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AFD-based ILC designs in frequency domain for linear discrete-time systems by Fu, Wen-Yuan; Li, Xiao-Dong; Qian, Tao is a Engineering article available to read on EtoBox.

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The existing frequency-domain-based iterative learning control (ILC) methods are highly dependent on the mathematical models of the controlled systems. For linear discrete-time single-input singleoutput (SISO) systems with unknown mathematical models, this paper tries to present fully datadriven ILC designs in frequency domain. With the help of support vector machine (SVM), the inputoutput data of the linear discrete-time SISO system at the first repetition is utilised to constitute an adaptive Fourier decomposition (AFD) model. Then, based on the AFD model, a P-type ILC law and an extended D-type ILC law with data-driven determining techniques for learning gains are presented. It is noted that comparing with the conventional D-type ILC law, the newly proposed extended D-type ILC law exhibits superior tracking characteristic due to involving the frequency information during the ILC process. A numerical example is utilised to illustrate the effectiveness of the proposed ILC algorithms with the data-driven determining techniques for learning gains.

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Author

Fu, Wen-Yuan; Li, Xiao-Dong; Qian, Tao

Publisher

Taylor and Francis Group; Informa UK (Taylor & Francis); Taylor & Francis; Informa UK Limited (ISSN 0020-7721)

Published

2020

Language

EN

Field

Engineering (Physical Sciences)