Model predictive control strategies using consensus-based optimization
Optimization and Control
2023-12-21 v1
Abstract
Model predictive control strategies require to solve in an sequential manner, many, possibly non-convex, optimization problems. In this work, we propose an interacting stochastic agent system to solve those problems. The agents evolve in pseudo-time and in parallel to the time-discrete state evolution. The method is suitable for non-convex, non-differentiable objective functions. The convergence properties are investigated through mean-field approximation of the time-discrete system, showing convergence in the case of additive linear control. We validate the proposed strategy by applying it to the control of a stirred-tank reactor non-linear system.
Cite
@article{arxiv.2312.13085,
title = {Model predictive control strategies using consensus-based optimization},
author = {Giacomo Borghi and Michael Herty},
journal= {arXiv preprint arXiv:2312.13085},
year = {2023}
}