A distributed framework for linear adaptive MPC

TL;DR

This paper introduces a distributed adaptive MPC framework for networked multi-agent systems that guarantees safety, reduces uncertainty online, and improves performance compared to existing methods.

Contribution

It proposes a novel distributed adaptive MPC algorithm with structured control design, ensuring robustness, recursive feasibility, and stability in multi-agent network systems.

Findings

Ensures robust constraint satisfaction and recursive feasibility.

Achieves finite gain $ ext{ell}_2$ stability.

Yields lower closed-loop cost than existing robust distributed MPC.

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.