Policy iteration and event-triggered robust adaptive dynamic programming for large-scale systems

Fuyu Zhao, Weinan Gao, Tengfei Liu, Zhong Ping Jiang

Research output: Contribution to journalConference articlepeer-review


In this paper, an event-triggered robust optimal control approach is proposed for large-scale systems with both parametric and dynamic uncertainties through robust adaptive dynamic programming, policy iteration, and small-gain techniques. By using the input and output data, the unmeasurable states are reconstructed instead of constructing a Luenberger observer. Starting from an admissible control policy, an event-based feedback control policy is learned to save the communication resources and reduce the number of control updates. The closed-loop stability and the convergence of the proposed algorithm are analyzed by using Lyapunov and small-gain techniques. A practical example of multimachine power systems with governor controllers is given to demonstrate the effectiveness of the proposed method.

Original languageEnglish (US)
Pages (from-to)376-381
Number of pages6
Issue number14
StatePublished - 2021
Event3rd IFAC Conference on Modelling, Identification and Control of Nonlinear Systems MICNON 2021 - Tokyo, Japan
Duration: Sep 15 2021Sep 17 2021


  • Event-triggered control
  • Large-scale system
  • Output-feedback
  • Robust adaptive dynamic programming (RADP)
  • Small-gain theory

ASJC Scopus subject areas

  • Control and Systems Engineering


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