Distributed Data-Driven Control of Network Systems

Imperfect models lead to imperfect controllers and deriving accurate models from first principles or system identification is especially challenging in networked systems. Instead, data can be used to directly compute controllers, without requiring any system identification or modeling. In this paper we propose a strategy to directly learn control actions when data from past system trajectories is distributed among multiple agents in a network. The approach we develop provably converges to a suboptimal solution in a finite number of steps, bounded by the diameter of the network, and with a sub-optimality gap that can be characterized as a function of data, and that can be made arbitrarily small. We further characterize the robustness properties of our approach and give provable guarantees on its performance when data are affected by noise or by a class of attacks.