Coalitional control is a type of distributed control characterized by the dynamic adjustment of the overall controller structure so that only strongly coupled agents interact with each other. In particular, local controllers merge into cooperative coalitions (or clusters) only when it improves global performance, thus reducing the overall cooperation burden. This paper proposes a novel coalitional model predictive control (MPC) approach in which coupled variables are decomposed into a public part, which is optimized by the neighboring agents, and a private one, which is locally controlled by the agent that owns it. The bounds on these variables are negotiated in a distributed manner, and a threshold is established to trigger different interaction modes, including the classical decentralized and distributed approaches, and flexible modes of cooperation. Finally, to illustrate the benefits of this control scheme, results on a simulated eight input-coupled tanks plant are provided.
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