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Genetic algorithms in adaptive fuzzy controlResearchers at the U.S. Bureau of Mines have developed adaptive process control systems in which genetic algorithms (GA's) are used to augment fuzzy logic controllers (FLC's). GA's are search algorithms that rapidly locate near-optimum solutions to a wide spectrum of problems by modeling the search procedures of natural genetics. FLC's are rule based systems that efficiently manipulate a problem environment by modeling the 'rule-of-thumb' strategy used in human decision making. Together, GA's and FLC's possess the capabilities necessary to produce powerful, efficient, and robust adaptive control systems. To perform efficiently, such control systems require a control element to manipulate the problem environment, an analysis element to recognize changes in the problem environment, and a learning element to adjust fuzzy membership functions in response to the changes in the problem environment. Details of an overall adaptive control system are discussed. A specific computer-simulated chemical system is used to demonstrate the ideas presented.
Document ID
19930020383
Acquisition Source
Legacy CDMS
Document Type
Conference Paper
Authors
Karr, C. Lucas
(Bureau of Mines Tuscaloosa, AL, United States)
Harper, Tony R.
(Bureau of Mines Tuscaloosa, AL, United States)
Date Acquired
September 6, 2013
Publication Date
December 1, 1992
Publication Information
Publication: NASA. Johnson Space Center, North American Fuzzy Logic Processing Society (NAFIPS 1992), Volume 2
Subject Category
Theoretical Mathematics
Accession Number
93N29572
Distribution Limits
Public
Copyright
Work of the US Gov. Public Use Permitted.

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