Learning-based Event-triggered Adaptive Optimal Output Regulation of Linear Discrete-time Systems

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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In this paper, a data-driven event-triggered output-feedback control approach is proposed to solve the problem of adaptive optimal output regulation for uncertain discrete-time linear systems when only the output information is available. A crucial strategy is to develop a novel co-design scheme for the event-triggering mechanism and the data-driven optimal controller. Theoretical analysis and an application to a LCL coupled inverter-based distributed generation system demonstrate the effectiveness of the proposed learning-based, event-triggered, adaptive optimal controller design with output-feedback.

Original languageEnglish (US)
Title of host publicationProceedings of 2021 IEEE 10th Data Driven Control and Learning Systems Conference, DDCLS 2021
EditorsMingxuan Sun, Huaguang Zhang
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1516-1521
Number of pages6
ISBN (Electronic)9781665424233
DOIs
StatePublished - May 14 2021
Event10th IEEE Data Driven Control and Learning Systems Conference, DDCLS 2021 - Suzhou, China
Duration: May 14 2021May 16 2021

Publication series

NameProceedings of 2021 IEEE 10th Data Driven Control and Learning Systems Conference, DDCLS 2021

Conference

Conference10th IEEE Data Driven Control and Learning Systems Conference, DDCLS 2021
Country/TerritoryChina
CitySuzhou
Period5/14/215/16/21

Keywords

  • Adaptive Dynamic Programming
  • Event-Triggered Control
  • Output Regulation

ASJC Scopus subject areas

  • Control and Optimization
  • Artificial Intelligence
  • Signal Processing
  • Safety, Risk, Reliability and Quality

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