Data-driven trajectory-tracking in automated parking system via iterative learning compensation and model predictive control

Abstract

Automated parking system (APS) that explicitly considers the time efficiency of the motion has received large amounts of attention in recent years. Trajectory planning module in these APS delivered parking trajectory, which was expected to be precisely tracked by tracking module. However, the reference points of frequently used trackers were selected in the spatial domain, resulting in significant trajectory tracking errors with temporal information. In this paper, a tracking control method called ILC-MPC, which combined model predictive control (MPC) and iterative learning control (ILC), was proposed to improve the spatiotemporal tracking accuracy of the autonomous vehicle. ILC was utilized for longitudinal compensation using the error signal between historical and expected speed. Accordingly, the error model in the longitudinal direction was simplified to decrease the number of decision variables in MPC. Simulation experiments using CarSim were carried out to compare the proposed method with open-loop control, linear quadratic regulator (LQR), and pure MPC that had a similar computing time with ILC-MPC. ILC-MPC converged in a few iterations of the learning process and achieved the highest tracking accuracy in spatiotemporal domain among the mentioned methods.