Event-Triggered Robust Adaptive Dynamic Programming With Output-Feedback for Large-Scale Systems

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

Research output: Contribution to journalArticlepeer-review

Abstract

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

Original languageEnglish (US)
Pages (from-to)1-12
Number of pages12
JournalIEEE Transactions on Control of Network Systems
DOIs
StateAccepted/In press - 2022

Keywords

  • Adaptive systems
  • Control systems
  • Dynamic programming
  • Event-triggered control
  • Large-scale systems
  • Optimal control
  • output-feedback
  • Power system dynamics
  • Power system stability
  • robust adaptive dynamic programming (RADP)
  • small-gain theory

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Signal Processing
  • Computer Networks and Communications
  • Control and Optimization

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