Intermittent and Successive ILC for Stochastic Nonlinear Systems with Random Data Dropouts

The iterative learning control (ILC) problem is addressed in this paper for stochastic nonlinear systems with random data dropouts. The data dropout is modeled by the conventional Bernoulli random variable to describe the successful transmission or loss. Both intermittent and successive ILC are considered, where the former stops updating if no information is received, while the latter keeps updating based on the latest available data. It is strictly proved the almost sure convergence of both algorithms. The simulations on a mechanical model are provided to show the comparisons and effectiveness of the proposed algorithms.