Abstract
In this article, a new model-free approach is proposed to solve the output regulation problem for networked control systems, where the system state can be lost in the feedback process. The goal of the output regulation is to design a control law that can make the system achieve asymptotic stability of the tracking error while maintaining the stability of the closed-loop system. The solvability of the output regulation problem depends on the solvability of a set of matrix equations called the regulator equations. First, a restructured dynamic system is established by using the Smith predictor; then, an off-policy algorithm based on reinforcement learning is developed to calculate the feedback gain using only the measured data when dropout occurs. Based on the solution to the feedback gain, a model-free solution is provided for solving the forward gain using the regulator equations. The simulation results demonstrate the effectiveness of the proposed approach for discrete-time networked systems with unknown dynamics and dropout.
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