By Topic

Adaptive recurrent fuzzy neural network control for synchronous reluctance motor servo drive

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 $31
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

1 Author(s)
Lin, C.-H. ; Dept. of Electr. Eng., Nat. United Univ., Miao Li, Taiwan

In the paper an adaptive recurrent fuzzy neural network (ARFNN) control system is proposed, to control a synchronous reluctance motor (SynRM) servo drive. First, the field-oriented mechanism is applied to formulate the dynamic equation of the SynRM servo drive. Then, the ARFNN control system is proposed to control the rotor of the SynRM servo drive for the tracking of periodic reference inputs. In the ARFNN control system, the RFNN controller is used to mimic an optimal control law, and the compensated controller with adaptive algorithm is proposed to compensate for the difference between the optimal control law and the RFNN controller. Moreover, an online parameter training methodology, which is derived using the Lyapunov stability theorem and the backpropagation method, is proposed to increase the learning capability of the RFNN. The effectiveness of the proposed control scheme is verified by simulated and experimental results.

Published in:

Electric Power Applications, IEE Proceedings -  (Volume:151 ,  Issue: 6 )