The main objective of this study is to address the challenge of
simultaneously ensuring robustness and convergence performance in
model-free inversion-based iterative learning control. Initially, this
research provides a mathematical analysis of the sources of errors in
the iterative process, followed by proposing a design guideline to
enhance both convergence speed and the final value error. Based on the
design guideline, a gain design method associated with the number of
iterations is proposed, resulting in a novel model-free inversion-based
iterative learning control algorithm. Subsequently, a robustness
analysis of the proposed algorithm is conducted. Finally, a
comprehensive simulation and numerical comparison of the proposed
algorithm with existing similar algorithms are presented to demonstrate
the superior performance of the proposed control algorithm.