Nonlinear Dynamic Surface Control of Chaos in Permanent Magnet Synchronous Motor Based on the Minimum Weights of RBF Neural Network
Author
Source
Issue
Vol. 2014, Issue 2014 (31 Dec. 2014), pp.1-9, 9 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2014-06-22
Country of Publication
Egypt
No. of Pages
9
Main Subjects
Abstract EN
This paper is concerned with the problem of the nonlinear dynamic surface control (DSC) of chaos based on the minimum weights of RBF neural network for the permanent magnet synchronous motor system (PMSM) wherein the unknown parameters, disturbances, and chaos are presented.
RBF neural network is used to approximate the nonlinearities and an adaptive law is employed to estimate unknown parameters.
Then, a simple and effective controller is designed by introducing dynamic surface control technique on the basis of first-order filters.
Asymptotically tracking stability in the sense of uniformly ultimate boundedness is achieved in a short time.
Finally, the performance of the proposed controller is testified through simulation results.
American Psychological Association (APA)
Luo, Shaohua. 2014. Nonlinear Dynamic Surface Control of Chaos in Permanent Magnet Synchronous Motor Based on the Minimum Weights of RBF Neural Network. Abstract and Applied Analysis،Vol. 2014, no. 2014, pp.1-9.
https://search.emarefa.net/detail/BIM-1014327
Modern Language Association (MLA)
Luo, Shaohua. Nonlinear Dynamic Surface Control of Chaos in Permanent Magnet Synchronous Motor Based on the Minimum Weights of RBF Neural Network. Abstract and Applied Analysis No. 2014 (2014), pp.1-9.
https://search.emarefa.net/detail/BIM-1014327
American Medical Association (AMA)
Luo, Shaohua. Nonlinear Dynamic Surface Control of Chaos in Permanent Magnet Synchronous Motor Based on the Minimum Weights of RBF Neural Network. Abstract and Applied Analysis. 2014. Vol. 2014, no. 2014, pp.1-9.
https://search.emarefa.net/detail/BIM-1014327
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1014327