High-order open and closed loop iterative learning control scheme with initial state learning

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In this paper, a high order open and closed ILC (iterative learning control) scheme with initial state learning is presented. The convergent bounds are only dependent on the system uncertainties and disturbances but independent of the initialization errors. The scheme performs better than common ILC scheme with initial state learning both in convergence rate and transient performance. By adding closed loop, the whole algorithm has better performance in both stability and convergence than the open loop one alone. Furthermore, the effectiveness of the proposed method is illustrated by simulation experiments.