NDLI: Comparison of fuzzy and neural network adaptive methods for the position control of a pneumatic system

Content Provider	IEEE Xplore Digital Library
Author	Dehghan, B. Surgenor, B.W.
Copyright Year	2013
Description	Author affiliation: Dept. of Mech. & Mater. Eng., Queen's Univ., Kingston, ON, Canada (Dehghan, B.; Surgenor, B.W.)
Abstract	This paper reports on a study whose objective is to explore the potential and compare the performance of intelligent adaptive control methods. Specifically, the performance of PID plus an adaptive neural network compensator (ANNC) is compared with the performance of a fuzzy adaptive PID controller. The application is position control of a pneumatic gantry robot. Both controllers were carefully tuned to provide a fair comparison. Experimental results were collected for the tracking of a sine wave. Both adaptive controllers were found to improve tracking performance over fixed gain PID by upwards of 70%. However, the tuning procedure for the fuzzy controller is judged to be more intuitive in nature and hence, more practical than that for ANNC. The fuzzy adaptive controller uses a novel rule set that is reduced in size from that used in previous studies.
Starting Page	1
Ending Page	4
File Size	446217
Page Count	4
File Format	PDF
ISBN	9781479900312
ISSN	08407789
e-ISBN	9781479900336
e-ISBN	9781479900329
DOI	10.1109/CCECE.2013.6567712
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-05-05
Publisher Place	Canada
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Adaptive systems Pneumatic systems Fuzzy logic Artificial neural networks Position control Robots pneumatic servosystems Adaptive PID fuzzy neural network
Content Type	Text
Resource Type	Article
Subject	Electrical and Electronic Engineering Hardware and Architecture

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