Adaptive MPC for trajectory tracking with online adaption of the vehicle model's yaw intensification

This paper presents an Adaptive Model Predictive Controller (AMPC) for vehicle trajectory tracking. The general goal is to adapt the vehicle model's yaw intensification, to compensate for lost tracking accuracy caused by model errors and condition changes. The approach combines a classic Model Predictive Controller (MPC) with an online learning algorithm based on a trajectory-dynamic lookup table. This lookup table is updated periodically according to the trajectory dynamics and the tracking performance of the controller. In contrast to current research, the proposed AMPC is able to adapt repeatedly to condition changes and transfer learned behavior to unknown trajectories. The feasibility of this approach is evaluated via simulation experiments in CarMaker and Matlab/Simulink on several closed loop circuits with additional weight to emulate condition changes.