Interval type-2 fuzzy neural network control for X-Y-Theta motion control stage using linear ultrasonic motors

Faa Jeng Lin; Syuan Yi Chen; Po Huan Chou; Po Huang Shieh

doi:10.1016/j.neucom.2008.02.013

Interval type-2 fuzzy neural network control for X-Y-Theta motion control stage using linear ultrasonic motors

Faa Jeng Lin^*
, Syuan Yi Chen
, Po Huan Chou
, Po Huang Shieh

^*此作品的通信作者

研究成果: 雜誌貢獻 › 期刊論文 › 同行評審

36 引文斯高帕斯（Scopus）

摘要

An interval type-2 fuzzy neural network (IT2FNN) control system is proposed to control the position of an X-Y-Theta (X-Y-θ) motion control stage using linear ultrasonic motors (LUSMs) to track various contours. The IT2FNN, which combines the merits of interval type-2 fuzzy logic system (FLS) and neural network, is developed to simplify the computation and to confront the uncertainties of the X-Y-θ motion control stage. Moreover, the parameter learning of the IT2FNN based on the supervised gradient descent method is performed on line. The experimental results show that the tracking performance of the IT2FNN is significantly improved compared to type-1 FNN.

原文	英語
頁（從 - 到）	1138-1151
頁數	14
期刊	Neurocomputing
卷	72
發行號	4-6
DOIs	https://doi.org/10.1016/j.neucom.2008.02.013
出版狀態	已發佈 - 2009 1月
對外發佈	是

ASJC Scopus subject areas

電腦科學應用
認知神經科學
人工智慧

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10.1016/j.neucom.2008.02.013

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@article{70e0c0441f7e4f9793afb0a3011bac58,

title = "Interval type-2 fuzzy neural network control for X-Y-Theta motion control stage using linear ultrasonic motors",

abstract = "An interval type-2 fuzzy neural network (IT2FNN) control system is proposed to control the position of an X-Y-Theta (X-Y-θ) motion control stage using linear ultrasonic motors (LUSMs) to track various contours. The IT2FNN, which combines the merits of interval type-2 fuzzy logic system (FLS) and neural network, is developed to simplify the computation and to confront the uncertainties of the X-Y-θ motion control stage. Moreover, the parameter learning of the IT2FNN based on the supervised gradient descent method is performed on line. The experimental results show that the tracking performance of the IT2FNN is significantly improved compared to type-1 FNN.",

keywords = "Gradient descent method, Linear ultrasonic motors, Type-2 fuzzy logic system, Type-2 fuzzy neural network, X-Y-Theta motion control",

author = "Lin, \{Faa Jeng\} and Chen, \{Syuan Yi\} and Chou, \{Po Huan\} and Shieh, \{Po Huang\}",

note = "Funding Information: The author would like to acknowledge the financial support of the National Science Council of Taiwan, ROC, through its Grant NSC 95-2221-E-259-042-MY3. ",

year = "2009",

month = jan,

doi = "10.1016/j.neucom.2008.02.013",

language = "English",

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TY - JOUR

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AU - Lin, Faa Jeng

AU - Chen, Syuan Yi

AU - Chou, Po Huan

AU - Shieh, Po Huang

N1 - Funding Information: The author would like to acknowledge the financial support of the National Science Council of Taiwan, ROC, through its Grant NSC 95-2221-E-259-042-MY3.

PY - 2009/1

Y1 - 2009/1

N2 - An interval type-2 fuzzy neural network (IT2FNN) control system is proposed to control the position of an X-Y-Theta (X-Y-θ) motion control stage using linear ultrasonic motors (LUSMs) to track various contours. The IT2FNN, which combines the merits of interval type-2 fuzzy logic system (FLS) and neural network, is developed to simplify the computation and to confront the uncertainties of the X-Y-θ motion control stage. Moreover, the parameter learning of the IT2FNN based on the supervised gradient descent method is performed on line. The experimental results show that the tracking performance of the IT2FNN is significantly improved compared to type-1 FNN.

AB - An interval type-2 fuzzy neural network (IT2FNN) control system is proposed to control the position of an X-Y-Theta (X-Y-θ) motion control stage using linear ultrasonic motors (LUSMs) to track various contours. The IT2FNN, which combines the merits of interval type-2 fuzzy logic system (FLS) and neural network, is developed to simplify the computation and to confront the uncertainties of the X-Y-θ motion control stage. Moreover, the parameter learning of the IT2FNN based on the supervised gradient descent method is performed on line. The experimental results show that the tracking performance of the IT2FNN is significantly improved compared to type-1 FNN.

KW - Gradient descent method

KW - Linear ultrasonic motors

KW - Type-2 fuzzy logic system

KW - Type-2 fuzzy neural network

KW - X-Y-Theta motion control

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Interval type-2 fuzzy neural network control for X-Y-Theta motion control stage using linear ultrasonic motors

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ASJC Scopus subject areas

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