Hybrid control using recurrent fuzzy neural network for linear-induction motor servo drive

Faa Jeng Lin, Rong Jong Wai

研究成果: 雜誌貢獻期刊論文同行評審

98 引文 斯高帕斯(Scopus)

摘要

In this paper, a hybrid control system using a recurrent-fuzzy-neural network (RFNN) is proposed to control a linear-induction motor (LIM) servo drive. First, the feedback linearization theory is used to decouple the thrust force and the flux amplitude of the LIM. Then, a hybrid control system is proposed to control the mover of the LIM for periodic motion. In the hybrid control system, the RFNN controller is the main tracking controller, which is used to mimic a perfect control law, and the compensated controller is proposed to compensate the difference between the perfect control law and the RFNN controller. Moreover, an on-line parameter training methodology, which is derived using the Lyapunov stability theorem and the gradient descent method, is proposed to increase the learning capability of the RFNN. The effectiveness of the proposed control scheme is verified by both the simulated and experimental results. Furthermore, the advantages of the proposed control system are indicated in comparison with the sliding mode control system.

原文???core.languages.en_GB???
頁(從 - 到)102-115
頁數14
期刊IEEE Transactions on Fuzzy Systems
9
發行號1
DOIs
出版狀態已出版 - 2月 2001

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