Data-based iterative learning control for nonlinear systems subject to iteration-dependent durations

This paper addresses the data-based iterative learning control (ILC) problem for locally Lipschitz nonlinear systems, where the durations are iteration-dependent. A test framework is developed to perform test iterations for collecting specific input and output data from nonlinear ILC systems. By resorting to these data, an ILC updating law is provided through integrating modified outputs to compensate for the adverse effects of iteration-dependent durations. Thanks to the persistent full-learning property, a necessary and sufficient condition is proposed to accomplish the iteration-dependent perfect tracking objective, which depends on the output data. The developed ILC updating law that employs only data particularly applies to locally Lipschitz nonlinear ILC systems subject to irregular dynamics.