The University of Southampton
University of Southampton Institutional Repository

A Comparison between Fuzzy, PI and B-spline Control

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
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. 372--373 .

Record type: Conference or Workshop Item (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.

This record has no associated files available for download.

More information

Published date: 1992
Additional Information: Organisation: IEEE Address: Glasgow, UK
Venue - Dates: Int. Symp. on Intelligent Control, 1992-01-01
Organisations: Southampton Wireless Group

Identifiers

Local EPrints ID: 250261
URI: http://eprints.soton.ac.uk/id/eprint/250261
PURE UUID: d1ba428f-52c5-4b17-a695-b2c6262bbe01

Catalogue record

Date deposited: 04 May 1999
Last modified: 10 Dec 2021 20:07

Export record

Contributors

Author: P. Constancis
Author: C.J. Harris

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of http://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×