Recurrent wavelet-based Elman neural network control for multi-axis motion control stage using linear ultrasonic motors

F. J. Lin*, Y. S. Kung, Syuan-Yi Chen, Y. H. Liu

*此作品的通信作者

研究成果: 雜誌貢獻期刊論文同行評審

34 引文 斯高帕斯(Scopus)

摘要

A novel recurrent wavelet-based Elman neural network (RWENN) control system is proposed in this study to control the mover position of a multi-axis motion control stage using linear ultrasonic motors (LUSMs) for the tracking of various contours. First, the structure and operating principles of the LUSMs are introduced briefly. Since the dynamic characteristics and motor parameters of the LUSMs are non-linear and time varying, the RWENN is proposed to control the mover of the X-Y-θ motion control stage to track various contours precisely using a direct decentralised control strategy. In the proposed RWENN, each hidden neuron employs a different wavelet function as an activation function. Moreover, the recurrent connective weights are added in the RWENN. Therefore compared with the conventional Elman neural network (ENN), both the precision and time of convergence are improved. Furthermore, the on-line learning algorithm based on the supervised gradient descent method and the convergence analysis of the tracking error using a discrete-type Lyapunov function of the RWENN are developed. Finally, some experimental results of various contours tracking show that the tracking performance of the RWENN is significantly improved compared with the ENN.

原文英語
文章編號IEPAAN000004000005000314000001
頁(從 - 到)314-332
頁數19
期刊IET Electric Power Applications
4
發行號5
DOIs
出版狀態已發佈 - 2010 五月 1

ASJC Scopus subject areas

  • 電氣與電子工程

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