Field-programmable gate array-based intelligent dynamic sliding-mode control using recurrent wavelet neural network for linear ultrasonic motor

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

*此作品的通信作者

研究成果: 雜誌貢獻期刊論文同行評審

24 引文 斯高帕斯(Scopus)

摘要

A field-programmable gate array (FPGA)-based intelligent dynamic sliding-mode control (IDSMC) using recurrent wavelet neural network (RWNN) estimator is proposed to control the mover position of a linear ultrasonic motor (LUSM) in this study. First, the structure and operating principles of the LUSM are introduced briefly. Then, the dynamics of LUSM mechanism with the introduction of a lumped uncertainty, which include the friction force, is derived. Since the dynamic characteristics and motor parameters of the LUSM are non-linear and time-varying, an IDSMC using RWNN estimator is designed to achieve robust control performance of the LUSM drive system. The RWNN estimator is employed to estimate the non-linear functions including the system parameters and external disturbance. Moreover, the adaptive learning algorithm trained the parameters of the RWNN online is derived using the Lyapunov stability theorem. Furthermore, an FPGA chip is adopted to implement the developed control and on-line learning algorithms for possible low-cost and high-performance industrial applications. The experimental results show that excellent positioning and tracking performance are achieved. In addition, the robustness to parameter variations and friction force can be obtained as well using the proposed control system.

原文英語
頁(從 - 到)1511-1532
頁數22
期刊IET Control Theory and Applications
4
發行號9
DOIs
出版狀態已發佈 - 2010 9月
對外發佈

ASJC Scopus subject areas

  • 控制與系統工程
  • 人機介面
  • 電腦科學應用
  • 控制和優化
  • 電氣與電子工程

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