Research On Motion Control Method For Autonomous Vehicle Based On Model Predictive Control

With the rapid development of low delay and high reliability communication technology represented by dedicated short range communication,autonomous driving technology is gradually maturing.As one of the key issues in the field of autopilot,motion control is the foundation of automatic driving.Scholars at home and abroad focus on the motion control of autopilot,which includes lateral motion control,longitudinal motion control and multi-vehicle cooperative control.Therefore,formulating appropriate control methods to overcome the nonlinearity of vehicles and the complexity of driving conditions has far-reaching research significance.In this paper,model predictive control is used to study the motion control of autonomous driving vehicle.Aiming at the lateral motion control of autonomous vehicles,for the purpose of reduce the tracking error and improve the stability of vehicle motion,a linear time-varying model predictive controller based on tracking error is built.First,a nonlinear vehicle dynamics model is established considering lateral error,lateral error rate,heading error,and heading error rate;Then,to improve the real-time performance of the operation,the dynamics model is linearized and discretized;Finally,the MPC controller is constructed based on linear error model.The simulation results show that the MPC controller in this paper has strong robustness to speed.At the same time,the tracking error is lower than that of the ordinary MPC controller,and the control performance is better.In the view of the longitudinal motion control of autonomous vehicles,considering the complexity of the following conditions,a multi-objective adaptive cruise control(MOACC)system is designed.First,a workshop longitudinal kinematics model is established;Second,in the upper-level controller,considering multiple control objectives such as driving safety,driving comfort,dynamic tracking and fuel economy,a comprehensive performance index function is built to optimize the desired acceleration using MPC theory;Finally,In the lower controller,the feedforward plus feedback method is used to modify the established inverse longitudinal dynamic model to achieve the vehicle's tracking of the expected acceleration.The simulation results show that the MOACC system in this paper can effectively adapt to following vehicles in various working conditions,and has high tracking accuracy and good comfort.For multi-vehicle coordination motion control,to improve the fuel economy when the vehicle passes through the continuous signal intersection and reduce the negative impact of the driver error on the economic driving of the network connected vehicle,a method of economic driving control of the network connected vehicle considering the driver error is proposed.Based on the traffic signal timing method,the target vehicle speed is planned,the driver error model is established by Markov process,the optimal vehicle speed sequence is solved by stochastic model predictive control algorithm,and the fuel consumption of networked vehicles is calculated by the approximate fuel consumption model.The simulation results show that the control method in this paper is basically consistent with the ideal situation.Compared with the benchmark method,the optimal speed sequence in this paper fluctuates less,and the average fuel economy is improved.