Online System Identification and Control for Linear Systems With Multiagent Controllers Over Wireless Interference Channels

In this article, we consider identification and control for potentially unstable linear systems with multiagent controllers in the presence of wireless interference channels among the remote controllers and the actuators. We formulate the optimal control problem as a stochastic game and utilize the stochastic approximation technique to learn the optimal control solutions and the plant dynamics simultaneously in an online manner. Using Lyapunov drift analysis, we show that the identified plant dynamics and the learned control solutions via the proposed algorithm can converge to the true plant dynamics and optimal control solutions, respectively. Furthermore, we also rigorously show the convergence speed of the proposed learning algorithm in terms of the online learning step sizes in the proposed algorithm. The proposed scheme is also compared with various state-of-art baselines and we show that significant performance gains can be achieved.