Distributed Implementation of Minimax Adaptive Controller For Finite Set of Linear Systems

TL;DR

This paper presents a scalable distributed minimax adaptive control algorithm for networked linear systems, which uses local data to achieve near-optimal control performance after parameter estimation.

Contribution

It introduces a novel distributed implementation of minimax adaptive control for finite set linear systems, leveraging local neighbor data for scalability.

Findings

Distributed controller performs like optimal H-infinity controller after parameter estimation

Scalable approach using only local neighbor data

Numerical simulations validate effectiveness

Abstract

This paper deals with a distributed implementation of minimax adaptive control algorithm for networked dynamical systems modeled by a finite set of linear models. To hedge against the uncertainty arising out of finite number of possible dynamics in each node in the network, it collects only the historical data of its neighboring nodes to decide its control action along its edges. This makes our proposed distributed approach scalable. Numerical simulations demonstrate that once each node has sufficiently estimated the uncertain parameters, the distributed minimax adaptive controller behaves like the optimal distributed H-infinity controller in hindsight.