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Proceedings Paper

Improved Fuzzy Process Control of Spacecraft Autonomous Rendezvous Using a Genetic Algorithm
Author(s): C. L. Karr; L. M. Freeman; D. L. Meredith
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Paper Abstract

The U.S. Bureau of Mines is currently investigating ways to combine the control capabilities of fuzzy logic with the learning capabilities of genetic algorithms. Fuzzy logic allows for the uncertainty inherent in most control problems to be incorporated 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 decision. In this paper, high-performance fuzzy membership functions for a fuzzy logic controller that manipulates a mathematical model simulating the autonomous rendezvous of a spacecraft are learned using a genetic algorithm, a search technique based on the mechanics of natural genetics. The membership functions learned by the genetic algorithm provide for a more efficient fuzzy logic controller than membership functions selected by the authors for the rendezvous problem. Thus, genetic algorithms are potentially an effective and structured approach for learning fuzzy membership functions.

Paper Details

Date Published: 1 February 1990
PDF: 15 pages
Proc. SPIE 1196, Intelligent Control and Adaptive Systems, (1 February 1990); doi: 10.1117/12.969926
Show Author Affiliations
C. L. Karr, U.S. Bureau of Mines (United States)
L. M. Freeman, The University of Alabama (United States)
D. L. Meredith, The University of Alabama (United States)


Published in SPIE Proceedings Vol. 1196:
Intelligent Control and Adaptive Systems
Guillermo Rodriguez, Editor(s)

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