Robust Policy Iteration for Continuous-Time Linear Quadratic Regulation

Bo Pang, Tao Bian, Zhong Ping Jiang

Research output: Contribution to journalArticlepeer-review


This article studies the robustness of policy iteration in the context of continuous-time infinite-horizon linear quadratic regulator (LQR) problem. It is shown that Kleinman's policy iteration algorithm is small-disturbance input-to-state stable, a property that is stronger than Sontag's local input-to-state stability but weaker than global input-to-state stability. More precisely, whenever the error in each iteration is bounded and small, the solutions of the policy iteration algorithm are also bounded and enter a small neighborhood of the optimal solution of the LQR problem. Based on this result, an off-policy data-driven policy iteration algorithm for the LQR problem is shown to be robust when the system dynamics are subject to small additive unknown bounded disturbances. The theoretical results are validated by a numerical example.

Original languageEnglish (US)
Pages (from-to)504-511
Number of pages8
JournalIEEE Transactions on Automatic Control
Issue number1
StatePublished - Jan 1 2022


  • Adaptive dynamic programming, adaptive optimal control, data-driven control, policy iteration, reinforcement learning, robustness

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Computer Science Applications
  • Electrical and Electronic Engineering


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