| The lateral trajectory tracking and stability analysis of intelligent vehicle are the basic problems in motion control technology.Its research can not only improve the comfort,safety and stability of the vehicle during driving,but also provide a theoretical basis for the future driverless technologies.Due to the high nonlinearity and strong coupling of the vehicle system,it is difficult to design a high-precision lateral trajectory tracking controller.In addition,the stability of the vehicle is decreased during the tracking process at high speed,which directly affects the comfort and safety of the vehicle.Therefore,aiming at the problem of low tracking accuracy and insufficient stability on the process of lateral trajectory tracking,based on the vehicle dynamics simulation software CarSim and Matlab/Simulink co-simulation platform,this thesis mainly focuses on the research of lateral trajectory tracking and stability control algorithms.The main work of this thesis is as follows:(1)Research on vehicle dynamics modelBased on the analysis of the working principle of the vehicle during its movement.According to the Newton's second law,a two-degree freedom dynamic model of the vehicle is established.The required dynamic state equation is obtained according to the model and used for the design of lateral sliding mode controller.(2)Research on the lateral trajectory tracking based on fuzzy sliding mode control(SMC)Aiming at the problem of insufficient lateral tracking error which exists in the traditional sliding mode control,a fuzzy SMC is proposed.Firstly,the sliding surface function is designed according to the yaw rate error,the sliding surface function and its change rate are used as the input of the fuzzy controller.And then the fuzzy rules are designed according to the change of input variables,the control law of the front wheel angle is obtained through fuzzification,fuzzy reasoning and defuzzification.At last,the cosimulation test show that compared with the traditional SMC,the designed fuzzy SMC lateral trajectory tracking controller can reduce the tracking error.(3)Research on SMC based on RBF neural network for lateral trajectory trackingAiming at the chattering phenomenon exists in the sliding mode control surface and the lateral tracking error caused by the uncertainty of the dynamic model,a RBF-SMC method is proposed to solve the problem in this paper.The error between reference yaw angle and the actual yaw angle of the vehicle and change rate of error are used to design sliding mode surface function,the output of the sliding mode controller is the front wheel angle control law,and the stability and convergence of the control system are analyzed by Lyapunov theory,and then the RBF neural network is used for compensating a front wheel angle control law to reducing the tracking error caused by model uncertainty.The simulation results show that compared with the traditional SMC method,the accuracy of the lateral trajectory tracking is improved because of the nonlinear approximation ability of RBF-SMC method,moreover,the chattering phenomenon on the sliding mode surface is effectively reduced.(4)Research on the stability of intelligent vehicles based on SMCAiming at the stability problem on the process of lateral trajectory tracking control,the side slip angle,the yaw rate and lateral acceleration of the vehicle are selected as the important performance index to measuring the vehicle stability.In this thesis,based on the DYC strategy,a sliding mode controller is designed to improve the stability of the vehicle through controlling brake torque or drive torque of tire.The simulation results show that,compared without the DYC,the fluctuation range of the side slip angle,the yaw rate and lateral acceleration are decreased after adding DYC,at the same time,the variation is gentle of three performance indexes and there is no obvious sudden change.It shows that the vehicle stability is improved on the process of lateral trajectory tracking control. |
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