Robust Adaptive Iterative Learning Control for A Generic Class of Uncertain Non-square MIMO Systems

In this work, the adaptive iterative learning control (AILC) for a generic class of non-square nonlinear systems is investigated in presence of unknown control gain matrices and non-parametric iteration-varying uncertainties. Differently from the existing approaches, the present work develops a unified, structurally simple and user-friendly AILC method, which is effective to handle nonlinear systems with parametric or non-parametric uncertainties, square or non-square input matrices, known or unknown control directions. From the design point of view, the proposed approach extends the AILC approach to non-square systems with unknown control gain matrices, which contributes significantly not only to refine the theory of AILC, but also to widen its application scope. The convergence of the proposed control algorithms are analysed rigorously by using the composite energy function methodology, and their effectiveness have been verified through an illustrated example.