ABSTRACT
This paper is dedicated to iterative learning control for nonlinear systems with completely unknown states and time-iteration-varying parameter uncertainties. The unknown states cover unknown iteration-varying initial states and system operational states. The uncertainties are converted to a scalar without requiring an iterative sequence. A reference signal-based adaptive gain observer is developed to estimate the operational states. An iteration factor-based contraction mapping composite energy function is exploited to treat the iteration-varying initial states. The resultant controller is built on reference input and uncertainty estimation. The validity of the proposed method is verified through a circuit model.
International Journal of Robust and Nonlinear Control, EarlyView. Read More
