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

Department of Electrical Engineering

Research output: Contribution to journal › Article

31 Citations (Scopus)

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.

Original language	English
Pages (from-to)	1138-1151
Number of pages	14
Journal	Neurocomputing
Volume	72
Issue number	4-6
DOIs	https://doi.org/10.1016/j.neucom.2008.02.013
Publication status	Published - 2009 Jan 1

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Keywords

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

ASJC Scopus subject areas

Computer Science Applications
Cognitive Neuroscience
Artificial Intelligence

Cite this

Lin, F. J., Chen, S. Y., Chou, P. H., & Shieh, P. H. (2009). Interval type-2 fuzzy neural network control for X-Y-Theta motion control stage using linear ultrasonic motors. Neurocomputing, 72(4-6), 1138-1151. https://doi.org/10.1016/j.neucom.2008.02.013

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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.",

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Access to Document

10.1016/j.neucom.2008.02.013