Iterative learning-based decentralized adaptive tracker for large-scale systems: A digital redesign approach

Jason Sheng Hong Tsai, Yan Yi Du, Pei Hsiang Huang, Shu Mei Guo, Leang San Shieh, Yuhua Chen

Research output: Contribution to journalArticlepeer-review

9 Citations (Scopus)


In this paper, a digital redesign methodology of the iterative learning-based decentralized adaptive tracker is proposed to improve the dynamic performance of sampled-data linear large-scale control systems consisting of N interconnected multi-input multi-output subsystems, so that the system output will follow any trajectory which may not be presented by the analytic reference model initially. To overcome the interference of each sub-system and simplify the controller design, the proposed model reference decentralized adaptive control scheme constructs a decoupled well-designed reference model first. Then, according to the well-designed model, this paper develops a digital decentralized adaptive tracker based on the optimal analog control and prediction-based digital redesign technique for the sampled-data large-scale coupling system. In order to enhance the tracking performance of the digital tracker at specified sampling instants, we apply the iterative learning control (ILC) to train the control input via continual learning. As a result, the proposed iterative learning-based decentralized adaptive tracker not only has robust closed-loop decoupled property but also possesses good tracking performance at both transient and steady state. Besides, evolutionary programming is applied to search for a good learning gain to speed up the learning process of ILC.

Original languageEnglish
Pages (from-to)344-356
Number of pages13
JournalISA Transactions
Issue number3
Publication statusPublished - 2011 Jul

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Instrumentation
  • Computer Science Applications
  • Electrical and Electronic Engineering
  • Applied Mathematics


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