Data-driven LQR for permanent magnet synchronous machines

Kanat Suleimenov, Ton Duc Do

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

Abstract

In this paper, application of data-driven Linear quadratic regulator (LQR) approach for a permanent magnet synchronous machines (PMSM) is introduced. The gain of feedback control is calculated using only input and output data of the PMSM. The estimation algorithm of system Markov parameters as well as an extended observability matrix is given. Simulation results of the proposed controller and model-based LQR are compared using Matlab/Simulink.

Original languageEnglish
Title of host publication2019 IEEE Vehicle Power and Propulsion Conference, VPPC 2019 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728112497
DOIs
Publication statusPublished - Oct 2019
Event2019 IEEE Vehicle Power and Propulsion Conference, VPPC 2019 - Hanoi, Viet Nam
Duration: Oct 14 2019Oct 17 2019

Publication series

Name2019 IEEE Vehicle Power and Propulsion Conference, VPPC 2019 - Proceedings

Conference

Conference2019 IEEE Vehicle Power and Propulsion Conference, VPPC 2019
CountryViet Nam
CityHanoi
Period10/14/1910/17/19

Keywords

  • Data-driven control
  • LQR
  • Markov parameters
  • Permanent magnet synchronous machines (PMSM)
  • Ricatti equation
  • Toeplitz matrix

ASJC Scopus subject areas

  • Control and Optimization
  • Modelling and Simulation
  • Artificial Intelligence
  • Transportation
  • Energy Engineering and Power Technology
  • Fuel Technology
  • Automotive Engineering
  • Electrical and Electronic Engineering

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