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A Novel Hybrid Recurrent Wavelet Neural Network Control for a PMSM Driven Electric Scooter

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Abstract:

The electric scooter with nonlinear friction force of the transmission belt made the hybrid recurrent neural network (HRNN) control system with degenerated tracking responses. In order to overcome this problem, a hybrid recurrent wavelet neural network (HRWNN) control system is proposed to control for a permanent magnet synchronous motor (PMSM) driven electric scooter. The HRWNN control system consists of a supervisor control, a RWNN and a compensated control with adaptive law. The on-line parameter training methodology of the RWNN can be derived using adaptation laws and the Lyapunov stability theorem. The RWNN has the on-line learning ability to respond to the system’s nonlinear and time-varying behaviors. To show the effectiveness of the proposed controller, comparative studies with HRNN control system is demonstrated by experimental results.

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Periodical:

Applied Mechanics and Materials (Volumes 284-287)

Pages:

2194-2198

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.284-287.2194

Citation:

Cite this paper

Online since:

January 2013

Authors:

Chih Hong Lin, Chih Peng Lin

Keywords:

Permanent Magnet Synchronous Motor, Recurrent Neural Network, Recurrent Wavelet Neural Network (RWNN)

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© 2013 Trans Tech Publications Ltd. All Rights Reserved

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