Robust Economic MPC for Perturbed Autonomous Electric Vehicles With Variable Space Constraints

This paper addresses the challenges of energy consumption, driving safety, and robustness in tracking control for autonomous vehicles under stochastic disturbances by proposing a robust economic model predictive control (REMPC) algorithm without terminal constraints. The disturbances considered include high-probability small disturbances as well as low-probability large disturbances in tracking control. A tightening constraint is introduced to ensure robustness against small disturbances, leveraging constraint tightening theory. Additionally, a maximum probability interval is derived to account for the large disturbances that the vehicle can withstand. To further enhance driving safety and energy efficiency, a variable space constraint is adaptively designed based on road slope information. The paper demonstrates recursive feasibility and robust asymptotic performance of the optimization problem with variable space constraints. Robust asymptotic stability in probability of the system is ensured by deriving a probability condition and a lower bound on the prediction horizon, with an exhaustive principle for selecting the prediction horizon. The efficiency and superiority of the proposed REMPC algorithm are verified by a comprehensive case study under various operating conditions.