ABSTRACT Most existing adaptive iterative learning control (AILC) schemes, while effective for state tracking for repetitive systems, rely on full state measurement. Although observer‐based AILC methods can achieve output tracking, they are predominantly hampered by observers with asymptotic convergence, which conflict with the finite‐time operation of AILC. To overcome the limitations, this article proposes a novel finite‐time observer‐based AILC scheme for a class of nonlinear high‐order fully actuated (HOFA) systems with input disturbances. By leveraging the HOFA system framework, the proposed approach first converts the original nonlinear HOFA system into a linear closed‐loop system, which facilitates the construction of a linear finite‐time observer and furthermore develops a corresponding AILC law. Rigorous convergence analysis of the constructed observer and the proposed AILC algorithm is conducted. The effectiveness of the proposed AILC method is validated through two illustrative examples and comparative studies with existing algorithms.
Wang et al. (Sun,) studied this question.