On iterative learning control for MIMO nonlinear systems in the presence of time-iteration-varying parameters

In this study, the problem of adaptive iterative learning control (AILC) is considered for a class of multiple-input-multiple-output discrete-time nonlinear systems where the initial condition and reference trajectory could be randomly varying in the iteration domain. It is assumed that the considered systems are subjected to time-iteration-varying unknown parameters. The iteration-varying parameters are generated by a known high-order internal model (HOIM) that is formulated as a polynomial between two consecutive iterations. By incorporating the HOIM into the controller design, the learning convergence of ILC is guaranteed through rigorous analysis under Lyapunov theory. The illustrative example is presented to demonstrate the effectiveness of AILC method.