| The development of automobile intelligence is an inevitable trend,in which motion control technology is related to driving safety and driving stability,is the current research hotspot,and high-speed turning condition is the research focus of intelligent vehicle driving.Therefore,it is very important to deeply analyze and study the trajectory tracking control performance of intelligent vehicle under high-speed turning condition.(1)Vehicle dynamics modeling and the establishment of trajectory tracking controller were the premise and foundation of the research.Based on the analysis of the target vehicle and the working condition,the nonlinear vehicle model was transformed into a linear time-varying model which was easy to calculate by using approximate linearization method.On the basis of the simplified vehicle model,build a based on model predictive control(MPC)algorithm of trajectory tracking controller,and used the Carsim/Simulink to build joint simulation platform,built to verify the effectiveness of the trajectory tracking controller.(2)Next,considering the nonlinear sideslip characteristics of tires under high-speed turning conditions,a tire dynamic sideslip stiffness estimator was designed to perform real-time estimation of tire sideslip stiffness,so as to solve the instability problem of the original controller caused by nonlinear changes in the control process,and the effectiveness of the estimator was verified through the co-simulation platform.Considering the influence of time domain parameters on the controller,particle swarm optimization algorithm(PSO)was introduced to optimize the time domain parameters of the controller,so as to improve the accuracy and stability of the controller in the control of vehicle running,and the comparison and verification was carried out through the co-simulation platform.Results show that the controller can improve the tracking precision error reduced to 4.9%,yawing Angle error reduced to 2.6%,prove the validity of the improved controller. |
PDF Full Text Request