Iterative learning control using faded measurements without system information: a gradient estimation approach

Abstract

This paper studies iterative learning control (ILC) using faded measurements without system information. The measurements are transmitted through fading channels, where the fading phenomenon is modelled by a multiplicative random variable. The system matrices are assumed unknown a priori and a random difference technique is applied to estimate the gradient using the available tracking data. An online ILC algorithm is established with strict convergence analysis along the iteration axis, followed by practical variants and discussions. The generated input sequence is proved to converge to the desired one in the almost sure sense. Illustrative simulations are presented to verify the theoretical results.