625	RWTH Publication No: 975585        2023
TITLE	Model predictive control strategies using consensus-based optimization
AUTHORS	Giacomo Borghi, Michael Herty
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.
KEYWORDS	Model predictive control, Consensus-based optimization, Stochastic particle method, Nonlinear systems