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Proceedings Paper

Design of a cart-pole balancing fuzzy logic controller using a genetic algorithm
Author(s): Charles L. Karr
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Paper Abstract

Scientists at the U.S. Bureau of Mines are currently investigating ways to combine the control capabilities of fuzzy logic with the learning capabilities of genetic algorithms. Fuzzy logic affords a mechanism for incorporating the uncertainty inherent in most control problems into conventional expert systems. Although fuzzy logic-based expert systems have been used successfully for controlling a number of physical systems, the selection of acceptable fuzzy membership functions has generally been a subjective and time consuming decision. In this paper, high-performance fuzzy membership functions for a fuzzy logic controller that manipulates a mathematical model simulating a cart-pole balancing system are selected using a genetic algorithm, a search technique based on the mechanics of natural genetics. The membership functions chosen by the genetic algorithm provide for a more efficient fuzzy logic controller than membership functions selected by the author for the cart-pole balancing problem. Thus, genetic algorithms represent a potentially effective and structured approach for designing fuzzy logic controllers.

Paper Details

Date Published: 1 March 1991
PDF: 11 pages
Proc. SPIE 1468, Applications of Artificial Intelligence IX, (1 March 1991); doi: 10.1117/12.45446
Show Author Affiliations
Charles L. Karr, U.S. Bureau of Mines (United States)

Published in SPIE Proceedings Vol. 1468:
Applications of Artificial Intelligence IX
Mohan M. Trivedi, Editor(s)

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