Adaptive Optimal Output Regulation of Time-Delay Systems via Measurement Feedback

Weinan Gao, Zhong Ping Jiang

Research output: Contribution to journalArticlepeer-review

Abstract

This brief proposes a novel solution to problems related to the measurement feedback adaptive optimal output regulation of discrete-time linear systems with input time-delay. Based on reinforcement learning and adaptive dynamic programming, an approximate optimal control policy is obtained via recursive numerical algorithms using online information. Convergence proofs for the proposed algorithms are given. Notably, the exact knowledge of the plant and the exosystem is not needed. The learned control policy is only a function of retrospective input and measurement output data. Theoretical analysis and an application to a grid-connected inverter show that the proposed methodologies serve as effective tools for solving adaptive and optimal output regulation problems.

Original languageEnglish (US)
Article number8418850
Pages (from-to)938-945
Number of pages8
JournalIEEE transactions on neural networks and learning systems
Volume30
Issue number3
DOIs
StatePublished - Mar 2019

Keywords

  • Measurement feedback control
  • optimal control
  • output regulation
  • reinforcement learning
  • time-delay systems

ASJC Scopus subject areas

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

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