Learning Predictive Control with Online Modeling for Agile Maneuvering of Autonomous Vehicles

Abstract

The agile maneuvering control of autonomous vehicles (AVs) requires the tracking of reference trajectories characterized by high acceleration, sharp curvature, considerable disturbances, and significant time-varying, all while ensuring stability and accuracy. The inherent uncertainty and time-varying nature of both the vehicle model and its environment pose significant challenges to achieving high-performance tracking during agile maneuvers. Developing a control algorithm that enables solving the optimal policy for nonlinear systems with uncertainties is critical. In this paper, we propose a learning-based predictive control approach, namely, an adaptive model predictive control (AMPC) with Actor-Critic Learning (ACL) for generating closed-loop MPC policies for agile maneuvering of AVs. The proposed approach leverages neural networks to model the dynamics uncertainties online. The control policy and model are updated simultaneously to realize performance op-timization under time-varying uncertainties. Simulation results demonstrate that our proposed algorithm outperforms other leading ACL methods, as well as MPC and Linear Quadratic Regulator (LQR). Furthermore, field test experiment results validate its effectiveness on the HongQi-EHS3 electric vehicle, showing superior control performance compared to MPC both on paved roads and curved off-roads with excellent stability performance.