Model-free inversion-based iterative learning control algorithm with
adaptive gain: achieving better robustness and convergence
- Zhicheng Kou,
- Jinggao Sun
Zhicheng Kou
East China University of Science and Technology
Author ProfileJinggao Sun
East China University of Science and Technology
Corresponding Author:jgsun@ecust.edu.cn
Author ProfileAbstract
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.01 Mar 2023Submitted to Optimal Control, Applications and Methods 01 Mar 2023Submission Checks Completed
01 Mar 2023Assigned to Editor
01 Mar 2023Review(s) Completed, Editorial Evaluation Pending
21 Jun 2023Reviewer(s) Assigned
18 Aug 2023Editorial Decision: Revise Minor
23 Aug 20231st Revision Received
28 Aug 2023Submission Checks Completed
28 Aug 2023Assigned to Editor
28 Aug 2023Review(s) Completed, Editorial Evaluation Pending
12 Feb 2024Editorial Decision: Revise Minor
21 Feb 2024Review(s) Completed, Editorial Evaluation Pending