A SHRFNN Control for a Switched Reluctance Motor Drive

Abstract:

Due to unmodeled dynamic behavior and uncertainties exist in the applications of switched reluctance motor (SRM) drive which seriously affected the drive performance, a supervisoy hybrid recurrent fuzzy neural network (SHRFNN) speed control system that combined supervisor control, recurrent RFNN and compensated control is proposed to increase the robustness of the SRM drive system. First, the asymmetrical structure of the power converter is applied to SRM drive. In order to process uncertainties, a SHRFNN control system is proposed to control SRM drive system. With proposed SHRFNN control system, the SRM drive possesses the advantages of good transient control performance and robustness to unmodeled dynamic behavior and uncertainties for speed control. The effectiveness of the proposed control scheme is verified by experimental results.

• Periodical: Advanced Materials Research (Volumes 482-484)
• Pages: 245-251
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.482-484.245
• Online since: February 2012
• Authors: Chih Hong Lin, Ming Kuan Lin, Chih Peng Lin
• Keywords: Recurrent Fuzzy Neural Network, Supervisor Control, Switched Reluctance Motor (SRM)

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