Research Of Intelligent Vehicle Path Tracking Control Algorithm Based On CarSim

In recent years,the rapid development of the automotive industry has brought about a series of social problems such as energy consumption,environmental pollution and traffic congestion.Countries around the world have put forward many strategies and solutions for these problems.The establishment of intelligent transportation systems is one of them,and the most critical one is the application and research development of intelligent vehicles.In the era of information explosion and comprehensive intelligence,intelligent vehicles have become the focus and hotspot of research in the field of automobile control.The research on environmental perception,decision planning and tracking control in the intelligent driving system of intelligent vehicles is the most important.In the context of vehicle intelligence,this paper studies the intelligent vehicle path tracking control algorithm.Aiming at the problems of system nonlinearity,dynamic height coupling,kinematics and dynamics parameter uncertainty in the path tracking control of intelligent vehicles,a nonlinear robust adaptive triple-step control algorithm is designed.The simulation and verification analysis of multi-scenario are carried out in CarSim,an accurate vehicle dynamics simulation software.First,modeling the vehicle dynamics system is critical before designing the control algorithm.Therefore,the vehicle modeling tool high-precision vehicle dynamics simulation software CarSim and the controller design simulation calculation tool Matlab/Simulink software are introduced.According to the principle of vehicle motion,three reference coordinate systems of vehicle motion are constructed.The vehicle dynamics modeling is carried out according to Newton's law and d'Alembert principle."Magic formula" tire model is modeled according to the tire dynamics principle.In the high-precision simulation software CarSim,the vehicle model is selected and various subsystems are established.In order to better verify the vehicle tracking control capability,four types of scene models,such as double-lane straight road,cross-road turn right angle bend,four-lane roundabout and driving in and out of the island simulation,and multi-moving target vehicle settings were built in CarSim.And they will be used for simulation verification of control algorithm for multiple operating conditions.Then,in the design of the control algorithm,the triple-step method control is a nonlinear control algorithm that is widely used in the field of vehicle control with clear design logic,clear physical meaning and simple engineering realization structure.However,for some time-varying parameters in vehicle motion,the traditional triple-step control method has a certain limitations.Therefore,based on the traditional triple-step control design,a robust adaptive triple-step control method algorithm is designed to solve the problem.The adaptive problem of uncertain parameters exists in the system,and the linear matrix inequalities of control law parameters are given to obtain a safe,fast,stable and robust adaptive tracking control system.The basic principle of triple-step control method and the control principle of adaptive triple-step control method are introduced.The adaptive triple-step control method laws and adaptive laws of first-order nonlinear affine system and second-order nonlinear affine system are derived,respectively.Aiming at the problem of intelligent vehicle path tracking control in this subject,the mathematical model of kinematics and dynamics required to establish the design control algorithm is described.According to the requirements of longitudinal and lateral tracking,the longitudinal first-order robustness is designed.An adaptive triple-step control method and a lateral second-order robust adaptive triple-step control method are given.The steadystate feedback control law,reference dynamic feedforward control law,feedback control law and adaptive law are given.And a linear matrix inequality solution of the PID parameters of the feedback control law is given.Finally,the proof of the robust stability of the algorithm and the derivation process of the adaptive law are given.