Continuous-time robust dynamic programming

Research output: Contribution to journalArticlepeer-review


This paper presents a new theory, known as robust dynamic programming, for a class of continuous-time dynamical systems. Diferent from traditional dynamic programming (DP) methods, this new theory serves as a fundamental tool to analyze the robustness of DP algorithms, and, in particular, to develop novel adaptive optimal control and reinforcement learning methods. In order to demonstrate the potential of this new framework, two illustrative applications in the felds of stochastic and decentralized optimal control are presented. Two numerical examples arising from both fnance and engineering industries are also given, along with several possible extensions of the proposed framework.

Original languageEnglish (US)
Pages (from-to)4150-4174
Number of pages25
JournalSIAM Journal on Control and Optimization
Issue number6
StatePublished - 2019


  • Adaptive optimal control
  • Dynamic programming
  • Robust control
  • Stochastic optimal control

ASJC Scopus subject areas

  • Control and Optimization
  • Applied Mathematics


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