Learning control for linear systems under general data dropouts at both measurement and actuator sides: A Markov chain approach

This paper contributes to the convergence analysis of iterative learning control for linear systems under general data dropouts at both measurement and actuator sides. By using a simple compensation mechanism for the dropped data, the sample path behavior along the iteration axis is analyzed and formulated as a Markov chain first. Based on the Markov chain, the recursion of the input error is reformulated as a switching system, and then a novel convergence proof is established in the almost sure sense under mild design conditions. Illustrative examples are provided to verify the theoretical results.