Vehicle Path Tracking Control Based on Adaptive Sampling Period and Predictive Time Domain MPC
In order to solve the problem of low accuracy of path tracking control of autonomous vehicle on low adhesion road surfaces,an adaptive sampling period and prediction time domain MPC controller was designed.Firstly,the MPC controller was designed by combining the vehicle dynamics model and the MPC algorithm with the tire sideslip angle constraints.Then,the influence of sampling period and predic-tive time domain of controllers on the control effect was analyzed.A adaptive control strategy that compre-hensively considered the sampling period and predictive time domain was proposed,in which the sam-pling period was updated by the front wheel steering angle,and the predictive time domain was updat-ed by vehicle speed.Finally,using Carsim and Matlab/Simulink co-simulation platform,simulation experiments were carried out under different vehicle speeds on low adhesion road surface.The results showed that when the vehicle speed was 25 km/h and 45 km/h,compared with the fixed control pa-rameter MPC controller,the maximal lateral errors of the adaptive sampling period and predictive time domain MPC controller were reduced by 140.2 mm and 40.8 mm respectively;its path tracking control accuracy was higher at different vehicle speeds;the yaw rate and sideslip angle were all within reasonable limits,and the vehicle stability was good.It means the proposed path tracking controller has high control accuracy and feasibility on low adhesion road surfaces.
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