Field-programmable gate array-based recurrent wavelet neural network control system for linear ultrasonic motor

F. J. Lin, S. Y. Chen, Y. C. Hung

Research output: Contribution to journalArticle

7 Citations (Scopus)

Abstract

A field-programmable gate array (FPGA)-based recurrent wavelet neural network (RWNN) control system is proposed to control the mover position of a linear ultrasonic motor (LUSM). First, the structure and operating principles of the LUSM are introduced. Since the dynamic characteristics and motor parameters of the LUSM are non-linear and time-varying, an RWNN controller is designed to improve the control performance for the precision tracking of various reference trajectories. The network structure and its on-line learning algorithm using delta adaptation law of the RWNN are described in detail. Moreover, the connective weights, translations and dilations of the RWNN are trained on-line. Furthermore, to guarantee the convergence of the tracking error, analytical methods based on a discrete-type Lyapunov function are proposed to determine the varied learning rates of the RWNN. In addition, an FPGA chip is adopted to implement the developed control algorithm for possible low-cost and high-performance industrial applications. Finally, the effectiveness of the proposed control system is verified by some experimental results.

Original languageEnglish
Pages (from-to)298-312
Number of pages15
JournalIET Electric Power Applications
Volume3
Issue number4
DOIs
Publication statusPublished - 2009 Jul 14

Fingerprint

Field programmable gate arrays (FPGA)
Ultrasonics
Neural networks
Control systems
Lyapunov functions
Learning algorithms
Industrial applications
Trajectories
Controllers
Costs

ASJC Scopus subject areas

  • Electrical and Electronic Engineering

Cite this

Field-programmable gate array-based recurrent wavelet neural network control system for linear ultrasonic motor. / Lin, F. J.; Chen, S. Y.; Hung, Y. C.

In: IET Electric Power Applications, Vol. 3, No. 4, 14.07.2009, p. 298-312.

Research output: Contribution to journalArticle

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