An iterative learning controller using current iteration tracking error information and initial state learning

In this paper, an initial state learning scheme is proposed to remove the common assumption in the iterative learning control (ILC) that the initial states in each repetitive operation should be inside a given ball centered at the desired initial states, which may not be available. It is shown that the tracking error bound is independent of the initialization errors. By incorporating the current tracking error information in the ILC updating law, the uniform bound of the tracking error as well as the ILC convergence rate can be adjusted to a desired level. A class of nonlinear time-varying uncertain systems are investigated. The effectiveness of the proposed iterative learning controller is illustrated by a simulation