By Topic

Total sliding-model-based particle swarm optimization control design for linear induction motor

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
$33 $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)
Rong-Jong Wai ; Department of Electrical Engineering, Yuan Ze University, Chung-Li, Taiwan 32003, China ; Kun-Lun Chuang ; Jeng-Dao Lee

In this study, a total sliding-mode-based particle swarm optimization control (TSPSOC) scheme is designed for the periodic motion control of an indirect field-oriented linear induction motor (LIM) drive. First, an indirect field-oriented mechanism for a LIM drive is introduced to preserve the decoupling control characteristic. Then, the concept of total sliding-mode control (TSC) is incorporated into particle swarm optimization (PSO) to form an on-line TSPSOC framework for preserving the robust control characteristics and reducing the chattering control phenomena of TSC. Moreover, an adaptive inertial weight is devised to accelerate the searching speed effectively. In this control scheme, a PSO control system is utilized to be the major controller, and the stability can be indirectly ensured by the concept of TSC without strict constraint and detailed system knowledge. Numerical simulations are given to verify the effectiveness of the proposed control scheme for the tracking of periodic reference trajectories. In addition, the superiority of the proposed TSPSOC scheme is indicated in comparison with the TSC, Petri fuzzy-neural-network control (PFNNC), and traditional fuzzy-neural -network control (TFNNC) systems.

Published in:

2007 IEEE Congress on Evolutionary Computation

Date of Conference:

25-28 Sept. 2007