51 results about "Backstepping controller" patented technology

The invention discloses an interference compensation control method for a heave compensation winch. The method comprises the following steps: based on a state space model of a heave compensation winch system comprising a heave compensation winch and a pump station, establishing a backstepping controller through a backstepping control method; establishing a parameter online estimation module of the hydraulic motor of the heave compensation winch, and inputting and processing the initial identification parameters of the hydraulic motor and the dynamic regression vector of the hydraulic cylinder to obtain the identification parameters of the hydraulic motor updated in real time; and inputting the identification parameters and the operation parameters of the heave compensation winch into a backstepping controller, outputting a valve core displacement control instruction of a proportional servo valve of a pump station after processing, and further realizing interference compensation control of the heave compensation winch by controlling the pump station. By means of the method, high-precision tracking and robust stability control over the heave compensation winch system can be still kept under the conditions that the model is uncertain and disturbance is strong, and support is provided for the underwater high-precision operation requirement.

The invention provides an orchard tracked vehicle pure tracking control method and system, and the method comprises the steps: constructing a geometry-vector dynamic coupling model based on path geometric features and vector field analysis, quantifying local steering characteristics through a direction change sensing mechanism, introducing a curvature factor to represent path accumulated deformation, inhibiting noise interference in combination with a direction consistency correction term, improving curvature estimation of a three-point arc method, and generating multi-scale curvature characteristic data; based on a tracked vehicle kinematics model, analyzing a coupling relationship between a transverse tracking error and a course tracking error, and generating coupling error state data; and inputting the multi-scale curvature characteristic data and the coupling error state data into a backstepping controller, analyzing and exporting a dynamic look-ahead distance explicit expression through a simultaneous kinematics equation and a backstepping control equation, generating a self-adaptive angular velocity control instruction, and outputting the self-adaptive angular velocity control instruction to an execution mechanism to drive a vehicle to steer. And adaptive optimization of the look-ahead distance along with the path curvature, the vehicle state and the tracking error is realized.

The application relates to a kind of linear vernier permanent magnet motor deadbeat control methods, on the basis of traditional DTC control, by introducing speed backstepping controller to replace traditional speed loop PI regulator, using deadbeat controller to replace current loop PI regulator, using improved double-sampling double-update SVPWM to replace traditional SVPWM modulation module.According to multiple simulation, the application can effectively reduce the overshoot of the system and effectively reduce the thrust ripple of the system on the basis of maintaining traditional DTC control, and improve the dynamic performance of the system.

The application discloses a construction method of a flat wire permanent magnet wheel hub motor backstepping model predictive controller, first derives an angular velocity deviation, constructs a corresponding Lyapunov function one, calculates q-axis current, obtains a q-axis current estimation value according to a calculation formula of the q-axis current and a torque estimation error, and constructs a backstepping controller with the angular velocity deviation input and the q-axis current estimation value output; then constructs a corresponding Lyapunov function two according to the angular velocity deviation and a current tracking error, solves acceleration, constructs an acceleration control module with the angular velocity deviation and the current tracking error input and the acceleration output, and finally evaluates each d-axis current prediction value, q-axis current prediction value and acceleration prediction value through a value function formula, selects a voltage vector corresponding to a prediction value that makes the value function minimum; the method realizes rapid and accurate acquisition of motor given current under complex working conditions, improves the response capability of a hub driving system, and is favorable to improvement of collaborative control performance of a distributed driving system.

The invention discloses a permanent magnet synchronous motor sliding mode integrated control method based on a disturbance observer, and the method comprises the steps: constructing a hyperlocal mathematical model of a speed ring of a permanent magnet synchronous motor, combining a state equation with a first-order nonlinear model, and reducing the dependence on a precise system model; designing a model-free adaptive fast integration terminal sliding mode controller; a current loop backstepping controller is designed, electromagnetic parameter disturbance is considered, d-axis and q-axis current control laws are recursively constructed, and the anti-interference capability of a current loop is enhanced; and designing an extended super-spiral disturbance observer, estimating total unknown disturbance of the system and performing feed-forward compensation, thereby improving the control precision. The speed tracking precision of the system is improved, and the steady-state error is reduced.

This invention discloses a multivariable, highly coupled hydrogen-oxygen dual-loop coordinated control method for hydrogen fuel cells, comprising the following steps: S1, establishing a nonlinear high-order system model with flow rate and pressure coupling based on the cathode and anode gas dynamic equations of the fuel cell system; S2, designing a multivariable coordinated backstepping controller with the excess oxygen ratio, excess hydrogen ratio, and anode-cathode pressure difference as control objectives; S3, considering the influence of load changes and model uncertainties on the hydrogen-oxygen dual loop, introducing an adaptive control method and designing control commands for the air compressor, circulating pump, and solenoid valve; S4, selecting a suitable Lyapunov function to prove the stability of the system and completing the adaptive backstepping control of the anode-cathode pressure difference and gas flow rate; This invention solves the problem of multivariable, highly coupled coordinated control of the hydrogen-oxygen dual loop in fuel cells, and can maintain the transient and steady-state performance of the system under sudden load changes, improving the safety and reliability of system operation.

The invention discloses an intelligent automobile transverse control method based on a radial basis function neural network. The method comprises the following steps that a vehicle-road coupling dynamics tracking model considering steering characteristics is established; constructing a fixed time preset performance function to express the tracking error; establishing a self-adaptive backstepping controller model based on a radial basis function neural network; determining steering wheel output torque; and determining a final front wheel steering angle control instruction. According to the method, the radial basis function neural network and the adaptive disturbance estimation strategy are combined, the system uncertainty disturbance existing in the intelligent vehicle path tracking process is considered, the path tracking precision can be remarkably improved, the robustness of intelligent vehicle control is ensured in the complex road environment, and especially under the conditions of different vehicle speeds and roads, the intelligent vehicle path tracking precision is improved. The path tracking error of the vehicle can be always kept within the preset precision range. According to the invention, rapid convergence of the path tracking error in finite time can be ensured, so that the stability and reliability of the system are remarkably improved.

The invention belongs to the related technical field of hydraulic servo control systems, and discloses a humanoid robot hydraulic driver position servo self-adaptive control method and system oriented to variable load working conditions. The method comprises the following steps: establishing a mathematical model of a to-be-controlled object hydraulic driver and converting the mathematical model into a state-space equation; according to the state-space equation of the to-be-controlled object, establishing an extended state observer to observe the state and total disturbance of the to-be-controlled object in real time; according to the state and the total disturbance observed by the expansion state observer in real time, a backstepping controller is designed to control the to-be-controlled object, so that the actual displacement of the to-be-controlled object is close to the expected displacement, when the backstepping controller is designed, an adaptive law is established for the load quality in the backstepping controller, and the actual displacement of the to-be-controlled object is controlled. Therefore, the dynamic change of the load in practical application is met. According to the invention, the problems of low tracking precision and weak robustness of hydraulic driver position control under a variable load working condition are solved.

This invention discloses a multi-strategy traffic following control system based on an onboard edge-cloud collaborative architecture, comprising: an onboard control unit, a roadside communication identification unit, and a cloud-based control management platform. The roadside communication identification unit is deployed at key road nodes to identify road types and communication status in real time, and issues control strategy switching commands based on these real-time identifications. The onboard control unit integrates an adaptive sliding mode controller and a nonlinear self-stabilizing backstepping controller, used for following control on straight and curved road sections, respectively. The cloud-based control management platform periodically updates controller parameters using historical data and pushes this information to the onboard control unit and the roadside communication identification unit. This invention achieves automatic switching of control strategies based on the road structure type where the vehicle is located, employing a hybrid weighted fusion control structure to ensure a smooth transition between different controller signals during switching, thereby improving the control accuracy and stability of mixed vehicle fleets in complex traffic environments.

A finite-time anti-interference control method for a universal pneumatic flexible manipulator includes the following steps: The universal pneumatic flexible manipulator is simplified to a link model using the D-H method, and kinematic analysis is performed to obtain the kinematic analysis model and the position of the end point of the universal pneumatic flexible manipulator; based on the kinematic analysis model of the universal pneumatic flexible manipulator, a dynamic model of the universal pneumatic flexible manipulator is established using the Euler-Lagrange method; the uncertainty of the universal pneumatic flexible manipulator is considered as a disturbance, and a second-order mathematical model of the pneumatic flexible manipulator is established; a finite-time extended state observer is designed to estimate the disturbance, and based on the disturbance estimate, a finite-time backstepping controller is designed to compensate for the influence of the disturbance on the system; to address the model uncertainty, a finite-time control method is designed to compensate for the influence on the position control accuracy of the pneumatic flexible manipulator, exhibiting strong robustness and high control accuracy.

The invention relates to an electric steering engine fine anti-interference control method under the constraint of a rudder deflection angle, and belongs to the technical field of electric steering engine control. Firstly, an electric steering engine mathematical model considering unknown frequency multi-source interference is established; secondly, aiming at multi-source interference generated by cogging torque, magnetic flux harmonic waves, PWM dead zone torque and the like, constructing an unknown frequency interference observer to estimate interference; and finally, designing a backstepping controller based on a obstacle Lyapunov function, and completing the design of the fine anti-interference control method of the electric steering engine under the constraint of the rudder deflection angle by combining the interference and the estimated value of the first derivative of the interference. The method realizes fine anti-interference control of the electric steering engine, has the characteristics of high control precision and good reliability, and is suitable for the control problem of the electric steering engine under multi-source interference and constraint.

The invention discloses a DNN-based adaptive optimization control method for a fractional order single-machine infinite bus system, and relates to the field of single-machine infinite bus system control. The method comprises the following steps: establishing a dynamical model of a single-machine infinite bus system, and converting the dynamical model of the system into a state model; designing a function approximate DNN architecture, approaching an unknown function in the system, and designing a weight updating law based on a first-order Taylor series to reduce the mathematical difficulty; in the backstepping process, a virtual controller and an actual controller are constructed by utilizing an optimization backstepping technology, and the overall control optimization of the system is realized; an event trigger function is designed, and consumption of system communication resources is reduced; and carrying out Lyapunov analysis to ensure that each signal of the system is bounded. The adaptive optimization backstepping controller based on the DNN architecture is designed for a fractional order single-machine infinite bus system, unknown nonlinear terms in the system can be compensated, and a single-machine power angle tracks a given reference signal.

The present application relates to the field of flexible actuator driven rehabilitation robot control, and discloses a rehabilitation robot control method based on interference compensation and finite time instruction filtering, which comprises a finite time instruction filter, a finite time filtering error compensator, a first finite time disturbance observer, a second finite time disturbance observer and a composite anti-interference finite time backstepping controller. Compared with the traditional backstepping control method, the present application solves the problem of 'derivative explosion' in the traditional backstepping control method and the problem of filtering error reducing system control performance in the dynamic surface control method. In addition, the control method of the present application uses a finite time disturbance observer to estimate system disturbance, and can be applied to various types of interference suppression situations through disturbance compensation, thereby improving the anti-interference ability of the system.

The application discloses a kind of active suspension nonlinear mapping constraint control methods based on fixed time, comprising the following steps: step 1: constructing nonlinear mapping function: by nonlinear mapping function, asymmetric time-varying constraint to system state variable is realized by coordinate conversion;The application is by constructing nonlinear mapping function to carry out coordinate conversion, and the constraint problem of original system state is converted into the bounded problem of new system state, while realizing asymmetric time-varying state constraint, avoid the limitation of feasibility condition to control design.On this basis, combined with fixed time stability theory to construct backstepping controller, so that closed-loop system realizes fixed time stability under time-varying asymmetric constraint condition, and its convergence time avoids dependence on initial state, so as to improve the rapidity, robustness and safety of active suspension system.

In order to improve the stability and anti-interference performance of a single-inverter double-parallel motor driving system, the invention provides a self-adaptive speed control method for a single-inverter double-parallel permanent magnet synchronous motor. Firstly, an equivalent mathematical model of a system is established through non-singular coordinate transformation, and the coupling relation between inverter output voltage and a system control target is disclosed. On the basis, the design idea of a backstepping controller is combined, and a basic control framework of the double parallel motor driving system is constructed. Based on a deterministic equivalence principle, a self-adaptive average speed controller is designed, and the robustness of the system to parameter mismatch and load disturbance is remarkably enhanced. A self-adaptive differential speed controller is designed in combination with a gradient search method, and the risk of instability caused by system singular points is effectively avoided through online gradient correction of controller gain.

This invention discloses a backstepping-based variable iterative learning control method for robotic arms, applicable to motor-driven robotic arm systems. This method converts the robotic arm system model into a third-order strict feedback form. By defining coordinate transformation error and introducing a command filter, an auxiliary system is constructed to compensate for the deviation between the filter and the virtual control law. A radial basis function neural network is used to approximate the unknown dynamics and disturbances of the system online. A backstepping controller is designed based on the compensation error, and a parameter learning law with variable iteration length is constructed to ensure system stability and convergence of the compensation error. By dynamically adjusting the iteration length during the control phase, this method can reduce computational resource consumption while maintaining high-precision tracking performance. When used for robotic arm control of repetitive tasks, it significantly improves the tracking accuracy and robustness of the system, demonstrating good engineering application value.

This application discloses a fuzzy optimization formation control method and system for multiple unmanned systems, belonging to the field of unmanned control technology. The method includes: after confirming the positions of the target system and neighboring systems, calculating the position errors of the target system and neighboring systems; inputting the position data and position errors of the target system into a fuzzy identifier to obtain dynamic data; designing an adaptive backstepping controller based on the target system's position data, position errors, and dynamic data to obtain the adaptive backstepping control input; obtaining the optimal control input based on the new error dynamic system, and applying smooth saturation constraints to the adaptive backstepping control input and the optimal control input to obtain the control signal. This application utilizes the universal approximation characteristic of fuzzy logic systems to establish a fuzzy identifier that approximates the dynamic data of the target system and a formation optimization controller with a single evaluation structure, solving the technical problem of control performance degradation caused by input saturation in unmanned formations.

The application discloses a hovercraft filtering backstepping trajectory tracking control method based on RLESO, and first establishes a three-degree-of-freedom mathematical model of hovercraft movement, compares actual trajectory information of the hovercraft with reference trajectory information to obtain a position error dynamics model of the hovercraft; designs RLESO estimation and compensation of unknown environmental disturbance of the hovercraft based on state information of the mathematical model of the hovercraft; combines the position error dynamics model to construct a time-varying BLF with a position error constraint function; then designs a position error constraint-based hovercraft command filter backstepping controller according to the BLF, backstepping technology and a second-order command filter, and completes a trajectory tracking control target of the hovercraft. The hovercraft filtering backstepping trajectory tracking control method based on RLESO can improve safety performance and controllability of the hovercraft, can make the hovercraft obtain better performance under the influence of external marine environment, and can improve tracking precision of the hovercraft.

The application belongs to the technical field of flight control. The application provides a predetermined time robust control method for an aircraft attitude angle under unknown disturbance. The method comprises: establishing an attitude angle and angular rate dynamics equation of the aircraft according to wind tunnel experiment data; considering the uncertainty and unknown disturbance existing in the dynamics equation, modeling the uncertainty and unknown disturbance into a form of comprehensive disturbance, and converting the dynamics equation into an affine linearization form containing the disturbance; designing a predetermined time disturbance estimator to realize accurate estimation of the comprehensive disturbance, designing an attitude angle predetermined time backstepping controller, and compensating the disturbance in the controller to realize predetermined time control of the aircraft attitude angle. The method of the disclosed embodiment has the advantages of simple structure and strong robustness, simplifies the parameter adjustment requirement for the convergence time, and can realize predetermined time control of the aircraft attitude angle under unknown disturbance.

The present application relates to a kind of cone-shaped dielectric elastomer actuator (Dielectric Elastomer Actuator, DEA) implicit inverse control method for input square state constraint, aims at effectively reducing the influence of hysteresis on system control performance, and solve the input square full state constraint problem in DEA control, including the following steps: based on traditional Preisach model, input square Preisach model is proposed, the hysteresis modeling when input is voltage square term in DEA system is realized.Combined with Fuzzy Logic System (FLS) and Barrier Lyapunov Function (BLF), a new adaptive fuzzy segmented implicit inverse controller is proposed for DEA system with input square state constraint.Based on the control platform built, compared with the controller without implicit inverse and the traditional Backstepping controller, the accuracy and effectiveness of the designed control strategy are verified, the proposed control algorithm has higher precision, and the high-precision control goal of dielectric elastomer actuator is realized.

The present invention discloses a method and control system for reducing the sway of a suspended UAV in flight. The method uses an observer to observe the suspended UAV flight system using a control signal U and an observer, and adds an estimated value obtained by the observation when generating a new control signal to offset the adverse effects of disturbances on the flight control of the system. Furthermore, the control signal U output by the control method of the present invention enables the UAV to achieve high-precision position tracking and suppress the sway of the suspended load, ensuring that the UAV position tracking error and the load sway angle vary within a constrained range. The UAV suspended flight reduction control system of the present invention is used to implement the above-mentioned control method. By observing the UAV through an observer to obtain an estimated value, and outputting a control signal to the UAV flight system through a backstepping controller, the UAV can achieve precise control of the UAV position and sway reduction control of the suspended load.

This application relates to the field of servo system control technology, and provides a green disturbance rejection control method and device for a CMG frame servo system. The method establishes a mathematical model of the CMG frame servo system under multi-source disturbances based on a high-order full-drive method, and models the multi-source disturbances as external sources. Based on the external source model and the system mathematical model, a fine disturbance separation estimator is determined. The fine disturbance separation estimator is used to determine the equivalent estimate of the multi-source disturbances. Based on this equivalent estimate, a state observer is determined, and then the state observer is used to determine the estimate of the higher-order state variables. Finally, a backstepping controller based on a barrier Lyapunov function is established using the estimates of the multi-source disturbances and the higher-order state variables. This backstepping controller controls the CMG frame servo system, providing more adjustable degrees of freedom for control performance optimization, enhancing the fine disturbance rejection capability of the CMG frame servo system under multiple constraints, and exhibiting simple design and low energy consumption.

The invention provides a clamping force control method for an EMB executing mechanism of an electric vehicle. The clamping force control method comprises the following steps that a backstepping controller is used for controlling a motor used for driving the EMB executing mechanism; observing the load torque of the motor through a load observer, and obtaining the current position of a motor rotor through a sensor after the load torque of the motor is obtained; obtaining an expected motor rotation angle corresponding to the current expected brake clamping force according to the brake clamping force and motor rotation angle characteristic curve; and finally, according to the obtained deviation between the expected motor rotation angle value and the actual value, a backstepping controller is designed in combination with the motor load torque, and accurate output control over the brake clamping force is achieved. According to the invention, the EMB actuating mechanism is controlled in a manner of accurately controlling the permanent magnet synchronous motor by using the backstepping controller, so that the accurate output of the braking clamping force is realized, and the accurate control of the braking clamping force of the EMB actuating mechanism is realized.

The application discloses a kind of based on manned submersible's submarine pipeline leak source searching method and system, method includes: the system model of establishing including submarine oil spill diffusion model, submersible upper layer control model and oil concentration sensor model;Based on the estimation framework of bayes, fusion real-time measurement information, adopt particle filter iterative update the posterior probability distribution of leak source state;Based on posterior distribution, adopt exploration and development dual control strategy to construct objective function, solve optimal control quantity and expected target position;According to target position, based on the dynamics model of submersible, design based on interference observer's backstepping controller drive submersible accurate tracking.The application realizes the autonomous positioning of leak source under complex submarine environment, with strong adaptability, high search efficiency, the advantages of good anti-interference ability, suitable for emergency search task of sudden leakage.

This disclosure provides a method and system for safe control of unmanned vehicles based on derivative-aware reinforcement learning, relating to the field of vehicle safety control technology. The method includes: establishing a three-degree-of-freedom disturbed vehicle horizontal plane dynamic model and a DoS attack model based on the dynamic characteristics of the unmanned vehicle, and describing the vehicle horizontal plane dynamic model as a disturbed second-order nonlinear model; designing a finite-time switching state observer and a safe backstepping controller based on the disturbed second-order nonlinear model; inputting the parameter errors and external disturbances observed by the switching state observer to the safe backstepping controller; integrating a derivative-aware Actor-Critic reinforcement learning enhancement channel and a steady-state priority scheduler on the basis of safe robust backstepping control; enhancing the online optimization of transient tracking performance through reinforcement learning during the intervals of the DoS attack model; and reducing the reinforcement learning authority in the steady-state region through the steady-state priority scheduler to protect the steady-state error accuracy of the safe main control. This disclosure protects the steady-state error accuracy of the safe main control.

The present application relates to a kind of structured neural network controller design method and system for trajectory tracking control problem.The method includes the following steps: according to the information of moving object, the dynamic model of the moving object is constructed, the dynamic characteristics of the moving object are analyzed;Input convex neural network is constructed;Lagrange neural network is constructed;Neural backstepping controller design when system model information is accurately known is executed;The structure of the Lagrange neural network is improved using the input convex neural network, and the neural backstepping controller design when system model is unknown is executed.The system includes the following modules: dynamic characteristics analysis module, convex neural network construction module, Lagrange neural network construction module, configured to construct Lagrange neural network therein, first neural backstepping controller design module, second neural backstepping controller design module.The neural backstepping controller proposed in the present application has stability guarantee and performance guarantee, is stable for any feasible deep neural network parameters, and can improve performance by optimizing parameters, in the case where system exists disturbance or model uncertainty, steady-state tracking error can also be guaranteed to be bounded.

The invention provides a sensorless robust prediction-backstepping double-loop control method and device for a permanent magnet synchronous motor, and solves the problems that in an existing sensorless control system of the permanent magnet synchronous motor, a PI control structure is difficult to give consideration to fast dynamic response and robust stability, and a complex nonlinear algorithm is poor in real-time performance and insufficient in realizability. According to the method, a sliding-mode observer based on a Sigmoid function is constructed to estimate induced electromotive force, and a phase-locked loop is combined to extract a smooth rotor speed and position estimation value; a robust prediction controller based on a proportion-integral type error trend index is adopted in a rotating speed outer ring, an integral type backstepping controller is adopted in a current inner ring, and the motor is driven to operate through SVPWM modulation. According to the invention, through prediction-backstepping double-loop cooperative control, the dynamic response speed, anti-disturbance capability and stability of the system are substantially enhanced, the structure is simple, and engineering realization is easy.

The application discloses a heave compensation winch interference compensation control method. The method comprises the following steps: based on the state space model of a heave compensation winch system comprising a heave compensation winch and a pump station, a backstepping controller is established by a backstepping control method; an online parameter estimation module of the hydraulic motor of the heave compensation winch is established, the initial identification parameters of the hydraulic motor and the dynamic regression vector of the hydraulic cylinder are inputted for processing, and the identification parameters of the hydraulic motor are obtained in real time; the identification parameters and the operation parameters of the heave compensation winch are inputted into the backstepping controller, the valve core displacement control instruction of the proportional servo valve of the pump station is outputted after processing, and then the interference compensation control of the heave compensation winch is realized by controlling the pump station. The method can still maintain the high-precision tracking and robust stability control of the heave compensation winch system under the condition of model uncertainty and strong disturbance, and provides support for underwater high-precision operation requirements.

The invention discloses a networked motor preset performance control method based on an adaptive event trigger mechanism, and the method comprises the steps: firstly constructing a DC motor mathematical model according to a voltage balance equation and a torque balance equation; constructing a preset performance function based on a rotating speed tracking error generated by the DC motor mathematical model; performing nonlinear transformation on the rotating speed error according to a preset performance function to obtain an intermediate variable, and constructing a backstepping controller in combination with a mathematical model of the direct current motor; constructing a self-adaptive event triggering mechanism of a network channel between the backstepping controller and the actuator; and constructing a control structure based on equivalent input interference estimation and compensation in a local loop of the direct current motor. According to the method, the preset performance function is embedded into the backstepping controller, accurate transient and steady-state performance guarantee is achieved, it can be strictly guaranteed that system output always meets preset performance index requirements in the whole operation process, and the accuracy of motor rotating speed tracking and predictability of control quality are remarkably improved.

The invention discloses a multi-strategy traffic car-following control system based on a vehicle-mounted side cloud collaborative architecture. The multi-strategy traffic car-following control system comprises a vehicle-mounted control unit, a roadside communication identification unit and a cloud control management platform, the roadside communication identification unit is deployed at a road key node and is used for identifying a road type and a communication state in real time and sending a control strategy switching instruction according to the road type and the communication state which are identified in real time; the vehicle-mounted control unit integrates a self-adaptive sliding mode controller and a nonlinear self-stabilization backstepping controller which are respectively used for following control of a straight road section and a curve road section; and the cloud control management platform is used for regularly updating controller parameters through historical data and pushing the controller parameters to the vehicle-mounted control unit and the roadside communication identification unit. Automatic switching of control strategies is achieved according to the structure type of the road where the vehicle is located, a mixed weight fusion control structure is adopted, transition of signals of different controllers is natural in the switching process, and therefore the control precision and stability of the mixed motorcade in the complex traffic environment are improved.