Distributed Model Predictive Control for Dynamically Coupled Systems

Nowadays, more industrial processes consisting of dynamically coupled systems need to satisfy performance, production and efficiency demands. There are various control approaches for such systems, with distributed control being the trade-of between centralized and decentralized control strategies. The main idea is to consider each sub-system from the local point of view, taking also into account the coupling dynamics between sub-systems. The research domain of Distributed Model Predictive Control (DMPC) is active, in rapid expansion, and attracts increasing interest from the control community.

Since, DMPC algorithms have different classifications, the focus of this book is to present non-cooperative and coalitional DMPC algorithms, hereafter called Distributed Predictive Control Algorithms (DPCAs), which use distinct methods from the control literature to achieve zero-offset tracking error. All the described methods are suitable for solving constant or piecewise constant reference tracking problems.