Design of arbitrary-order robust iterative learning control based on robust control theory

Minghui Zheng, Cong Wang, Liting Sun, Masayoshi Tomizuka

Research output: Contribution to journalArticlepeer-review

24 Scopus citations


Iterative learning control (ILC) is an effective technique that improves the tracking performance of systems by adjusting the feedforward control signal based on the memory data. The key in ILC is to design learning filters with guaranteed convergence and robustness, which usually involves lots of tuning effort especially in high-order ILC. To facilitate this procedure, this paper proposes a systematics approach to design learning filters for arbitrary-order ILC with guaranteed convergence, robustness and ease of tuning. The filter design problem is transformed into an H optimal control problem for a constructed feedback system. This approach is based on an infinite impulse response (IIR) system and conducted directly in iteration-frequency domain. The proposed algorithm is further advanced to the one that explicitly considers system variations based on μ synthesis. Important characteristics of the proposed approach such as convergence and robustness are explored and demonstrated through both simulations and experiments on a wafer scanning system.

Original languageEnglish (US)
Pages (from-to)67-76
Number of pages10
StatePublished - Nov 2017

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Mechanical Engineering
  • Computer Science Applications
  • Electrical and Electronic Engineering


  • High-precision control
  • Iterative learning control
  • Robust control


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