Robust adaptive dynamic programming and feedback stabilization of nonlinear systems

Research output: Contribution to journalArticle

Abstract

This paper studies the robust optimal control design for a class of uncertain nonlinear systems from a perspective of robust adaptive dynamic programming (RADP). The objective is to fill up a gap in the past literature of adaptive dynamic programming (ADP) where dynamic uncertainties or unmodeled dynamics are not addressed. A key strategy is to integrate tools from modern nonlinear control theory, such as the robust redesign and the backstepping techniques as well as the nonlinear small-gain theorem, with the theory of ADP. The proposed RADP methodology can be viewed as an extension of ADP to uncertain nonlinear systems. Practical learning algorithms are developed in this paper, and have been applied to the controller design problems for a jet engine and a one-machine power system.

Original languageEnglish (US)
Article number6701191
Pages (from-to)882-893
Number of pages12
JournalIEEE Transactions on Neural Networks and Learning Systems
Volume25
Issue number5
DOIs
StatePublished - 2014

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Dynamic programming
Nonlinear systems
Stabilization
Feedback
Jet engines
Backstepping
Control theory
Learning algorithms
Controllers

Keywords

  • Adaptive dynamic programming (ADP)
  • nonlinear uncertain systems
  • robust optimal control.

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Networks and Communications
  • Computer Science Applications
  • Software

Cite this

Robust adaptive dynamic programming and feedback stabilization of nonlinear systems. / Jiang, Yu; Jiang, Zhong-Ping.

In: IEEE Transactions on Neural Networks and Learning Systems, Vol. 25, No. 5, 6701191, 2014, p. 882-893.

Research output: Contribution to journalArticle

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