Observer based repetitive learning control for a class of nonlinear systems with non-parametric uncertainties

Deqing Huang, Jian Xin Xu, Shiping Yang, Xu Jin

Research output: Contribution to journalArticlepeer-review

33 Scopus citations


In this paper, a repetitive learning control (RLC) scheme is developed for a class of nonlinear systems to handle an output tracking problem, where two state observers are introduced concurrently to estimate the unavailable control system and reference states information. The estimation of reference state information is because of the lack of reference internal model in the RLC design. By virtue of the periodicity of reference signals and the associated learning capability in control mechanism, the involved unstructured nonlinear uncertainties can be handled. The Lyapunov-like energy function method is adopted to facilitate the learning control design as well as property analysis thus achieve the asymptotical convergence of errors in state observation and output tracking simultaneously. Moreover, owing to the robustification of the learning controller that is addressed by incorporating projection, the proposed control scheme would be applicable in practice. In the end, an illustrative example is simulated to demonstrate the efficacy of the proposed RLC law.

Original languageEnglish
Pages (from-to)1214-1229
Number of pages16
JournalInternational Journal of Robust and Nonlinear Control
Issue number8
StatePublished - May 25 2015

Bibliographical note

Publisher Copyright:
© 2014 John Wiley & Sons, Ltd.


  • observer-based design
  • output tracking
  • repetitive learning control
  • robustification
  • unstructured nonlinear uncertainty

ASJC Scopus subject areas

  • Control and Systems Engineering
  • General Chemical Engineering
  • Biomedical Engineering
  • Aerospace Engineering
  • Mechanical Engineering
  • Industrial and Manufacturing Engineering
  • Electrical and Electronic Engineering


Dive into the research topics of 'Observer based repetitive learning control for a class of nonlinear systems with non-parametric uncertainties'. Together they form a unique fingerprint.

Cite this