By Topic

Differential Evolution Versus Particle Swarm Optimization for PID Controller Design

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

1 Author(s)
Ruijun Dong ; Dept. of Autom., Xidian Univ., Xi''an, China

Differential evolution is a high-performance optimizer that is very easy to understand and implement. It is similar in some ways to genetic algorithms or evolutionary algorithms, but requires less computational bookkeeping and generally only a few lines of code. In this paper, a differential evolution optimizer is implemented and compared to a particle swarm optimization for control of a first-order process with a time delay, using fuzzy PID, and PID controller. The results show that the optimization scenarios are better suited to differential evolution versus the other. The differential evolution optimizer shares the ability of the genetic algorithm to handle arbitrary nonlinear cost functions, but with a much simpler implementation and a better performance it clearly demonstrates good possibilities for widespread use in controller optimization.

Published in:

Natural Computation, 2009. ICNC '09. Fifth International Conference on  (Volume:3 )

Date of Conference:

14-16 Aug. 2009