Model predictive control strategies using consensus-based optimization

RWTH Publications (RWTH Aachen)

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.

Authors 2

1. Giacomo Borghi Aachen Institut für Geometrie und Praktische Mathematik

   RWTH Aachen University

   Affiliation as printed

   RWTH Aachen University , Institut für Geometrie und Praktische Mathematik , Templergraben 55 , Aachen , 52062 , Germany

2. Michaël Herty Aachen Institut für Geometrie und Praktische Mathematik

   RWTH Aachen University

   Affiliation as printed

   RWTH Aachen University , Institut für Geometrie und Praktische Mathematik , Templergraben 55 , Aachen , 52062 , Germany