Scheduled System Maintenance:
On May 6th, single article purchases and IEEE account management will be unavailable from 8:00 AM - 5:00 PM ET (12:00 - 21:00 UTC). We apologize for the inconvenience.
By Topic

Development and implementation of an adaptive fuzzy-neural-network controller for brushless drives

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
Rubaai, A. ; Dept. of Electr. Eng., Howard Univ., Washington, DC, USA ; Ricketts, D. ; Kankam, M.D.

This paper introduces a brushless drive system with an adaptive fuzzy-neural-network controller. First, a neural network-based architecture is described for fuzzy logic control. The characteristic rules and their membership functions of fuzzy systems are represented as the processing nodes in the neural network structure. Then, the fuzzy rules and input-output of the system are tuned by the supervised gradient decent learning algorithm. Using an experimental setup, the performance of the proposed controller is evaluated under various operating conditions. Test results are presented and discussed. The controller is shown to be robust, adaptive, and capable of learning. The effectiveness of the fuzzy-neural-network controller is demonstrated by its encouraging study results, when compared with those of a proportional-integral controller

Published in:

Industry Applications, IEEE Transactions on  (Volume:38 ,  Issue: 2 )