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Data-driven Distributed and Localized Model Predictive Control by Alonso, Carmen Amo; Yang, Fengjun; Matni, Nikolai is a scholarly article available to read on EtoBox.

What is Data-driven Distributed and Localized Model Predictive Control about?

Motivated by large-scale but computationally constrained settings, e.g., the Internet of Things, we present a novel data-driven distributed control algorithm that is synthesized directly from trajectory data. Our method, data-driven Distributed and Localized Model Predictive Control (D$^3$LMPC), builds upon the data-driven System Level Synthesis (SLS) framework, which allows one to parameterize \emph{closed-loop} system responses directly from collected open-loop trajectories. The resulting model-predictive controller can be implemented with distributed computation and only local information sharing. By imposing locality constraints on the system response, we show that the amount of data needed for our synthesis problem is independent of the size of the global system. Moreover, we show that our algorithm enjoys theoretical guarantees for recursive feasibility and asymptotic stability. Finally, we also demonstrate the optimality and scalability of our algorithm in a simulation experiment.

Author

Alonso, Carmen Amo; Yang, Fengjun; Matni, Nikolai

Published

2021

Language

EN