Iterative Learning Adaptive Control With Completely Unknown States

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.