Robust Adaptive Dynamic Programming

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

In this chapter, we propose a framework of robust adaptive dynamic programming (for short, robust-ADP), which is aimed at computing globally asymptotically stabilizing control laws with robustness to dynamic uncertainties, via off-line/on-line learning. It is shown that robust optimal control problems can be solved for higherdimensional, partially linear composite systems by integration of ADP and modern nonlinear control design tools such as backstepping and ISS small-gain methods. Finally, the robust-ADP framework is applied to the load-frequency control for a power system and the controller design for a machine tool power drive system.

Original languageEnglish (US)
Title of host publicationReinforcement Learning and Approximate Dynamic Programming for Feedback Control
PublisherJohn Wiley and Sons
Pages281-302
Number of pages22
ISBN (Print)9781118104200
DOIs
StatePublished - Feb 7 2013

Keywords

  • Asymptotic, stabilizing control laws/uncertainties
  • Optimality, robust-ADP for partial-state
  • Robust ADP, robust-ADP
  • Robust-ADP for disturbance attenuation
  • Robust-ADP, via off-line/on-line learning

ASJC Scopus subject areas

  • Engineering(all)

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  • Cite this

    Jiang, Y., & Jiang, Z. P. (2013). Robust Adaptive Dynamic Programming. In Reinforcement Learning and Approximate Dynamic Programming for Feedback Control (pp. 281-302). John Wiley and Sons. https://doi.org/10.1002/9781118453988.ch13