Chaotic Newton-raphson optimization based predictive control for permanent magnet synchronous motor systems with long-delay

Bing-Fei Wu*, Chun Hsien Lin

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Scopus citations

Abstract

A Tent-map chaotic Newton-Raphson optimization based neural network predictive control (TCNR-NPC) is developed to apply to the long-delay permanent magnet synchronous motor (PMSM) system in this paper. Due to a nonlinear model utilized in the predictive controller, nonlinear optimization methods turn into an important issue. To overcome the shortcoming of the conventional nonlinear programming on the initial condition sensitivity and maintain the accuracy of optimal solution, chaos optimization algorithm (COA) and Newton-Raphson (NR) are combined. With the comparison of COA and NR based optimization methods, our approach, the Tent-map chaotic Newton-Raphson (TCNR) optimization, is easier to reach the global optimum, thus, it would be employed in neural network predictive control. It is found that TCNR-NPC has a better performance than those of GPC, modified GPC, adaptive extended PSO based NPC, and PSO based PI controllers in real experiments.

Original languageEnglish
Title of host publicationProceedings - 2015 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2015
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages382-387
Number of pages6
ISBN (Electronic)9781479986965
DOIs
StatePublished - 12 Jan 2016
EventIEEE International Conference on Systems, Man, and Cybernetics, SMC 2015 - Kowloon Tong, Hong Kong
Duration: 9 Oct 201512 Oct 2015

Publication series

NameProceedings - 2015 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2015

Conference

ConferenceIEEE International Conference on Systems, Man, and Cybernetics, SMC 2015
CountryHong Kong
CityKowloon Tong
Period9/10/1512/10/15

Keywords

  • chaos optimization algorithm
  • long-delay plant
  • neural network
  • Newton-Raphson
  • predictive control

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