Analysis of continuous iterative learning control systems using current cycle feedback

Abstract

In this paper, a continuous iterative-learning control scheme with current cycle feedback (CCF type) is studied. Through theoretical analysis and comparison, we prove that faster convergence rate of the CCF type learning control can be achieved by selecting a higher feedback gain. In addition, owing to the current cycle error feedback, the control system is robust against any unpredictable small perturbation. In this paper, the multiperiod learning strategy is further developed in conjunction with the current cycle feedback algorithm. The method extended to the MIMO case. Finally, a number of case studies are carried out which support the aforementioned statements.