2023/10/04 by T. T. Chang, Shahin Shahrampour, Chang, Ting-Jui +1 · 1 citation
Computer Science · Decision Sciences · Engineering · #Adaptive Dynamic Programming Control #Advanced Bandit Algorithms Research #Advanced Control Systems Optimization #FOS: Computer and information sciences #FOS: Electrical engineering #FOS: Mathematics #Machine Learning (cs.LG) #Optimization and Control (math.OC) #Systems and Control (eess.SY) #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2310.03206
openalex publication_date 2023/10/04 · openalex created_date 2023/10/09 · openalex updated_date 2026/07/28
This paper addresses the distributed online control problem over a network of linear time-invariant (LTI) systems (with possibly unknown dynamics) in the presence of adversarial perturbations. There exists a global network cost that is characterized by a time-varying convex function, which evolves in an adversarial manner and is sequentially and partially observed by local agents. The goal of each agent is to generate a control sequence that can compete with the best centralized control policy in hindsight, which has access to the global cost. This problem is formulated as a regret minimization. For the case of known dynamics, we propose a fully distributed disturbance feedback controller that guarantees a regret bound of O(√(T)log T), where T is the time horizon. For the unknown dynamics case, we design a distributed explore-then-commit approach, where in the exploration phase all agents jointly learn the system dynamics, and in the learning phase our proposed control algorithm is applied using each agent system estimate. We establish a regret bound of O(T2/3 poly(log T)) for this setting.