Learning in Networked Control Systems
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We design adaptive controller (learning rule) for a networked control system (NCS) in which data packets containing control information are transmitted across a lossy wireless channel. We propose Upper Confidence Bounds for Networked Control Systems (UCB-NCS), a learning rule that maintains confidence intervals for the estimates of plant parameters (A_(),B_()), and channel reliability p_(), and utilizes the principle of optimism in the face of uncertainty while making control decisions. We provide non-asymptotic performance guarantees for UCB-NCS by analyzing its "regret", i.e., performance gap from the scenario when (A_(),B_(),p_()) are known to the controller. We show that with a high probability the regret can be upper-bounded as Õ(C√(T))[%s], where T is the operating time horizon of the system, and C is a problem dependent constant.
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