A Comparison between Fuzzy, PI and B-spline Control
A Comparison between Fuzzy, PI and B-spline Control
Fuzzy control is a rule-based control in which fuzzy logic interpolates between production rules. That is why it interesting to compare it with classical interpolation techniques like B-splines. A B-spline function is a piecewise polynomial function. A B-spline controller is defined as a controller that maps inputs to outputs using a B-spline surface. This controller can be interpreted as a fuzzy controller with the probability operators product and sum instead of Zadeh operators min and max. B-splines also provide a framework for choosing the shape of the fuzzy sets. Using this method, we obtain much smoother control surfaces than with the usual fuzzy operators and can readily obtain a linear PI controller. This new viewpoint, which is very fruitful, suggests also new ways to adapt fuzzy controllers. A comparison with neural networks is made.
372--373
Constancis, P.
1b73f60a-f225-430a-a9a7-372c2bbbb8d2
Harris, C.J.
c4fd3763-7b3f-4db1-9ca3-5501080f797a
1992
Constancis, P.
1b73f60a-f225-430a-a9a7-372c2bbbb8d2
Harris, C.J.
c4fd3763-7b3f-4db1-9ca3-5501080f797a
Constancis, P. and Harris, C.J.
(1992)
A Comparison between Fuzzy, PI and B-spline Control.
Int. Symp. on Intelligent Control.
.
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(Other)
Abstract
Fuzzy control is a rule-based control in which fuzzy logic interpolates between production rules. That is why it interesting to compare it with classical interpolation techniques like B-splines. A B-spline function is a piecewise polynomial function. A B-spline controller is defined as a controller that maps inputs to outputs using a B-spline surface. This controller can be interpreted as a fuzzy controller with the probability operators product and sum instead of Zadeh operators min and max. B-splines also provide a framework for choosing the shape of the fuzzy sets. Using this method, we obtain much smoother control surfaces than with the usual fuzzy operators and can readily obtain a linear PI controller. This new viewpoint, which is very fruitful, suggests also new ways to adapt fuzzy controllers. A comparison with neural networks is made.
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Published date: 1992
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Organisation: IEEE Address: Glasgow, UK
Venue - Dates:
Int. Symp. on Intelligent Control, 1992-01-01
Organisations:
Southampton Wireless Group
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Local EPrints ID: 250261
URI: http://eprints.soton.ac.uk/id/eprint/250261
PURE UUID: d1ba428f-52c5-4b17-a695-b2c6262bbe01
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Date deposited: 04 May 1999
Last modified: 10 Dec 2021 20:07
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Author:
P. Constancis
Author:
C.J. Harris
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