Neural network based self-tuning control of a switched reluctance motor drive to maximize torque per ampere Conference Paper

abstract

• On-line self-tuning control is essential to optimize the performance of a Switched Reluctance Motor (SRM) Drive in the presence of parameter variations. This paper introduces an advanced adaptive Neural Network (NN) based control to maximize torque per ampere in the low speed region. The proposed control technique utilizes a heuristic search method to find the change in the optimal excitation instances in case of parameter variations. Based on the results of this heuristic search, the NN employs an incremental learning to adapt its network weights. Computer simulations are performed to verify the applicability of the proposed algorithm. Experimental results are provided to demonstrate the working of the self-tuning controller.

name of conference

• IAS '97. Conference Record of the 1997 IEEE Industry Applications Conference Thirty-Second IAS Annual Meeting

authors

• Ehsani, Mehrdad

published proceedings

• IAS '97 - CONFERENCE RECORD OF THE 1997 IEEE INDUSTRY APPLICATIONS CONFERENCE / THIRTY-SECOND IAS ANNUAL MEETING, VOLS 1-3

author list (cited authors)

• Rajarathnam, A. V., Fahimi, B., & Ehsani, M.

citation count

• 11

complete list of authors

• Rajarathnam, AV||Fahimi, B||Ehsani, M

publication date

• January 1997

publisher

• Institute of Electrical and Electronics Engineers (IEEE)  Publisher

published in

• Conference Record of the IEEE Industry Applications Conference  Journal

keywords

• Bioengineering
• Clinical Research

Digital Object Identifier (DOI)

• 10.1109/ias.1997.643075

International Standard Book Number (ISBN) 10

• 0-7803-4067-1

start page

• 548

end page

• 555

volume

• 1

URL

• http%3A%2F%2Fdx.doi.org%2F10.1109%2Fias.1997.643075