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Variable-gain PD-type iterative learning control for a class of nonlinear time-varying systems |
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| Authors: | Xinxin Zhang Huafeng Ding Min Li Junxiang Li |
| Affiliation: | 1. School of Mechanical Engineering and Automation, Wuhan Textile University, Wuhan, P.R. China
Hubei Key Laboratory of Digital Textile Equipment, Wuhan Textile University, Wuhan, P.R. China Contribution: Investigation, Methodology, Validation, Writing - original draft;2. School of Mechanical Engineering and Electronic Information, China University of Geosciences, Wuhan, P.R. China Contribution: Funding acquisition, Writing - review & editing;3. School of Mechanical Engineering and Electronic Information, China University of Geosciences, Wuhan, P.R. China;4. School of Mechanical Engineering and Automation, Wuhan Textile University, Wuhan, P.R. China Hubei Key Laboratory of Digital Textile Equipment, Wuhan Textile University, Wuhan, P.R. China |
| Abstract: | This paper investigates variable-gain PD-type iterative learning control (ILC) for a class of nonlinear time-varying systems to well balance high-gain convergence rate and low-gain noise transmission. Different from the classic PD-type ILC, the control gains of the proposed method are variable. Each variable-gain consists of an amplitude-dependent term and an iteration-varying term. The amplitude-dependent terms vary with the amplitudes of tracking error and derivative of tracking error, and the iteration-varying terms are increasing along the iteration axis. The proposed ILC achieves a faster convergence rate than low-gain ILC and higher tracking accuracy with limited noise amplification than high-gain ILC. Moreover, the convergence condition of the proposed method in the presence of external noise is provided. Simulation and experimental results demonstrate the effectiveness of the proposed method. |
| Keywords: | iterative learning control (ILC) nonlinear time-varying system robotic manipulator variable-gain |