Hardware-in-the-loop testing of current cycle feedback ILC for stabilisation and tracking control of under-actuated visual servo system

Jonnalagadda, Vimala Kumari; Elumalai, Vinodh Kumar

doi:10.1080/00207721.2020.1853273

Cited by 3 publications

(1 citation statement)

References 31 publications

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“…It improves the control effect by learning from previous experience. After nearly 40 years of development, ILC has obtained rich theoretical results (Amann et al, 1996; Freeman, 2017; Jonnalagadda and Elumalai, 2020; Volckaert et al, 2013; Xu and Li, 2018) and many successful applications (Li et al, 2016; Sampson et al, 2016; Won et al, 2017) in practice.…”

Section: Introductionmentioning

confidence: 99%

High-order model-free adaptive iterative learning control

Lin

Chi

et al. 2023

Transactions of the Institute of Measurement and Control

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In this paper, a high-order model-free adaptive iterative learning control (HOMFAILC) scheme is proposed for nonlinear non-affine discrete-time systems. The main feature of the proposed HOMFAILC is that not only the control input iterative learning law is designed with higher-order algorithms but also the parameter iterative adaptive updating law has the similar forms. Using additional control knowledge in both the iterative learning control law and the parameter estimation law, the control performance of the proposed HOMFAILC can be improved consequently. Furthermore, the similar structure in the control law and parameter updating law can also help to achieve a better control performance. The convergence is proved with rigorous mathematical analysis. Simulation study shows the effectiveness of the proposed HOMFAILC.

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