Low Complexity Distributed Model Predictive Control by Using Contractive Sets

Alexandra Grancharova (1) , Sorin Olaru (2)

1 University of Chemical Technology and Metallurgy [Sophia]
2 L2S - Laboratoire des signaux et systèmes

In this paper, an approach to low complexity distributed MPC of linear interconnected systems with coupled dynamics subject to both state and input constraints is proposed. The suggested approach is based on the idea of introducing a contractive set constraint in the centralized MPC problem formulation, which would guarantee the closed-loop system stability when using a small prediction horizon. Then, a dual accelerated gradient method is applied to obtain distributedly a suboptimal solution of the resulting Quadratic Programming problem. The suggested approach would be appropriate for embedded distributed MPC since it will reduce the complexity of the on-line MPC computations, simplify the software implementation, and reduce the requirements for available memory. (C) 2017, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

Mots clés: Predictive control; Distributed control; Interconnected systems; Linear systems; Constraints

Domaines: Automatique

Alexandra Grancharova, Sorin Olaru. Low Complexity Distributed Model Predictive Control by Using Contractive Sets. IFAC-PapersOnLine, 2017, 50 (1), pp.13164-13169. ⟨10.1016/j.ifacol.2017.08.2171⟩. ⟨hal-02345885⟩