We present a numerically efficient Nonlinear Model Predictive Control (NMPC) approach, called Set Membership based NMPC (SM-NMPC). In particular, a Set Membership method is used to derive from data an approximation and tight bounds on the optimal NMPC control law. These quantities are used to reduce the dimensionality and volume of the search domain of the NMPC optimization problem, allowing a significant shortening of the computation time. The proposed SM-NMPC strategy is tested in simulation, considering realistic autonomous vehicle scenarios, like parallel parking and lane keeping maneuvers.
@article{arxiv.2212.12414,
title = {Set Membership based Nonlinear Model Predictive Control},
author = {Mattia Boggio and Carlo Novara and Michele Taragna},
journal= {arXiv preprint arXiv:2212.12414},
year = {2023}
}