Unmanned motorcycle control limit active drift control device and method

Abstract

The invention belongs to the technical field of unmanned motorcycle active control, and particularly relates to an unmanned motorcycle control limit active drift control device and method. The device comprises an attitude acquisition module, a balance point output module and a control module, the attitude acquisition module is used for acquiring attitude information of the unmanned motorcycle; the balance point output module stores balance point information of a plurality of unmanned motorcycles under drifting limit working conditions, and is used for outputting balance point information corresponding to the reference path; and the control module is used for generating a control quantity according to the balance point information corresponding to the reference path and the attitude information of the unmanned motorcycle so as to realize the control of the unmanned motorcycle under the drifting limit working condition.

Description

Technical Field

[0001] The present invention belongs to the technical field of active control of unmanned motorcycles, and in particular relates to an active drift control device and method for the maneuvering limit of an unmanned motorcycle. Background Art

[0002] In recent years, the research on the drift motion control of distributed drive vehicles under extreme maneuvering conditions has attracted extensive attention from scholars at home and abroad. Drifting is a common racing skill that usually causes the vehicle to actively enter an oversteering state and put the rear wheels in a completely slipping state. By precisely controlling the accelerator pedal and the front wheel angle, the vehicle can turn at high speed. Active drifting has gradually been introduced into the design of emergency obstacle avoidance and post-collision active safety control strategies for autonomous driving due to its high-speed maneuverability and fast passability. The tire force in the drift state is often in the nonlinear saturation region, coupled with the uncertainty of vehicle structural parameters, which brings great challenges to the design of vehicle active drift control systems. Current methods for drift control mainly include model-based control and learning-based control, both of which can better complete the steady-state drift control of autonomous driving. With the development of vehicle intelligent technology, unmanned motorcycles, with their maneuverability and strong passability, have shown attractive prospects in reconnaissance and detection in mountainous jungles, underground corridors, alleys, and alleys, and cargo transportation, which has aroused great interest and widespread attention from scholars in the field of vehicles and control. Unmanned motorcycles are essentially under-actuated systems with no direct control input in the roll direction, which increases the difficulty of motion control. In addition, due to the requirements of mission maneuverability and mobility, unmanned motorcycles are often required to have active drifting capabilities to cope with complex driving environments and transportation support tasks. From the perspective of public literature, there are few studies on the drifting dynamics and control of unmanned motorcycles, and the relevant dynamic modeling and control technologies are in urgent need of mastering. Summary of the invention

[0003] Based on this, the present invention provides an unmanned motorcycle handling limit active drift control device and method, which can achieve unmanned motorcycle stability control under drift limit conditions.

[0004] The technical solution for implementing the present invention is as follows:

[0005] In a first aspect, the present invention provides an unmanned motorcycle maneuvering limit active drift control device, comprising: a posture acquisition module, a balance point output module and a control module;

[0006] A posture acquisition module is used to collect the posture information of the unmanned motorcycle;

[0007] The balance point output module stores balance point information of multiple unmanned motorcycles under drift limit conditions, and is used to output balance point information corresponding to the reference path;

[0008] The control module is used to generate a control amount to realize the control of the unmanned motorcycle under the drift limit condition according to the balance point information corresponding to the reference path and the posture information of the unmanned motorcycle.

[0009] Optionally, the control module of the present invention uses the front wheel turning angle and the rear wheel speed as control variables, and based on pre-set objective functions and constraints, implements the control of the unmanned motorcycle under the drift limit condition of the unmanned motorcycle.

[0010] Optionally, the objective function and constraint conditions of the control module of the present invention are:

[0011]

[0012] Constraints:

[0013] Δu min ≤Δu(t+i|t)≤Δu max

[0014] u min ≤u(t+i|t)≤u max

[0015] Among them, N p is the prediction step length, N c is the control step size, ε is the relaxation factor, ρ is the weighting coefficient of the relaxation factor ε; δ t+i ,ω t+i , and Indicates the attitude information, namely the front wheel angle, rear wheel speed, yaw rate and body roll angle of the current unmanned motorcycle; δ ref|t+i ,ω ref|t+i , and is the balance point information, i.e., the front wheel turning angle, rear wheel speed, yaw rate and body roll angle of the unmanned motorcycle in the balance state; [Δu min ,Δu max ] is the range of the difference of the control quantity at adjacent moments, [u min ,u max ] is the value range of the control quantity, To square the extended state matrix, To square the control increment matrix.

[0016] Optionally, the balance point information of the multiple unmanned motorcycles under drift limit working conditions described in the present invention is obtained by the following process:

[0017] Based on the balance state simulation of the unmanned motorcycle, multiple balance point information of the unmanned motorcycle under drifting conditions is obtained;

[0018] Different drift conditions and corresponding evaluation indicators are set. Based on the obtained multiple balance point information, it is tested whether each balance point meets the passability and maneuverability requirements of the unmanned motorcycle, and the corresponding balance point information under different reference paths is determined.

[0019] Optionally, the balance state of the unmanned motorcycle of the present invention is:

[0020]

[0021] in, is the vehicle roll angle, m is the vehicle mass, h is the center of gravity height, δ f is the effective turning angle, a and b are the longitudinal distances from the front wheel and rear wheel to the motorcycle’s center of mass, respectively. is the yaw rate, μ is the friction coefficient between the tire and the road, I r is the tire moment of inertia, r is the tire radius, τ is the complementary angle of the tire slip angle, c is the trailing distance, and λ is the front fork inclination angle.

[0022] Optionally, the specific process of obtaining the multiple balance point information in the present invention is:

[0023] Obtain the balance state of the unmanned motorcycle and perform simulation;

[0024] Select the front wheel turning angle, rear wheel speed and body roll angle as state quantities;

[0025] Apply a moment in the roll direction with feedback control, fix two of the three state quantities, and adjust the other state quantity until the external moment value is 0, and record the three state quantities at this time as the balance point information of the unmanned motorcycle;

[0026] According to the coupling relationship between the rear wheel speed and the yaw rate, the yaw rate of the balance point is further determined, and the balance point information finally obtained includes: front wheel angle, rear wheel speed, body roll angle and yaw rate.

[0027] Optionally, when the balance point passes the detection, the present invention sets test condition 1: a steady-state drift condition of straight driving-semicircle drifting-straight driving, to achieve the drift and U-turn of the unmanned motorcycle, and selects a suitable balance point from a series of drift balance points according to the expected trajectory as the basis for subsequent vehicle drift tracking control. The minimum width that the unmanned motorcycle can pass is used as an evaluation index, and its passability is tested compared with the normal steering condition where the unmanned motorcycle turns around with the minimum turning radius.

[0028] Optionally, when the balance point passes the detection, the present invention sets test condition 2: a transient drift condition of passing an S-curve, and the curvature radii of the two arcs of the S-curve are arbitrarily selected within an optional range. From a series of drift balance points, a suitable balance point is selected according to the expected trajectory as the basis for subsequent vehicle drift tracking control. The time it takes for the motorcycle to pass the path is used as an evaluation indicator, and its maneuverability is tested compared with the normal steering and cornering condition of the motorcycle.

[0029] In a second aspect, the present invention provides an active drift control method for an unmanned motorcycle maneuvering limit, the specific process of which is as follows:

[0030] Collect the posture information of the unmanned motorcycle;

[0031] Outputting the balance point information corresponding to the reference path from the pre-stored balance point information of multiple unmanned motorcycles under drift limit working conditions;

[0032] According to the balance point information corresponding to the reference path and the posture information of the unmanned motorcycle, a control amount is generated to realize the control of the unmanned motorcycle under the drift limit working condition.

[0033] Beneficial effects:

[0034] The present invention adopts model predictive control, which can consider multiple control objectives at the same time, optimize the overall performance, and ensure that the control action does not exceed the allowable range, thereby improving the reliability and stability of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, 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 invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0036] Figure 1 This is a schematic diagram of the control process of the control device of the present invention;

[0037] Figure 2 A flow chart of obtaining the balance point information of the present invention;

[0038] Figure 3 This is a schematic diagram of the hardware connection of the actual vehicle platform. DETAILED DESCRIPTION

[0039] The embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0040] It should be noted that the following embodiments and features in the embodiments may be combined with each other in the absence of conflict; and, based on the embodiments in the present disclosure, all other embodiments obtained by ordinary technicians in the field without making any creative work are within the scope of protection of the present disclosure.

[0041] It should be noted that various aspects of the embodiments within the scope of the appended claims are described below. It should be apparent that the aspects described herein may be embodied in a wide variety of forms, and any specific structure and / or function described herein is merely illustrative. Based on the present disclosure, it should be understood by those skilled in the art that an aspect described herein may be implemented independently of any other aspect, and two or more of these aspects may be combined in various ways. For example, any number of aspects described herein may be used to implement the device and / or practice the method. In addition, other structures and / or functionalities other than one or more of the aspects described herein may be used to implement this device and / or practice this method.

[0042] The present application embodiment provides an unmanned motorcycle maneuvering limit active drift control device, such as Figure 1 As shown, it includes: a posture acquisition module, a balance point output module and a control module;

[0043] A posture acquisition module is used to collect the posture information of the unmanned motorcycle;

[0044] The balance point output module stores balance point information of multiple unmanned motorcycles under drift limit conditions, and is used to output balance point information corresponding to the reference path;

[0045] The control module is used to generate a control amount to realize the control of the unmanned motorcycle under the drift limit condition according to the balance point information corresponding to the reference path and the posture information of the unmanned motorcycle.

[0046] Furthermore, the process of acquiring the balance point information of multiple unmanned motorcycles under drift limit working conditions stored in the balance point output module in the control device is specifically as follows:

[0047] Based on the balance state simulation of the unmanned motorcycle, multiple balance point information of the unmanned motorcycle under drifting conditions is obtained;

[0048] Different drift conditions and corresponding evaluation indicators are set. Based on the obtained multiple balance point information, it is tested whether each balance point meets the passability and maneuverability requirements of the unmanned motorcycle, and the corresponding balance point information under different reference paths is determined.

[0049] Furthermore, the specific process of obtaining multiple balance points in this embodiment is as follows:

[0050] Obtain the balance state of the unmanned motorcycle and perform simulation;

[0051] Select the front wheel turning angle, rear wheel speed and body roll angle as state quantities;

[0052] Apply a roll torque with feedback control, fix two of the three state quantities, and adjust the other state quantity until the external torque value is 0, and record the three state quantities at this time as the balance point of the unmanned motorcycle;

[0053] After determining the state quantities of the balance point under drift conditions, namely, the front wheel turning angle, the rear wheel speed, and the body roll angle, the yaw angular velocity is further determined because the rear wheel speed and the yaw angular velocity have the following coupling relationship.

[0054]

[0055] In this embodiment, simulation is performed without the control module, and by additionally applying a moment in the roll direction with feedback control and controlling two state quantities, the drift balance point information of the entire vehicle system is accurately solved using the three-point search method.

[0056] For example, since a motorcycle does not need to apply additional torque to achieve a drifting steady state, based on this fixed front wheel angle and rear wheel speed, the counterclockwise direction of the rear view is positive, and according to the positive and negative of the additional torque, since the relationship between the roll angle and the additional torque is monotonic, the three-point search is suitable for more accurately finding the independent variable whose function value is equal to the target value when it is known to be a monotonic function (monotonically increasing or monotonically decreasing). Use the three-point search method to adjust the size of the roll angle until the value of the external torque is 0, and the corresponding body side slip angle is the desired balance value. The corresponding process is as follows Figure 2 As shown. You can also choose any other two state quantities as fixed values ​​and solve the remaining state quantities.

[0057] Furthermore, in another embodiment of the present application, the balance state of the unmanned motorcycle is:

[0058]

[0059] in, is the vehicle roll angle, m is the vehicle mass, h is the center of gravity height, δ f is the effective turning angle, a and b are the longitudinal distances from the front wheel and rear wheel to the motorcycle’s center of mass, respectively. is the yaw rate, μ is the friction coefficient between the tire and the road, I r is the moment of inertia of the tire, r is the radius of the tire, τ is the complementary angle of the tire slip angle, c is the trailing distance, and λ is the inclination angle of the front fork;

[0060] The balance state of the unmanned motorcycle in this embodiment is derived through the following process:

[0061] (1) Establish a five-degree-of-freedom monorail vehicle model.

[0062] Considering the complexity of controller design and the requirement of real-time solution, the vehicle model adopts a five-degree-of-freedom single-track vehicle model, and the wheel speed dynamics are not considered here. Based on the Lagrangian function, the following dynamic model is established:

[0063]

[0064] In the above formula, m is the mass of the vehicle, υ G is the velocity of the center of gravity, J is the moment of inertia of the front steering mechanism, is the steering angular velocity of the front steering mechanism, h is the center of gravity height, is the vehicle roll angle, Δ hG It is the height at which the center of gravity changes.

[0065] By deduction, we know

[0066]

[0067] Front wheel turning angle δ and effective turning angle δ f The relationship is as follows:

[0068]

[0069] Substituting the above formula into the Lagrangian function, we can get:

[0070]

[0071] Among them, l is the lateral velocity at the point where the rear wheel contacts the ground, f The radial velocity at the point where the rear wheel contacts the ground, is the vehicle roll angular velocity, a is the distance from the center of gravity to the rear wheel axle, b is the distance from the center of gravity to the front wheel axle, is the yaw rate, c is the trailing distance, λ is the front fork inclination angle, δ is the steering angle of the front steering mechanism, δ f is the effective steering angle.

[0072] (2) Establish tire dynamics model.

[0073] Accurate characterization of tire mechanical properties is crucial for successful motorcycle drifting operations. The accurate description of tire mechanical properties is inseparable from the correct selection of tire models. In this solution, both the front and rear tires are modeled using the improved side slip-longitudinal slip composite Fiala tire model. The tire model formula is as follows:

[0074]

[0075] z=tan(α)

[0076]

[0077] In the above formula, C α is the tire cornering stiffness, α is the tire slip angle, F z is the tire vertical load, μ is the friction coefficient between the tire and the road, ξ is the attenuation factor, F x is the longitudinal tire force. If the vehicle is driven only by the rear wheels, for the front tires, obviously ξ=1, and the front wheel model is the pure side slip Fia la tire model.

[0078] (3) Balance state of the unmanned motorcycle

[0079] First, estimate the vertical force of the front and rear tires. The calculation formula is as follows:

[0080]

[0081] Where mg is the overall gravity of the vehicle body, a and b are the longitudinal distances from the front wheel and rear wheel to the center of mass of the motorcycle, respectively.

[0082] Also because

[0083] Fyr=-ξμF ZR sgn(α)

[0084]

[0085] The turning radius R satisfies the following equation:

[0086]

[0087] Known rf ,υ l and f There is the following relationship between

[0088]

[0089] The complementary angle τ of the tire side slip angle can be obtained to satisfy the following formula:

[0090]

[0091] The rear wheel speed ω satisfies the following formula:

[0092]

[0093] From the front wheel steering geometry, the steering angle δ satisfies:

[0094]

[0095] Because in equilibrium is equal to 0, and the Lagrangian function L is rewritten as:

[0096]

[0097] The Lagrange equation can be expressed as:

[0098]

[0099] Combining the above formulas, we can get the following formula that the unmanned motorcycle needs to satisfy when it is in equilibrium:

[0100]

[0101] This embodiment first establishes a single-track two-wheel vehicle dynamics model suitable for controller design; secondly, a tire dynamics model is established. When establishing the tire dynamics model, both the front and rear tires are modeled using the improved lateral slip-longitudinal slip composite Fiala tire model. On the basis of the original tire model, the coupling relationship between the longitudinal force and the lateral force at the friction limit is considered, taking into account the practicability and accuracy of the model while reducing the complexity of the model; finally, based on the improved lateral slip-longitudinal slip composite Fiala tire model used in both the front and rear tires, it is deduced that the unmanned motorcycle is in equilibrium.

[0102] Furthermore, this embodiment sets different drift conditions and corresponding evaluation indicators, and for a series of balance points obtained, checks whether each balance point meets the passability and maneuverability requirements of the unmanned motorcycle, and determines the balance points corresponding to different reference paths.

[0103] Test condition 1 is set: a steady-state drift condition of straight driving-semicircle drifting-straight driving, to achieve drifting and turning of the unmanned motorcycle. From the information of multiple drift balance points, a suitable balance point is selected according to the expected trajectory as the basis for subsequent vehicle drift tracking control. The minimum width that the motorcycle can pass is used as the evaluation index. Compared with the normal turning condition where the motorcycle turns around with the minimum turning radius, its passability is tested to see whether it is improved.

[0104] Test condition 2 is set as the transient drift condition of passing an S-curve. The curvature radius of the two arcs of the S-curve can be selected arbitrarily within the optional range. From the information of multiple balance points, a suitable balance point is selected according to the expected trajectory as the basis for subsequent vehicle drift tracking control. The time it takes for the motorcycle to pass the path is used as the evaluation indicator. Compared with the normal steering and cornering condition of the motorcycle, its maneuverability is tested to see whether it is improved.

[0105] Furthermore, in this embodiment, the control module is based on the MPC algorithm, with the front wheel angle and rear wheel speed as the control quantity, and the body roll angle and yaw rate as the state quantity, to achieve the control of the unmanned motorcycle under the drift limit condition of the unmanned motorcycle. The drift balance point is combined with the basic principle of the MPC algorithm, and different test conditions and evaluation indicators are set. The specific objective function is: The MPC algorithm objective function and constraints are as follows:

[0106]

[0107]

[0108] Constraints:

[0109] Δu min ≤Δu(t+i|t)≤Δu max

[0110] u min ≤u(t+i|t)≤u max

[0111] In the formula, the first term represents the deviation between the motorcycle state and the control value, that is, the difference between the front wheel angle, rear wheel speed, body roll angle, yaw rate and the corresponding balance point state under drift conditions. Q is the weight matrix output by the control module, which reflects the system's requirements for control effect.

[0112] In the formula, the second term represents the steady input of the control quantity, R is the control increment weight matrix of the control module, which reflects the system's requirements for the control quantity constraint. ρ is the weight coefficient of the relaxation factor ε; the role of the relaxation factor ε is to enable a solution to be found when solving the quadratic programming.

[0113] In summary, the objective function comprehensively considers the error between the actual output of the control system and the expected reference value and the restrictions of the execution system on the control increment.

[0114] Basic principles of MPC algorithm: There are three key steps in the implementation of model predictive control technology: prediction model, rolling optimization and feedback correction. The prediction part predicts the state quantity at the future time based on the prediction model and the current state of the system and the control quantity at the future time. Rolling optimization is the embodiment of the core of the MPC algorithm, which is based on solving optimization problems. In the new control cycle, the feedback correction link corrects the model's prediction quantity in the prediction time domain according to the actual state quantity and output of the controlled object.

[0115] Furthermore, in the embodiment of the present application, the steady-state condition is defined as follows through the stable domain analysis of motorcycle driving:

[0116]

[0117] All steady-state driving states of the motorcycle meet the above conditions. The steady-state manifold is defined as the set of all steady states of the system in a variable domain. Its expression can be obtained by combining the above formula with the Lagrangian second-order dynamic equation:

[0118]

[0119] If the motorcycle's state variables satisfy the above expressions, it is on a steady-state manifold, and the expressions between the state variables can be resolved into equations to determine the critical conditions.

[0120] This embodiment uses the concept of steady-state manifold to obtain the variable region where the steady-state state points of the motorcycle are gathered, and can intuitively analyze the boundaries of various variables that keep the motorcycle running stably. Keeping the motorcycle's state close to the steady-state boundary can better control the balance of the motorcycle.

[0121] The present application embodiment provides an active drift control method for an unmanned motorcycle maneuvering limit, and the specific process is as follows:

[0122] Collect the posture information of the unmanned motorcycle;

[0123] Outputting the balance point information corresponding to the reference path from the pre-stored balance point information of multiple unmanned motorcycles under drift limit working conditions;

[0124] According to the balance point information corresponding to the reference path and the posture information of the unmanned motorcycle, a control amount is generated to realize the control of the unmanned motorcycle under the drift limit working condition.

[0125] Specific test process: In this embodiment, a vehicle test is carried out based on a real vehicle platform to verify the effectiveness of the device. The hardware connection of the real vehicle platform is as follows Figure 3 As shown:

[0126] Initialization: Start the motorcycle, the system self-checks and initializes all sensors, controllers and actuators.

[0127] As the host computer, Raspberry Pi 4B has high computing power and is suitable for running complex algorithms and operating systems. It is responsible for tasks with large computing volume. In this experiment, it is mainly used to receive data from various sensors and perform real-time MPC algorithm calculations based on the data, and send the control results to STM32.

[0128] Although the STM32 microcontroller has relatively weak computing power, it excels in real-time control and low power consumption, so it is used to control the actuator. It can generally only withstand lower voltages and cannot directly drive high-voltage motors, so a motor driver module is needed to isolate and control the high voltage of the motor.

[0129] The motor drive module A4950 can accept the low-voltage control signal of STM32 and convert it into a signal suitable for driving a high-voltage motor, thereby realizing the system's control over the motor.

[0130] This framework isolates the control task from the computing task, realizes the specialization of each CPU, improves the stability of the system, and facilitates the expansion or maintenance of later tasks. After the construction and verification of the above hardware, this solution can achieve the stability control of the unmanned motorcycle under the drift limit condition.

[0131] This application has the following effects:

[0132] First, when the unmanned motorcycle reaches a balanced state, an improved lateral slip-longitudinal slip composite Fia la tire model is used for modeling. The lateral force generated by the tire can be accurately calculated according to parameters such as the tire's lateral slip angle and vertical load, truly reflecting the steering characteristics of the vehicle.

[0133] Second, the balance point information adopts a method based on simulation to obtain the drift balance point, and uses the three-point search method to adjust the size of the roll angle until a stable drift balance point is found, which solves the problem that the balance point obtained by the analytical method may be inaccurate.

[0134] Thirdly, the steady-state manifold theory is further used to obtain the stable driving area of ​​the motorcycle, and the variable boundary that keeps the motorcycle running stably is analyzed, providing a reference for better controlling the balance of the motorcycle.

[0135] Fourth, the use of model predictive control (MPC) can consider multiple control objectives at the same time to optimize the overall performance, and can also ensure that the control action does not exceed the allowable range, thereby improving the reliability and stability of the system.

[0136] In summary, the above are only preferred embodiments of the present invention and are not intended to limit the protection scope of the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. An active drift control device for unmanned motorcycle maneuvering limit, characterized in that: include: Posture acquisition module, balance point output module and control module; A posture acquisition module is used to collect the posture information of the unmanned motorcycle; The balance point output module stores balance point information of multiple unmanned motorcycles under drift limit conditions, and is used to output balance point information corresponding to the reference path; The control module is used to generate a control amount to realize the control of the unmanned motorcycle under the drift limit condition according to the balance point information corresponding to the reference path and the posture information of the unmanned motorcycle.

2. The unmanned motorcycle maneuvering limit active drift control device according to claim 1, characterized in that: The control module uses the front wheel turning angle and the rear wheel speed as control variables and implements the control of the unmanned motorcycle under the drift limit working condition of the unmanned motorcycle based on a preset objective function.

3. The unmanned motorcycle maneuvering limit active drift control device according to claim 2, characterized in that: The objective function of the control module is: Among them, N p is the prediction step length, N c is the control step size, ε is the relaxation factor, ρ is the weighting coefficient of the relaxation factor ε; δ t+i ,ω t+i , and Indicates the attitude information, namely the front wheel angle, rear wheel speed, yaw rate and body roll angle of the current unmanned motorcycle; δ ref|t+i ,ω ref|t+i , and is the balance point information, namely the front wheel turning angle, rear wheel speed, yaw rate and body roll angle of the unmanned motorcycle in the balance state. To square the extended state matrix, To square the control increment matrix.

4. The unmanned motorcycle maneuvering limit active drift control device according to claim 2, characterized in that: The constraints of the control module are: Δu min ≤Δu(t+i|t)≤Δu max u min ≤u(t+i|t)≤u max Among them, [Δu min ,Δu max ] is the range of the difference of the control quantity at adjacent moments, [u min ,u max ] is the value range of the control quantity.

5. The unmanned motorcycle maneuvering limit active drift control device according to claim 1, characterized in that: The balance point information of the plurality of unmanned motorcycles under drift limit working conditions is obtained by the following process: Based on the balance state simulation of the unmanned motorcycle, multiple balance point information of the unmanned motorcycle under drifting conditions is obtained; Different drift conditions and corresponding evaluation indicators are set. Based on the obtained multiple balance point information, it is tested whether each balance point meets the passability and maneuverability requirements of the unmanned motorcycle, and the corresponding balance point information under different reference paths is determined.

6. The unmanned motorcycle maneuvering limit active drift control device according to claim 1 or 5, characterized in that: The balance state of the unmanned motorcycle is: in, is the vehicle roll angle, m is the vehicle mass, h is the center of gravity height, δ f is the effective turning angle, a and b are the longitudinal distances from the front wheel and rear wheel to the motorcycle’s center of mass, respectively. is the yaw rate, μ is the friction coefficient between the tire and the road, I r is the tire moment of inertia, r is the tire radius, τ is the complementary angle of the tire slip angle, c is the trailing distance, and λ is the front fork inclination angle.

7. The unmanned motorcycle maneuvering limit active drift control device according to claim 6, characterized in that: The specific process of obtaining the information of the multiple balance points is as follows: Obtain the balance state of the unmanned motorcycle and perform simulation; Select the front wheel turning angle, rear wheel speed and body roll angle as state quantities; Apply a moment in the roll direction with feedback control, fix two of the three state quantities, and adjust the other state quantity until the external moment value is 0, and record the three state quantities at this time as the balance point information of the unmanned motorcycle; According to the coupling relationship between the rear wheel speed and the yaw rate, the yaw rate of the balance point is further determined, and the balance point information finally obtained includes: front wheel angle, rear wheel speed, body roll angle and yaw rate.

8. The unmanned motorcycle maneuvering limit active drift control device according to claim 4, characterized in that: When the balance point passes the test, set test condition 1: a steady-state drift condition of straight driving-semicircle drifting-straight driving to achieve the drift and U-turn of the unmanned motorcycle. From the information of multiple balance points, select a suitable balance point according to the expected trajectory as the basis for subsequent vehicle drift tracking control. The minimum width that the unmanned motorcycle can pass is used as the evaluation index, and its passability is tested compared with the normal turning condition of the unmanned motorcycle turning around with the minimum turning radius.

9. The unmanned motorcycle maneuvering limit active drift control device according to claim 4, characterized in that: When the balance point passes the detection, in the balance point detection module, set test condition 2: transient drift condition of passing the S-bend, the curvature radius of the two arcs of the S-bend can be selected arbitrarily within the optional range, and select a suitable balance point from multiple balance point information according to the expected trajectory as the basis for subsequent vehicle drift tracking control. The time it takes for the motorcycle to pass the path is used as an evaluation indicator, and its maneuverability is tested compared with the normal steering and cornering condition of the motorcycle.

10. An active drift control method for unmanned motorcycle handling limit, implemented based on any one of the control devices in claims 1-9, the specific process is: Collect the posture information of the unmanned motorcycle; Outputting the balance point information corresponding to the reference path from the pre-stored balance point information of multiple unmanned motorcycles under drift limit working conditions; According to the balance point information corresponding to the reference path and the posture information of the unmanned motorcycle, a control amount is generated to realize the control of the unmanned motorcycle under the drift limit working condition.

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