Scheduled System Maintenance:
On Monday, April 27th, IEEE Xplore will undergo scheduled maintenance from 1:00 PM - 3:00 PM ET (17:00 - 19:00 UTC). No interruption in service is anticipated.
By Topic

TSK-type recurrent fuzzy network for dsp-based permanent-magnet linear synchronous 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

A TSK-type recurrent fuzzy network (TSKRFN) control system is proposed to control the position of the mover of a field-oriented control permanent-magnet linear synchronous motor (PMLSM) servo drive system to track periodic reference trajectories in this study. The proposed TSKRFN combines the merits of self-constructing fuzzy neural network (SCFNN), TSK-type fuzzy inference mechanism, and recurrent neural network (RNN). Moreover, the structure and the parameter learning phases are preformed concurrently and online in the TSKRFN. The structure learning is based on the partition of input space, and the parameter learning is based on the supervised gradient-descent method using a delta adaptation law. Furthermore, all the control algorithms are implemented in a TMS320C32 DSP-based control computer. The simulated and experimental results due to periodic reference trajectories show that the dynamic behaviour of the proposed TSKRFN control system is robust with regard to uncertainties

Published in:

Electric Power Applications, IEE Proceedings -  (Volume:153 ,  Issue: 6 )