Robust Adaptive Dynamic Programming and Feedback Stabilization of Nonlinear Systems

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 dy...

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Veröffentlicht in:IEEE transaction on neural networks and learning systems 2014-05, Vol.25 (5), p.882-893
Hauptverfasser: Jiang, Yu, Jiang, Zhong-Ping
Format: Artikel
Sprache:eng
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Zusammenfassung: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.
ISSN:2162-237X
2162-2388
DOI:10.1109/TNNLS.2013.2294968