Fuzzy neural networks for identification and control of ultrasonic motor drive with LLCC resonant technique

Faa Jeng Lin, Rong Jong Wai, Rou Yong Duan

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22 Scopus citations

Abstract

This paper demonstrates the applications of fuzzy neural networks (FNN's) in the identification and control of the ultrasonic motor (USM). First, the USM is derived by a newly designed high-frequency two-phase voltage-source inverter using LLCC resonant technique. Then, two FNN's with varied learning rates are proposed to control the rotor position of the USM. The USM drive system is identified by a fuzzy neural network identifier (FNNI) to provide the sensitivity information of the drive system to a fuzzy neural network controller (FNNC). A backpropagation algorithm is used to train both the FNNI and FNNC on-line. Moreover, to guarantee the convergence of identification and tracking errors, analytical methods based on a discrete-type Lyapunov function are proposed to determine the varied learning rates of the FNN's. In addition, the effectiveness of the FNN-controlled USM drive system is demonstrated by experimental results. Accurate tracking response can be obtained due to the powerful on-line learning capability of the FNN's. Furthermore, the influence of parameter variations and external disturbances on the USM drive system can be reduced effectively.

Original languageEnglish
Pages (from-to)999-1011
Number of pages13
JournalIEEE Transactions on Industrial Electronics
Volume46
Issue number5
DOIs
StatePublished - 1999

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