Iterative learning control for network systems with communication delay or data dropout

In this paper, iterative learning control (ILC) is applied to network-based control problems in which communication channels are subject to random transport delay and data dropout. An averaging ILC algorithm is used to overcome the random factors. Through analysis, it is shown that ILC can perform well and achieve asymptotical convergence in ensemble average along the iteration axis, as far as the probability of the transmission delay and data dropout are known a priori. A unique contribution in this work is to illustrate the applicability of ILC to nonlinear systems while both the one-step delay and the data-dropout phenomena are taken into consideration. Theoretical analysis and simulations validate the effectiveness of the ILC algorithm for network-based control tasks.