By Topic

A model reference & sensitivity model-based self-learning fuzzy logic controller as a solution for control of nonlinear servo systems

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)
Kovaeic, Z. ; Fac. of Electr. Eng. & Comput., Zagreb Univ., Croatia ; Bogdan, S. ; Balenovic, M.

In this paper, the design, simulation and experimental verification of a self-learning fuzzy logic controller (SLFLC) suitable for the control of nonlinear servo systems are described. The SLFLC contains a learning algorithm that utilizes a second-order reference model and a sensitivity model related to the fuzzy controller parameters. The effectiveness of the proposed controller has been tested in the position control loops of two chopper-fed DC servo systems, first by simulation in the presence of a backlash nonlinearity, then by experiment in the presence of a gravity-dependent shaft load and fairly high static friction. The simulation and experimental results have proved that the SLFLC provides desired closed loop behavior and eliminates a steady-state position error

Published in:

Energy Conversion, IEEE Transactions on  (Volume:14 ,  Issue: 4 )