Robust coalitional model predictive control with negotiation of mutual interactions. (March 2023) - Sánchez-Amores, A.; Chanfreut, P.; Maestre, J.M.; Camacho, E.F.
Abstract: This article presents a robust coalitional model predictive control (MPC) approach where neighboring agents negotiate the bounds of their coupling variables. Also, the control variables of each agent are divided into a private part that is locally optimized, and a public part that is controlled by the corresponding neighbors. Under certain conditions, the agents communicate to update the bounds that determine the constraints on these private and public variables. Moreover, the mutual disturbances induced by coupling are considered using a tube-based approach, guaranteeing recursive feasibility and stability of the closed-loop system. The proposed method is tested on a simulated eight-input coupled tank benchmark to illustrate its benefits. Highlights: Splitting the global optimization problem by clustering model predictive controllers. Coupling variables decomposition into multiple versions distributed among agents. Event-based communication to negotiate the size of the input constraint set. Mutual disturbances are considered using a tube-based approach. Feasibility and stability of the closed-loop system are guaranteed.