NDLI: Gaussian radial basis function neural network controller of a synchronous reluctance motor in electric motorcycle applications

Content Provider	Springer Nature Link
Author	Chen, Chien An Chiang, Huann Keng Lin, Wen Bin
Copyright Year	2010
Abstract	In this article, a sliding mode control (SMC) design based on a Gaussian radial basis function neural network (GRBFNN) is proposed for the synchronous reluctance motor (SynRM) system in electrical motorcycle applications. The conventional SMC assumes that the upper lumped boundaries of parameter variations and external disturbances are known, and the sign function is used. This causes high-frequency chattering and the high-gain phenomenon. In order to avoid these drawbacks, the proposed method utilizes the Lyapunov stability method and the steep descent rule to guarantee the convergence asymptotically, and reduce the magnitude of the chattering or avoid it completely. Finally, numerical simulations are shown to illustrate the good performance of our controller design.
Starting Page	468
Ending Page	472
Page Count	5
File Format	PDF
ISSN	14335298
Journal	Artificial Life and Robotics
Volume Number	15
Issue Number	4
e-ISSN	16147456
Language	English
Publisher	Springer Japan
Publisher Date	2010-12-31
Publisher Place	Japan
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Sliding mode control Radial basis function neural network Synchronous reluctance motor Electric motorcycle Lyapunov stability method Control, Robotics, Mechatronics Computation by Abstract Devices Artificial Intelligence (incl. Robotics)
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence Biochemistry, Genetics and Molecular Biology

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