Iterative learning control for nonlinear nonaffine networked systems with stochastic noise in communication channels

Abstract

This article investigates the convergence analysis of networked iterative learning control for nonlinear nonaffine systems firstly by considering stochastic noise introduced by the network channels. The convergence analysis is under a data-driven framework, which does not rely on any mechanism model information. To deal with the nonlinearity, both the state transition technique and the differential mean value principle are used to formulate the iterative dynamics of system states, tracking errors and input signals using a lifted matrix expression, respectively. In terms of the contraction mapping principle, the tracking error is shown to be iteratively convergent under the sense of mathematical expectation. Since the $\lambda$ -norm is not used in the analysis, the convergence property of the tracking error is not affected by the operation interval and a good transient performance can be ensured in theory. Simulation studies test the theoretical results.