Perturbation wavelet neural sliding mode position control for a voice coil motor driver

淡江大學機構典藏 > 工學院 > 電機工程學系暨研究所 > 期刊論文 > Item 987654321/118964

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题名:	Perturbation wavelet neural sliding mode position control for a voice coil motor driver
作者:	Hsu, Chun-fei
关键词:	VCM driver;Wavelet neural network;Perturbed wavelet function;Structure learning;Parameter learning
日期:	2018-03-12
上传时间:	2020-07-22 12:10:22 (UTC+8)
出版者:	Springer U K
摘要:	To cope with the nonlinear electro-magneto-mechanical characteristics, this paper proposes a perturbation wavelet neural sliding mode position control (PWSPC) system for a voice coil motor (VCM) driver. A perturbed wavelet neural network (PWNN) approximator is used to online approximate the unknown nonlinear term in the VCM system dynamics. The PWNN approximator uses perturbed wavelet functions to handle the rules uncertainties like interval type-2 fuzzy sets. The structure learning ability enables the PWNN approximator to evolve its structure online. Further, the parameter learning laws and stability analysis are derived in the sense of Lyapunov function; thus, the parameter convergence and system stability can be guaranteed. Finally, the experimental results verify that the proposed PWSPC system can achieve favorable control performance such as good disturbance rejection and good tracking accuracy.
關聯:	Neural Computing and Applications 31,　p.5975-5988
DOI:	10.1007/s00521-018-3413-5
显示于类别:	[電機工程學系暨研究所] 期刊論文

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