the bicycle, respectively. The total mass of the motorcycle assembled from two vehicles.

11. A system for trajectory tracking and control of a dual-vehicle splicing unmanned off-road motorcycle according to any one of claims 1-10, characterized in that, Includes the following modules: The global path planning module is used to output the expected global path of the two-vehicle splicing motorcycle to the upper-level control module for trajectory tracking; The speed planning module is used to output the expected global speed of the dual-vehicle splicing motorcycle to the upper-level control module for trajectory tracking. The data acquisition module is used to collect sensor information from the dual-vehicle splicing motorcycle to obtain its motion state at the current moment, and output it to the upper-level control module and the lower-level control module for trajectory tracking. The upper-level control module for trajectory tracking is used to determine the desired acceleration of the two-vehicle splicing motorcycle and the desired front wheel angles of the inner and outer single vehicles, and outputs them to the lower-level control module for trajectory tracking. The trajectory tracking lower-level control module is used to track the desired acceleration and desired inner and outer front wheel angles of the two-vehicle splicing motorcycle, determine the lower-level control commands for the two-vehicle splicing motorcycle, and output them to the steering and drive motor control modules. The steering and drive motor control module is used to track the lower-level control commands of the dual-vehicle splicing motorcycle to control the vehicle's movement.

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