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\begin{document}

\title{Model-free inversion-based iterative learning control algorithm with
adaptive gain: achieving better robustness and convergence}



\author[1]{Zhicheng Kou}%
\author[1]{Jinggao Sun}%
\affil[1]{East China University of Science and Technology}%


\vspace{-1em}



  
  \date{June 13, 2024}


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\begin{abstract}
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.%
\end{abstract}%



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