Iterative learning control for a class of nonlinear systems with measurement dropouts
测量数据丢失的一类非线性系统迭代学习控制

This paper analyzes the stability of the iterative learning control (ILC) applied to a class of nonlinear discretetime systems with output measurement data dropouts. It is assumed that an ILC scheme is implemented via a networked control loop for the nonlinear system and that the packet dropout occurs due to limitations in network communication. The data dropout is described as a stochastic Bernoulli process with a given probability; on this basis we derive the convergence condition for the P-type ILC algorithm. The theoretical analysis is supported by the simulation of a numerical example; the convergence of ILC can be guaranteed when some output measurements are missing.