Distributed dissipativity and graph theoretic properties in distributed economic MPC

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The ability to control large networks of dynamical systems by local controlactions is an imperative prerequisite for the functionality of numerous modern engineering systems. Examples of such systems are smart grids, which use distributed control to integrate renewable energy sources into a network of intelligent" generators and loads; intelligent factories, which improve the production capabilities by controlling the transportation and manufacturing devices; or autonomous mobile systems, where groups of mobile agents have to perform complex tasks cooperatively. Despite the natural differences inherent to these envisioned systems, engineers working on any of them are all faced with one fundamental challenge: The dynamical systems (e.g., generators, production units, robots, etc.) have to be controlled in a way such that the control actions applied to a single system are consistent with the control actions applied to the other systems.This project proposes a constructive approach to address the above challenges within the distributed economic model predictive control framework. For this a novel model predictive control scheme will bedeveloped to solve control problems involving several distributed decision makers, all equipped with individual cost functions. We aim to develop algorithmic methods with guaranteed systems theoretic properties to ensure fairness and efficiency in such distributed and multi-objective set-ups. The key contribution of this project will be a connection between numerical distributed optimization algorithms and distributed model predictive control schemes. These results will provide a novel understanding of the requirements that distributed model predictive control imposes on the underlying numerical optimization algorithms.The present project is complemented by a partner project, proposed by Prof. Lars Grüne (University of Bayreuth). Together, the two projects will address the challenges of distributed economic model predictive control from a broad basis, and investigate conceptual as well as practical issues, ranging from a performance analysis up to numerical realizations.