Research for Nonlinear Model Predictive Controls to Laterally Control Unmanned Vehicle Trajectory Tracking

Abstract

The autonomous driving is rapid developing recently and model predictive controls (MPCs) have been widely used in unmanned vehicle trajectory tracking. MPCs are advantageous because of their predictive modeling, rolling optimization, and feedback correction. In recent years, most studies on unmanned vehicle trajectory tracking have used only linear model predictive controls to solve MPC algorithm shortcomings in real time. Previous studies have not investigated problems under conditions where speeds are too fast or trajectory curvatures change rapidly, because of the poor accuracy of approximate linearization. A nonlinear model predictive control optimization algorithm based on the collocation method is proposed, which can reduce calculation load. The algorithm aims to reduce trajectory tracking errors while ensuring real-time performance. Monte Carlo simulations of the uncertain systems are carried out to analyze the robustness of the algorithm. Hardware-in-the-loop simulation and actual vehicle experiments were also conducted. Experiment results show that under i7-8700, the calculation time is less than 100 ms, and the mean square error of the lateral deviation is maintained at 10−3 m2, which proves the proposed algorithm can meet the requirement of real time and accuracy in some particular situations. The unmanned vehicle trajectory tracking method provided in this article can meet the needs of real-time control.