Trajectory tracking hierarchical control method and system for non-coaxial two-wheeled unmanned platform

Abstract

The present application discloses a non-coaxial two-wheeled unmanned platform trajectory tracking hierarchical control method and system, which relates to the field of intelligent robot technology. The method includes obtaining the real-time posture information and coordinate information of the unmanned motorcycle during driving; inputting the posture data and coordinate information into the on-board computing unit, and based on the dual closed-loop trajectory tracking control strategy, correcting the driving deviation of the non-coaxial two-wheel system on the premise of ensuring the stability of the vehicle to track the desired trajectory; the on-board computing unit is pre-set with an offline desired trajectory, a trajectory tracking controller, a self-balancing controller, a kinematic model of the unmanned motorcycle system, and a dynamic model of the unmanned motorcycle system; the dual closed-loop trajectory tracking control strategy includes an outer-loop posture planning layer and an inner-loop balance control layer. The present application adopts a dual closed-loop control structure, which can effectively improve the trajectory tracking accuracy and stability of the unmanned motorcycle.

Description

Technical Field

[0001] The present application relates to the field of intelligent robot technology, and in particular to a trajectory tracking hierarchical control method and system for a non-coaxial two-wheeled unmanned platform. Background Art

[0002] Unmanned platforms are playing an increasingly important role. Two-wheeled unmanned platforms are widely used in casualty transportation and reconnaissance and strike missions in special environments such as narrow spaces and complex terrains due to their compact structure and maneuverability. Since the two wheels of non-coaxial two-wheeled unmanned platforms are arranged on the front and rear axles respectively, they have higher flexibility and stronger climbing and obstacle crossing capabilities. Therefore, the development of non-coaxial two-wheeled unmanned platform technology is of great significance for seizing a favorable situation in war and carrying out combat support tasks. However, this non-coaxial two-wheeled unmanned platform is essentially an under-driven system, and its steering subsystem and drive subsystem are coupled with each other. In addition, the unmodeled dynamics and external interference make the trajectory tracking control of the non-coaxial two-wheeled unmanned platform extremely challenging.

[0003] Non-coaxial two-wheeled unmanned platforms have higher maneuverability and passability than coaxial two-wheeled platforms, especially in narrow and rugged terrain environments. Because their two wheels are not located on the same axis, they show unique advantages and broad application prospects in logistics distribution, disaster relief, military reconnaissance and other scenarios. At the same time, due to the lack of direct drive in the lateral direction, their balance control faces challenges, and stable movement needs to be achieved through self-balancing control of the front wheel steering. Existing research on non-coaxial two-wheeled unmanned platform technology is mostly focused on its balance control, and its trajectory tracking control in uncertain environments is rarely involved. Summary of the invention

[0004] The purpose of this application is to provide a non-coaxial two-wheeled unmanned platform trajectory tracking hierarchical control method and system, which adopts a dual closed-loop trajectory tracking control strategy and can effectively improve the trajectory tracking accuracy and stability of the unmanned motorcycle.

[0005] To achieve the above objectives, this application provides the following solutions:

[0006] In a first aspect, the present application provides a non-coaxial two-wheeled unmanned platform trajectory tracking hierarchical control method, comprising:

[0007] Obtain the real-time position and coordinate information of the unmanned motorcycle during driving;

[0008] The posture data and coordinate information are input into the on-board computing unit, and based on the double closed-loop trajectory tracking control strategy, the driving deviation of the non-coaxial two-wheel system is corrected to track the expected trajectory while ensuring the stability of the vehicle; the on-board computing unit is pre-set with an offline expected trajectory, a trajectory tracking controller, a self-balancing controller, a kinematic model of the unmanned motorcycle system and a dynamic model of the unmanned motorcycle system; the double closed-loop trajectory tracking control strategy includes an outer-loop posture planning layer and an inner-loop balance control layer; the outer-loop posture planning layer is composed of coordinate information, offline expected trajectory, trajectory tracking controller and kinematic model of the unmanned motorcycle system, and is used to output the expected handlebar angle of the unmanned motorcycle; the inner-loop balance control layer is composed of the expected handlebar angle output by the outer-loop posture planning layer, posture information, self-balancing controller and dynamic model of the unmanned motorcycle system, and is used to correct the driving deviation of the non-coaxial two-wheel system.

[0009] In a second aspect, the present application provides a non-coaxial two-wheeled unmanned platform trajectory tracking hierarchical control system, comprising:

[0010] An information acquisition module is used to obtain the real-time position and coordinate information of the unmanned motorcycle during driving;

[0011] A control module is used to input posture data and coordinate information into an on-board computing unit, and based on a double closed-loop trajectory tracking control strategy, correct the driving deviation of a non-coaxial two-wheel system to track a desired trajectory while ensuring vehicle stability; the on-board computing unit is pre-set with an offline desired trajectory, a trajectory tracking controller, a self-balancing controller, a kinematic model of an unmanned motorcycle system, and a dynamic model of an unmanned motorcycle system; the double closed-loop trajectory tracking control strategy includes an outer-loop posture planning layer and an inner-loop balance control layer; the outer-loop posture planning layer is composed of coordinate information, an offline desired trajectory, a trajectory tracking controller, and a kinematic model of an unmanned motorcycle system, and is used to output a desired handlebar angle of the unmanned motorcycle; the inner-loop balance control layer is composed of the desired handlebar angle, posture information, a self-balancing controller, and a dynamic model of the unmanned motorcycle system output by the outer-loop posture planning layer, and is used to correct the driving deviation of the non-coaxial two-wheel system.

[0012] According to the specific embodiments provided in this application, this application discloses the following technical effects:

[0013] The present application provides a non-coaxial two-wheeled unmanned platform trajectory tracking hierarchical control method and system, the method comprising: obtaining the real-time posture information and coordinate information of the unmanned motorcycle during driving; inputting the posture data and coordinate information into the vehicle-mounted computing unit, based on the double closed-loop trajectory tracking control strategy, correcting the driving deviation of the non-coaxial two-wheel system under the premise of ensuring the stability of the vehicle, so as to track the desired trajectory. In the double closed-loop trajectory tracking control strategy provided by the present application, the outer-loop posture planning layer is responsible for generating the desired posture trajectory, and the deviation from the desired trajectory is calculated by obtaining the current posture information of the unmanned motorcycle in real time. Then, the deviation information is passed to the inner-loop balance control layer to adjust the driving direction and speed of the unmanned motorcycle to ensure that it can accurately track the desired trajectory. The inner-loop balance control layer is mainly responsible for the stable driving of the unmanned motorcycle. Based on the dynamic model and kinematic model of the unmanned motorcycle system, the inner-loop balance control layer calculates the required control amount in real time to maintain the balance and stability of the unmanned motorcycle. Through the double closed-loop control structure, the outer-loop posture planning layer and the inner-loop balance control layer cooperate with each other to achieve precise control of the unmanned motorcycle. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0015] Figure 1 A schematic flow chart of a trajectory tracking hierarchical control method for a non-coaxial two-wheeled unmanned platform provided in one embodiment of the present application.

[0016] Figure 2 A flow chart of a dual closed-loop trajectory tracking control strategy provided in one embodiment of the present application.

[0017] Figure 3 A schematic diagram of the movement of an unmanned motorcycle platform in a geodetic coordinate system provided in one embodiment of the present application.

[0018] Figure 4 A schematic diagram of a control system provided in one embodiment of the present application.

[0019] Figure 5 A schematic diagram of the structure of a trajectory tracking hierarchical control system for a non-coaxial two-wheeled unmanned platform provided in one embodiment of the present application. DETAILED DESCRIPTION

[0020] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0021] In order to make the above-mentioned objects, features and advantages of the present application more obvious and easy to understand, the present application is further described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0022] Embodiment 1

[0023] like Figure 1 As shown, this embodiment provides a non-coaxial two-wheeled unmanned platform trajectory tracking hierarchical control method, including:

[0024] Step 101: Obtain the real-time position information and coordinate information of the unmanned motorcycle during driving.

[0025] Step 102: Input the posture data and coordinate information into the on-board computing unit, and based on the dual closed-loop trajectory tracking control strategy, correct the driving deviation of the non-coaxial two-wheel system to track the desired trajectory while ensuring the stability of the vehicle; the on-board computing unit is pre-set with an offline desired trajectory, a trajectory tracking controller, a self-balancing controller, an unmanned motorcycle system kinematic model and an unmanned motorcycle system dynamic model; the dual closed-loop trajectory tracking control strategy includes an outer-loop posture planning layer and an inner-loop balance control layer; the outer-loop posture planning layer is composed of coordinate information, offline desired trajectory, trajectory tracking controller and unmanned motorcycle system kinematic model, and is used to output the desired handlebar angle of the unmanned motorcycle; the inner-loop balance control layer is composed of the desired handlebar angle, posture information, self-balancing controller and unmanned motorcycle system dynamic model output by the outer-loop posture planning layer, and is used to correct the driving deviation of the non-coaxial two-wheel system.

[0026] Among them, the hierarchical layer in the trajectory tracking hierarchical control method of the non-coaxial two-wheeled unmanned platform refers to the outer loop posture planning layer and the inner loop balance control layer based on the double closed-loop trajectory tracking control strategy.

[0027] In some embodiments, when executing step 101, the specific steps may be as follows:

[0028] The on-board sensors are used to obtain the real-time position and coordinate information of the unmanned motorcycle during driving. Specifically, the MPU6050 gyroscope chip is used to obtain the body roll angle and roll angular velocity, the drive motor encoder is used to obtain the body forward velocity, the handlebar steering motor is used to obtain the handlebar angular velocity, and the UWB technology is used to obtain the real-time coordinates of the unmanned motorcycle.

[0029] In some embodiments, when executing step 102, the specific steps may be as follows:

[0030] The collected information is input into the on-board computing unit, which has been pre-configured with the offline desired trajectory, trajectory tracking controller, self-balancing controller, unmanned motorcycle system kinematic model and unmanned motorcycle system dynamic model. Among them, the coordinate information is used as the outer loop posture planning layer, which is processed by the offline desired trajectory, trajectory tracking controller and unmanned motorcycle system kinematic model, and the output result and posture information are used as the inner loop balance control layer, which is adjusted by the self-balancing controller and unmanned motorcycle system dynamic model. This dual closed-loop trajectory tracking control method can accurately correct the deviation of the non-coaxial two-wheel system during driving to track the desired trajectory while ensuring the balance of the vehicle.

[0031] Among them, for each module in the vehicle computing unit, the content is as follows:

[0032] For the “offline expected trajectory” of the outer ring pose planning layer, it mainly reveals the geodetic coordinates of the expected trajectory in the time domain. The state vector determined by the reference trajectory is: .

[0033] The "trajectory tracking controller" of the outer loop posture planning layer can quickly eliminate the error between the desired state and the actual state to obtain the desired forward speed. and angular velocity The actual state vector of the unmanned motorcycle is , so the trajectory tracking error can be expressed as In order to fully consider the non-holonomic characteristics of the unmanned motorcycle, the trajectory tracking deviation is appropriately transformed and the error vector is taken The components satisfy:

[0034] .

[0035] And design the trajectory generator as follows:

[0036] .

[0037] In the formula, ,and are the reference forward velocity and yaw angular velocity determined according to the reference trajectory, , , is a positive design parameter. The actual state vector of the unmanned motorcycle is , the trajectory tracking error can be expressed as . represents the reference forward speed, represents the reference heading angular velocity, represents the lateral error, represents the longitudinal error, represents the yaw angle error, Indicates the error in the x-axis direction of the geodetic coordinates, Represents geodetic coordinates y Axis direction error, represents the desired coordinates and yaw angle on the reference trajectory; Indicates the actual coordinates and yaw angle.

[0038] The "unmanned motorcycle system kinematic model" of the outer ring posture planning layer reveals the position and yaw angle of the unmanned motorcycle platform at any time in the geodetic coordinate system. The motion diagram of the unmanned motorcycle platform in the geodetic coordinate system is shown in the figure below: Figure 3 As shown, the vertical lines passing through the front and rear wheel center speeds intersect to obtain Q point, Q is the instantaneous rotation center of the platform. O ' X ' Y ' is the body coordinate system of the unmanned motorcycle platform, the origin of which is fixed at the center of the rear wheel, and the handlebar angle , wheelbase The coordinate system OXY is the earth coordinate system, where the yaw angle , the distance from the instantaneous rotation center to the front wheel , the distance from the instantaneous rotation center to the rear wheel , vehicle speed All are expressed in the geodetic coordinate system. The specific form of the kinematic model established in the geodetic coordinate system is as follows:

[0039] .

[0040] The "self-balancing controller" of the inner loop balance control layer can ensure that the body roll angle quickly converges to the expected value after obtaining the body posture state to achieve the balance effect. The computer control process includes multiple sampling cycles, and the collected parameters are discrete. For the unmanned motorcycle time-varying system, in the first i In a sampling period, the unmanned motorcycle system can be expressed as:

[0041] .

[0042] Consider the performance indicators:

[0043] .

[0044] in, J is the objective function, x is the state vector of the system, u is the control vector, is the initial moment.

[0045] Choose an appropriate weighting matrix Q and R , and solve the Riccati equation at this sampling point:

[0046] .

[0047] The value can be solved by Matlab In care() Function, the specific reference form is care(A,B,Q,R) .

[0048] Then solve the feedback gain coefficient of the i-th sampling period :

[0049] .

[0050] Therefore, the Riccati equation is solved for each period sampling point to obtain a time-varying feedback gain matrix , the control quantity is:

[0051] = .

[0052] The control system schematic diagram is as follows Figure 4 In each sampling period, the variable gain LQR controller first reads the speed of the unmanned motorcycle , thus obtaining the state matrix at this sampling point , then the designed weighting matrix Q and R The Riccati equation is solved online to obtain the feedback gain matrix of the sampling period Finally, the control quantity can be further obtained u Input into the unmanned motorcycle system to complete the control within the cycle. The controller includes 4 state variables and vehicle speed, a total of 5 input quantities.

[0053] The "unmanned motorcycle system dynamics model" of the inner loop balance control layer reveals the dynamic characteristics of the system and is the basis of the self-balancing and trajectory tracking controller. The unmanned motorcycle system studied in this application has multiple incomplete constraints. In order to focus on the impact of these incomplete constraints on the system during the modeling process, the dynamic modeling process will be implemented using the Chaplekin equation, as shown below:

[0054] .

[0055] In the formula, is the total kinetic energy of the system; is the kinetic energy of the system after substituting the nonholonomic constraints; For the The nonholonomic constraint equation The coefficients of the independent generalized velocities; and is the generalized coordinate of the system; is the nonholonomic constraint of the system; and are the number of independent generalized coordinates and nonholonomic constraints, respectively. Independent generalized coordinates The corresponding generalized force.

[0056] In order to solve the problem that the high coupling of state variables of the non-coaxial two-wheel system leads to the difficulty of dynamic modeling, the coordinate transformation method is used to express the translational kinetic energy and rotational kinetic energy of each rigid body in the system with the generalized coordinates of the system and substitute them into the Chaplekin equation to obtain a nonlinear dynamic model. The specific form is as follows:

[0057] .

[0058] In the formula, is the inertia matrix, is the centrifugal force-Coriolis force, is the gravity term, is the handlebar driving torque, is the front wheel drive torque, matrix element , ( i, j =1,2,3) is the roll angle and handlebar angle The function of represents the driving wheel rotation angle, The angular velocity of represents the angular acceleration of the corresponding variable.

[0059] Among them, in some embodiments, the trajectory tracking double closed-loop control method, its control idea is as follows:

[0060] The first step is to create a deviation between the expected state and the state of the vehicle body in real time feedback , and then the trajectory controller obtains the linear velocity required to track the trajectory and angular velocity .

[0061] The second step is to use the kinematic model to calculate the expected linear velocity. and angular velocity Get the desired handlebar angle required to follow the trajectory , the relationship is as follows:

[0062] .

[0063] Step 3: Turn the handlebar to the desired angle The inner loop system consisting of the dynamic model and the balance controller is substituted to realize the balance control.

[0064] The fourth step is to use the kinematic model to obtain the actual state of the system. Feedback to the reference input terminal to achieve outer loop control.

[0065] Through the above four steps, the tracking of the trajectory by the unmanned motorcycle can be converted into the tracking of the desired handlebar angle in each operation cycle, so as to achieve a good trajectory tracking effect in a sufficiently long time. The flow chart is as follows: Figure 2 shown.

[0066] Embodiment 2

[0067] like Figure 5 As shown, this embodiment provides a non-coaxial two-wheeled unmanned platform trajectory tracking hierarchical control system, including:

[0068] The information acquisition module 501 is used to obtain the real-time position information and coordinate information of the unmanned motorcycle during driving.

[0069] The control module 502 is used to input the posture data and coordinate information into the vehicle-mounted computing unit, and based on the dual closed-loop trajectory tracking control strategy, correct the driving deviation of the non-coaxial two-wheel system to track the expected trajectory while ensuring the stability of the vehicle; the vehicle-mounted computing unit is pre-set with an offline expected trajectory, a trajectory tracking controller, a self-balancing controller, an unmanned motorcycle system kinematic model and an unmanned motorcycle system dynamic model; the dual closed-loop trajectory tracking control strategy includes an outer-loop posture planning layer and an inner-loop balance control layer; the outer-loop posture planning layer is composed of coordinate information, offline expected trajectory, trajectory tracking controller and unmanned motorcycle system kinematic model, and is used to output the expected handlebar angle of the unmanned motorcycle; the inner-loop balance control layer is composed of the expected handlebar angle, posture information, self-balancing controller and unmanned motorcycle system dynamic model output by the outer-loop posture planning layer, and is used to correct the driving deviation of the non-coaxial two-wheel system.

[0070] Specifically, the information acquisition module 501 includes a posture information acquisition submodule and a coordinate information acquisition submodule.

[0071] The posture information acquisition submodule is used to obtain the roll angle and roll angular velocity of the unmanned motorcycle using the MPU6050 gyroscope chip, to obtain the forward velocity of the unmanned motorcycle using the drive motor encoder, and to obtain the handlebar angular velocity through the handlebar steering motor of the unmanned motorcycle.

[0072] The posture information acquisition submodule is used to obtain the real-time coordinates of the unmanned motorcycle using UWB technology.

[0073] In summary, this application has the following technical effects:

[0074] 1. This application aims to solve the problem of difficulty in dynamic modeling due to high coupling of state variables of non-coaxial two-wheel system. By adopting the modeling method of Chaplekin equation combined with energy method, a more accurate dynamic model can be established, laying the foundation for the design of self-balancing controller.

[0075] 2. This application aims to solve the problem that the forward velocity v and angular velocity w of the unmanned motorcycle need to converge quickly to the desired forward velocity and angular velocity, and designs a trajectory tracking controller to realize the conversion of errors to expected values, providing important support for the outer loop posture planning layer.

[0076] 3. This application aims to solve the problem of an unmanned motorcycle quickly tracking a desired trajectory while maintaining balance, and establishes a dual closed-loop trajectory tracking controller to provide new ideas for the control method of an unmanned motorcycle.

[0077] 4. This application can integrate the under-actuated characteristics of the platform, the physical constraints of the platform actuators and their interactive coupling characteristics. The established non-coaxial unmanned platform trajectory tracking hierarchical control strategy adopts a dual closed-loop control structure, in which the outer loop is a kinematic controller composed of the differential error between the actual motion trajectory of the vehicle and the reference trajectory, and the inner loop is a dynamic controller designed by the difference between the expected speed generated by the kinematic controller and the actual speed of the vehicle dynamics. Through the design of a hierarchical coordinated control structure, not only can the complexity of the control system design be reduced, but also the robustness of the control system can be improved, providing theoretical and technical support for the design of motion control systems for narrow-track under-actuated unmanned platforms in complex environments.

[0078] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0079] This article uses specific examples to illustrate the principles and implementation methods of this application. The description of the above embodiments is only used to help understand the method and core ideas of this application. At the same time, for those skilled in the art, according to the ideas of this application, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting this application.

Claims

1. A non-coaxial two-wheeled unmanned platform trajectory tracking hierarchical control method, characterized in that: include: Obtain the real-time position and coordinate information of the unmanned motorcycle during driving; The posture data and coordinate information are input into the on-board computing unit, and based on the dual closed-loop trajectory tracking control strategy, the driving deviation of the non-coaxial two-wheel system is corrected to track the desired trajectory while ensuring the stability of the vehicle; the on-board computing unit is pre-set with an offline desired trajectory, a trajectory tracking controller, a self-balancing controller, a kinematic model of the unmanned motorcycle system and a dynamic model of the unmanned motorcycle system; the dual closed-loop trajectory tracking control strategy includes an outer-loop posture planning layer and an inner-loop balance control layer; the outer-loop posture planning layer is composed of coordinate information, offline desired trajectory, trajectory tracking controller and kinematic model of the unmanned motorcycle system, and is used to output the desired handlebar angle of the unmanned motorcycle; the inner-loop balance control layer is composed of the desired handlebar angle output by the outer-loop posture planning layer, posture information, self-balancing controller and dynamic model of the unmanned motorcycle system, and is used to correct the driving deviation of the non-coaxial two-wheel system; Wherein, the trajectory tracking controller is used to determine the desired forward speed and angular velocity required by the unmanned motorcycle by eliminating the error between the desired state and the actual state; The trajectory generator in the trajectory tracking controller is: ； in, is the expected forward velocity, is the angular velocity, e is the error vector, , , trajectory tracking error , , are the reference forward velocity and yaw angular velocity determined according to the reference trajectory, 、 、 is a positive design parameter, is the state vector, is the yaw angle; The self-balancing controller is used to make the vehicle body roll angle converge to a desired value through a control algorithm after obtaining the posture information, specifically including: Get the i The position and posture information of the unmanned motorcycle system in a sampling period; According to performance indicators , select the weight matrix Q and R ;in, J is the objective function, x is the state vector of the system, u is the control vector, t 0 is the initial moment; For the Riccati equation, i The sampling points of the sampling periods are solved to obtain the feedback gain coefficient of the i-th sampling period; The Riccati equation is solved for each periodic sampling point to obtain a time-varying feedback gain matrix; According to the control amount of the feedback gain matrix, the i The control of the sampling period.

2. The trajectory tracking hierarchical control method of a non-coaxial two-wheeled unmanned platform according to claim 1 is characterized in that: The kinematic model of the unmanned motorcycle system is used to determine the position and yaw angle of the unmanned motorcycle platform at any time in the geodetic coordinate system; The kinematic model of the unmanned motorcycle system is specifically: ； in, For the handlebar angle, is the wheelbase, is the yaw angle, For vehicle speed.

3. The trajectory tracking hierarchical control method of a non-coaxial two-wheeled unmanned platform according to claim 1 is characterized in that: The unmanned motorcycle system dynamics model is a nonlinear dynamics model based on the Chaplekin equation method; the nonlinear dynamics model is specifically: ； In the formula, is the inertia matrix, is the centrifugal force-Coriolis force, is the gravity term, are generalized coordinates, , is the handlebar driving torque, is the front wheel drive torque, matrix element , ( i, j =1,2,3) is the roll angle and handlebar angle The function of represents the driving wheel rotation angle, Indicates the corresponding variable The angular velocity of represents the angular acceleration of the corresponding variable.

4. The trajectory tracking hierarchical control method of a non-coaxial two-wheeled unmanned platform according to claim 1 is characterized in that: Obtain the real-time position information of the unmanned motorcycle during driving, including: The MPU6050 gyroscope chip is used to obtain the roll angle and roll angular velocity of the unmanned motorcycle, the drive motor encoder is used to obtain the forward speed of the unmanned motorcycle, and the handlebar angular velocity is obtained through the handlebar steering motor of the unmanned motorcycle.

5. The trajectory tracking hierarchical control method of a non-coaxial two-wheeled unmanned platform according to claim 1 is characterized in that: Obtain the real-time coordinate information of the unmanned motorcycle during driving, including: UWB technology is used to obtain the real-time coordinates of the unmanned motorcycle.

6. A non-coaxial two-wheeled unmanned platform trajectory tracking hierarchical control system, characterized in that: include: An information acquisition module is used to obtain the real-time position information and coordinate information of the unmanned motorcycle during driving; A control module is used to input posture data and coordinate information into an on-board computing unit, and based on a dual closed-loop trajectory tracking control strategy, correct the driving deviation of the non-coaxial two-wheel system on the premise of ensuring vehicle stability to track a desired trajectory; the on-board computing unit is pre-set with an offline desired trajectory, a trajectory tracking controller, a self-balancing controller, a kinematic model of the unmanned motorcycle system, and a dynamic model of the unmanned motorcycle system; the dual closed-loop trajectory tracking control strategy includes an outer-loop posture planning layer and an inner-loop balance control layer; the outer-loop posture planning layer is composed of coordinate information, offline desired trajectory, trajectory tracking controller, and kinematic model of the unmanned motorcycle system, and is used to output the desired handlebar angle of the unmanned motorcycle; the inner-loop balance control layer is composed of the desired handlebar angle output by the outer-loop posture planning layer, posture information, self-balancing controller, and dynamic model of the unmanned motorcycle system, and is used to correct the driving deviation of the non-coaxial two-wheel system; Wherein, the trajectory tracking controller is used to determine the desired forward speed and angular velocity required by the unmanned motorcycle by eliminating the error between the desired state and the actual state; The trajectory generator in the trajectory tracking controller is: ； in, is the expected forward velocity, is the angular velocity, e is the error vector, , , trajectory tracking error , , are the reference forward velocity and yaw angular velocity determined according to the reference trajectory, 、 、 is a positive design parameter, is the state vector, is the yaw angle; The self-balancing controller is used to make the vehicle body roll angle converge to a desired value through a control algorithm after obtaining the posture information, specifically including: Get the i The position and posture information of the unmanned motorcycle system in a sampling period; According to performance indicators , select the weight matrix Q and R ;in, J is the objective function, x is the state vector of the system, u is the control vector, t 0 is the initial moment; For the Riccati equation, i The sampling points of the sampling periods are solved to obtain the feedback gain coefficient of the i-th sampling period; The Riccati equation is solved for each periodic sampling point to obtain a time-varying feedback gain matrix; According to the control amount of the feedback gain matrix, the i The control of the sampling period.

7. A non-coaxial two-wheeled unmanned platform trajectory tracking hierarchical control system according to claim 6, characterized in that: The information acquisition module includes a posture information acquisition submodule and a coordinate information acquisition submodule.

8. The non-coaxial two-wheeled unmanned platform trajectory tracking hierarchical control system according to claim 7, characterized in that: The posture information acquisition submodule is used to obtain the roll angle and roll angular velocity of the unmanned motorcycle using the MPU6050 gyroscope chip, to obtain the forward velocity of the unmanned motorcycle using the drive motor encoder, and to obtain the handlebar angular velocity through the handlebar steering motor of the unmanned motorcycle; The posture information acquisition submodule is used to obtain the real-time coordinates of the unmanned motorcycle using UWB technology.

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