Real-time Nonlinear Model Predictive Control using One-step Optimizations and Reachable Sets
Abstract
Model predictive control allows solving complex control tasks with control and state constraints. However, an optimal control problem must be solved in real-time to predict the future system behavior, which is hardly possible on embedded hardware. To solve this problem, this paper proposes to compute a sequence of one-step optimizations aided by pre-computed inner approximations of reachable sets rather than solving the full-horizon optimal control problem at once. This feature can be used to virtually predict the future system behavior with a low computational footprint. Proofs for recursive feasibility and for the sufficient conditions for asymptotic stability under mild assumptions are given. The presented approach is demonstrated in simulation for functional verification.
Cite
@article{arxiv.2304.05768,
title = {Real-time Nonlinear Model Predictive Control using One-step Optimizations and Reachable Sets},
author = {Jan Olucak and Walter Fichter and Torbjørn Cunis},
journal= {arXiv preprint arXiv:2304.05768},
year = {2023}
}
Comments
Submitted to 62nd IEEE Conference on Decision and Control 2023