By Topic

Genetic algorithms in controller design and tuning

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)
Varsek, A. ; Dept. of Comput. Sci., Ljubljana Univ., Slovenia ; Urbancic, T. ; Filipic, B.

A three-phased framework for learning dynamic system control is presented. A genetic algorithm is employed to derive control rules encoded as decision tables. Next, the rules are automatically transformed into comprehensible form by means of inductive machine learning. Finally, a genetic algorithm is applied again to optimize the numerical parameters of the induced rules. The approach is experimentally verified on a benchmark problem of inverted pendulum control, with special emphasis on robustness and reliability. It is also shown that the proposed framework enables exploiting available domain knowledge. In this case, genetic algorithm makes qualitative control rules operational by providing interpretation of symbols in terms of numerical values

Published in:

Systems, Man and Cybernetics, IEEE Transactions on  (Volume:23 ,  Issue: 5 )