Study On Lateral Motion Control Of The Intelligent Vehicles Based On Model Predictive Control

With the development of society and economy,the number of motor vehicles in China has increased rapidly and brings convenience to people’s life.It also causes a series of problems,such as environment,safety,energy and transportation.Intelligent vehicle can greatly improve the security of the traffic system,and is one of the effective means to solve the traffic congestion.With "Made in China 2025" and " the Internet plus era " development strategy,the development of intelligent vehicle is risen to the national strategic level.The automobile industry is bound to be ushered in a new development period.The development concept of "the 13 th Five-Year Plan,which includes innovation,coordination,green,opening and sharing,will also promote automobile industry in a new development trend and direction along the route of high-tech and green manufacturing.Therefore,this project proceeds from the intelligent vehicle technology and studies the lateral motion control which is a key technology of intelligent vehicle.In this paper,the research status of intelligent vehicle and the lateral motion control is analyzed.The optimization method is proposed for the shortcomings of traditional control algorithm,like low environmental applicability.According to the characteristics of the intelligent vehicle,the lateral motion control of intelligent vehicles is studied from the vehicle path tracking control.The nonlinear vehicle tire model is established with the magic tire formula,then the linear working area of the tire can be determined.A multi-degree-of-freedom dynamic model for vehicle is established.Based on the model predictive control(MPC)theory,the vehicle path tracking controller is designed.Take consideration of the practical application and the highway traffic,this paper is focusing on the stability of the vehicle in the path tracking process and the tracking precision under the high-speed driving condition.The simulative analysis of the controller adopted the Carsim/Simulink joint simulation method.The results show that the controller has good tracking accuracy and vehicle stability under different speeds.At the same time,the PID controller path tracking controller is designed for comparison.The comparison results show that the MPC controller has better control effect.Considering the control parameters of the controller and the change of the actual state of the vehicle,a controller optimization scheme based on the road parameters and the change of vehicle state is proposed in use of characteristics of the Intelligent Connected Vehicles(ICV).Firstly,control parameter optimization scheme.On the simulation platform,we also verify the operation stability and tracking accuracy of the optimized controller.The optimal control parameters for different situations are determined with simulation.Based on the characteristics of ICV,the selection mechanism of control is designed.The control stability and tracking accuracy of the optimized controller are also verified on the Carsim/Simulink joint simulation platform.As the idealized simulation conditions are different from the actual vehicle environment,the experience on a real car is very important.In order to verify the performance of the model predictive controller,real vehicle comparative experiment is carried out for PID path tracking controller and model predictive path tracking controller under the same working condition with the HAVAL H8 intelligent vehicle platform.The road information is collected by the RT2000GPS/ Inertial Navigation System as the target trajectory and experimental data.The experimental results are completed to verify the stability and tracking accuracy of the model predictive path tracking controller.