A high performance tracker for the sampled-data system based on iterative learning control

Fu Ming Chen, Jason Sheng-Hon Tsai, Fu-Zen Shaw, Tzong Jiy Tsai, Ming Hong Lin, Jun Yen Lin

Research output: Contribution to journalArticlepeer-review

Abstract

A high performance tracker for the unknown stochastic sampled-data system based on iterative learning control (ILC) to be tracked is proposed in this paper. The newly developed ILC algorithm and the high-gain property tracker are first combined to speed up the convergence rate. The Euler method based difference type (D-type) ILC algorithm is proposed to combine with the prediction-based digital redesign method to construct the high performance modified D-type ILC for the model-based sampled-data systems. Finally, some examples are given for illustrating the effectiveness of the newly proposed method.

Original languageEnglish
Pages (from-to)29-39
Number of pages11
JournalInternational Journal of Control Theory and Applications
Volume5
Issue number1
Publication statusPublished - 2012 Dec 1

All Science Journal Classification (ASJC) codes

  • Computer Science(all)

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