Adaptive Neural Predictive Control for Permanent Magnet Synchronous Motor Systems With Long Delay Time

Bing-Fei Wu, Chun Hsien Lin*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

11 Scopus citations

Abstract

Since the permanent magnet synchronous motor system in this research needs about 40 ms to finish a control cycle, such a long delay in time strongly causes the bad performance for the conventional controllers, especially for position control. To well control the speed and position, an adaptive neural predictive control is proposed. A two-layer recursive neural network is employed as a speed predictor, and an extended Kalman filter is utilized to tune the parameters of the predictor adaptively. Chaos optimization algorithm and Newton-Raphson optimization are combined to solve the problem of predictive control. As for the speed control, the proposed method shows better performance. The position control is designed based on the speed control. Due to the physical limitation of the plant, the steady state error is still large. Hence, a fuzzy compensator is applied. From the experiment, the error is reduced obviously.

Original languageEnglish
Article number8788667
Pages (from-to)108061-108069
Number of pages9
JournalIEEE Access
Volume7
DOIs
StatePublished - 6 Aug 2019

Keywords

  • extended Kalman filter
  • fuzzy rule
  • Model predictive control
  • neural network
  • non-linear optimization

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