Control of network systems with uncertain local dynamics has remained an open problem for a long time. In this paper, a distributed minimax adaptive control algorithm is proposed for such networks whose local dynamics has an uncertain parameter possibly taking finite number of values. To hedge against this uncertainty, each node in the network collects the historical data of its neighbouring nodes to decide its control action along its edges by finding the parameter that best describes the observed disturbance trajectory. Our proposed distributed adaptive controller is scalable and we give both lower and upper bounds for its l2 gain. Numerical simulations demonstrate that once each node has sufficiently estimated its local uncertainty, the distributed minimax adaptive controller behaves like the optimal distributed H infinity controller in hindsight.
Original languageEnglish
Title of host publicationDistributed Adaptive Control for Uncertain Networks
PublisherIEEE - Institute of Electrical and Electronics Engineers Inc.
Publication statusAccepted/In press - 2024

Subject classification (UKÄ)

  • Control Engineering


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