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arXiv · 2109.05777

A distributed framework for linear adaptive MPC

Anilkumar Parsi·Ahmed Aboudonia·Andrea Iannelli·John Lygeros·Roy S. Smith
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Abstract

Adaptive model predictive control (MPC) robustly ensures safety while reducing uncertainty during operation. In this paper, a distributed version is proposed to deal with network systems featuring multiple agents and limited communication. To solve the problem in a distributed manner, structure is imposed on the control design ingredients without sacrificing performance. Decentralized and distributed adaptation schemes that allow for a reduction of the uncertainty online compatibly with the network topology are also proposed. The algorithm ensures robust constraint satisfaction, recursive feasibility and finite gain $\ell_2$ stability, and yields lower closed-loop cost compared to robust distributed MPC in simulations.

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Anilkumar Parsi, Ahmed Aboudonia, Andrea Iannelli, John Lygeros, Roy S. Smith. 2021-09-13. A distributed framework for linear adaptive MPC. https://doi.org/10.1109/cdc45484.2021.9683290.

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