Nonlinear Model Predictive Control: an Optimal Search Domain Reduction

Abstract

Nonlinear Model Predictive Control (NMPC) is a powerful control method, used in many industrial contexts. NMPC is based on the online solution of a suitable Optimal Control Problem (OCP) but this operation may require high computational costs, which may compromise its implementation in “fast” real-time applications. In this paper, we propose a novel NMPC approach, aiming to improve the numerical efficiency of the underlying optimization process. In particular, a Set Membership approximation method is applied to derive from data tight bounds on the optimal NMPC control law. These bounds are used to restrict the search domain of the OCP, allowing a significant reduction of the computation time. The effectiveness of the proposed NMPC strategy is demonstrated in simulation, considering an overtaking maneuver in a realistic autonomous vehicle scenario.