Research On Trajectory Planning And Control Method Of Hub Motor Intelligent Vehicle

The emergence of intelligent electric vehicles has greatly eased the pressure of environmental pollution caused by traditional internal combustion engine vehicles and the traffic pressure caused by the sharp increase in vehicle ownership.The research of intelligent electric vehicles is an important direction for the future development of automobiles.In this paper,the method of trajectory planning and trajectory tracking control for intelligent vehicle with hub motor is studied.The purpose of this study is to provide path information for intelligent vehicles,and to replace the driver to control the vehicle to track the track on different roads.The specific research content of the article is as follows:(1)In this paper,the vehicle system is modeled from the point of view of trajectory planning and tracking control.Firstly,considering the kinematic constraints of the hub motor intelligent vehicle in trajectory planning and the low-speed driving conditions in tracking control,the vehicle kinematic model is established.Secondly,aiming at the tracking control of intelligent vehicle with hub motor in high-speed driving condition,a dynamic model combined with tire model is established.Additionally,considering the real-time performance of the tracking control algorithm,the assumption of small sideslip angle of tire is made for the vehicle dynamics model.Finally,the steering differential characteristics of intelligent vehicle with hub motor are analyzed.(2)Based on the established vehicle kinematics model,a hub motor intelligent vehicle trajectory planning method based on improved RRT algorithm is designed.Firstly,the search of random trees is not completely random by using the principle of extended target bias.Then,based on the vehicle kinematic constraints,the random diffusion region is limited to ensure the feasibility of the trajectory.Additionally,vehicle size is considered,and collision detection between vehicle and obstacle is carried out based on separation axis theorem.Finally,the basic RRT algorithm and the improved RRT algorithm in this paper are simulated under different road conditions,and the improved RRT algorithm is verified by real vehicle.The simulation results show that the improved RRT algorithm proposed in this paper has the advantages of less random sampling,high search efficiency and smooth path than the basic RRT algorithm.The results of real vehicle verification show that the improvement of the smoothness of the planned path of the RRT algorithm and the efficiency of the planned path are superior to the A * planning algorithm commonly used in intelligent vehicles.(3)Based on the model predictive control algorithm,two kinds of intelligent vehicle trajectory tracking controllers are designed in this paper.First,the process of model predictive control is deduced in detail,including the linear discretization of nonlinear vehicle systems,the establishment of predictive equations,the design and solution of objective functions,and feedback adjustment.Then,using the established kinematics model and model predictive control algorithm,an MPC trajectory tracking controller based on vehicle kinematics is designed for the low-speed operating environment,which includes the establishment of predictive model,the design of constraint conditions,the optimization solution,etc.,and the corresponding S-function program is compiled.Finally,using the dynamic model and model predictive control algorithm,aiming at the high-speed running environment of the vehicle,the MPC trajectory tracking controller based on the vehicle dynamics is designed under the background of the front wheel active steering,which mainly includes the establishment of the predictive model,the design of the dynamic restraint conditions,the optimization solution,the programming of the Sfunction,etc.(4)A joint simulation platform was established to verify the performance of the tracking controller.Firstly,a hub motor model was built,and Car Sim /Simulink was combined to build a wheel motor vehicle model.Then,combining the two tracking controllers built in Simulink in Chapter 4,the Car Sim/Simulink co-simulation platform for trajectory tracking control of hub motor vehicles is established.Finally,different simulation conditions are designed for the two tracking controllers respectively,and the simulation verifies the effect of the hub motor intelligent vehicle tracking control.The simulation results show that the MPC controller designed in this paper can track the desired trajectory quickly and stably at low speed.At high speed,the MPC controller based on vehicle dynamics model designed in this paper can track the desired trajectory well at different speeds,and can adapt to the trajectory tracking control in different road environments.