Robust adaptive dynamic programming with an application to power systems

Research output: Contribution to journalArticlepeer-review


This brief presents a novel framework of robust adaptive dynamic programming (robust-ADP) aimed at computing globally stabilizing and suboptimal control policies in the presence of dynamic uncertainties. A key strategy is to integrate ADP theory with techniques in modern nonlinear control with a unique objective of filling up a gap in the past literature of ADP without taking into account dynamic uncertainties. Neither the system dynamics nor the system order are required to be precisely known. As an illustrative example, the computational algorithm is applied to the controller design of a two-machine power system.

Original languageEnglish (US)
Article number6484168
Pages (from-to)1150-1156
Number of pages7
JournalIEEE transactions on neural networks and learning systems
Issue number7
StatePublished - 2013


  • Nonlinear uncertain systems
  • optimal control
  • reinforcement learning

ASJC Scopus subject areas

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


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