Research On Intelligent Vehicle Trajectory Tracking Control Algorithm

With the increase of car per person,a series of problems,such as vehicle congestion,traffic accidents,environmental pollution,energy consumption and so on,are caused,especially the traffic accidents.Because smart cars can partly or completely eliminate the impact of human factors,intelligent vehicles are considered to be able to solve traffic safety problems greatly.Efficient and stable trajectory tracking algorithm is the premise to realize the safe and stable running of automatic driving vehicles,and also the necessary condition for intelligent vehicles to realize commercialization.In this paper,we first study the traditional trajectory tracking control algorithm based on the optimal LQR(Linear Quadratic Regulator)theory,and establish the trajectory tracking control dynamics model and Fiala nonlinear tire model.Based on the established vehicle model,the optimal linear quadratic regulator is used to obtain the feedback gain and the optimal feedback control rate is determined to achieve the optimal trajectory following control input.Because the optimal LQR tracking algorithm has steady-state lateral deviation in the trajectory tracking process,an optimal feedforward LQR trajectory tracking algorithm is proposed to eliminate the steady-state lateral deviation to improve the trajectory tracking control accuracy.Because the method can not deal with the constraints,and the linearization of the nonlinear system is approximated,the influence of the reference trajectory shape on the control system is not considered.Therefore,the method is sensitive to curvature mutation and has overshoot under the condition of abrupt curvature input.In view of that,the tracking control method based on feedforward feedback is further studied in this paper.Firstly,the basic feedforward feedback trajectory tracking algorithm is designed.The feedforward algorithm is designed with the vehicle centroid as the reference point,and the feedforward algorithm compensates for the variation of the reference path curvature;The feedback algorithm is designed with the automobile foresight point as the reference point and adjusts the control input according to the vehicle state feedback,minimizing the influence of disturbance and model error on the tracking,and keeping the vehicle closer to the desired path and maintaining stability.Because the basic feedforward feedback algorithm is designed with different reference points,the effect of the rear wheel cornering force on yaw stability can not be eliminated.Therefore,in order to improve the stability of vehicle tracking under extreme conditions,a feedforward feedforward algorithm with COP(center of percussion)as the reference point is proposed to design the integrated feedforward feedback algorithm.The optimal LQR control algorithm is incorporated in the design process of feedback control,which improves the precision and stability of trajectory tracking control.Finally,in order to verify the effectiveness of the proposed algorithm,the above algorithms are verified by simulation,and the optimal feedforward LQR trajectory tracking algorithm and the basic feedforward feedback algorithm are tested in real vehicle.The results show that the improved basic feedforward feedback control algorithm has better control effect.,and can meet the requirements of intelligent vehicle trajectory following control.