Model‐free inversion‐based iterative learning control algorithm with adaptive gain: Achieving superior robustness and convergence

Abstract

AbstractThe main objective of this work 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 gain design guideline to enhance both convergence speed and the final value error. Based on the gain 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 MFIIC‐like algorithms are presented to demonstrate the superior performance of the proposed control algorithm.