An algorithm of adaptive iterative learning control based on process models

An algorithm of adaptive iterative learning control (AILC) based on model is proposed for a class of CARMA models with repetitive trajectory in a specifice period of time. Process model paremeters are identificated using previous time data and AILC law of the current time is constituted by process model paramenters. Convergence and stability are analyzed. The algorithm for linear stable process can achieve the unbiased fast-tracking. Several simulation examples demonstrate the effectiveness and robustness of the algorithm.