Iterative learning control for linear systems with Markov data dropouts: Noise-free case

This paper contributes to the convergence analysis of iterative learning control (ILC) for a linear time-varying system with measurement data dropouts, where the data dropout problem is formulated by a Markov chain model. The widely used Bernoulli model for data dropout is a special case of the Markov chain model. A regulating parameter is added to the classic P-type update law. The mean square and almost sure convergence are strictly proved. An illustrative example is provided to verify the theoretical results.