Adaptive discrete-time iterative learning control for non-linear multiple input multiple output systems with iteration-varying initial error and reference trajectory
Most of the available results in iterative learning control (ILC) hitherto have considered the ILC systems with fixed initial error and iteration-invariant reference trajectory. An adaptive discrete-time ILC scheme is presented to deal with the ILC problem of non-linear multiple input multiple output systems with iteration-varying initial error and reference trajectory. The designed adaptive ILC law learns parametric system dynamics and pursues the iteration-varying reference trajectory tracking from iteration to iteration. It can drive the ILC tracking error to zero asymptotically beyond the initial time step, and keep all adjustable parameters and system signals bounded as the number of iteration approaches infinity. Numerical example is given to illustrate the effectiveness of the adaptive ILC scheme.
