Robust adaptive dynamic programming for nonlinear control design

Research output: Contribution to journalConference articlepeer-review


This paper presents a robust optimal controller design for unknown nonlinear systems from a perspective of robust adaptive dynamic programming (robust-ADP). The proposed methodology has several novel features. First, the class of nonlinear systems studied in the paper allows for the presence of dynamic uncertainties with unmeasured state and uncertain system order/dynamics. Second, in the absence of the dynamic uncertainty, the online policy iteration technique developed in this paper can be viewed as an extension of the existing ADP method to affine continuous-time nonlinear systems with completely unknown dynamics. Third, the theory of approximate/adaptive dynamic programming (ADP) is integrated for the first time with tools from modern nonlinear control theory, such as the nonlinear small-gain theorem, for robust optimal control design. It is shown that, with appropriate robust redesign, the robust-ADP controller asymptotically stabilizes the overall system. A practical robust-ADP-based online learning algorithm is developed in this paper, and is applied to the robust optimal controller design for a two-machine power system.

Original languageEnglish (US)
Article number6426987
Pages (from-to)1896-1901
Number of pages6
JournalProceedings of the IEEE Conference on Decision and Control
StatePublished - 2012
Event51st IEEE Conference on Decision and Control, CDC 2012 - Maui, HI, United States
Duration: Dec 10 2012Dec 13 2012

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Modeling and Simulation
  • Control and Optimization


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