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Fuzzy neural network control of the rehabilitation robotic arm driven by pneumatic muscles

Fuzzy neural network control of the rehabilitation robotic arm driven by pneumatic muscles Purpose – The main purpose of this paper is to enhance the control performance of the robotic arm by the controller of fuzzy neural network (FNN). Design/methodology/approach – The robot system has characters of high order, time delay, time variation and serious nonlinearity. The classical PID controller cannot achieve satisfactory performance in control of such a complex system. This paper combined the fuzzy control with neural networks and established the FNN controller and applied it in control of the robot. Findings – The experimental results showed that the FNN controller had excellent performances in position control of the rehabilitation robotic arm such as fast response, small overshoot and small vibration. Research limitations/implications – This work is focused on the static FNN algorithm by updating the second and fifth layers of the membership functions. The performance can be improved further if the third layer (reasoning layer) can be updated online. Originality/value – Based on a hierarchical structure of the FNN controller, this paper designed the FNN controller and applied it in control of the rehabilitation robot driven by pneumatic muscles. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Industrial Robot: An International Journal Emerald Publishing

Fuzzy neural network control of the rehabilitation robotic arm driven by pneumatic muscles

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References (23)

Publisher
Emerald Publishing
Copyright
Copyright © Emerald Group Publishing Limited
ISSN
0143-991X
DOI
10.1108/IR-07-2014-0374
Publisher site
See Article on Publisher Site

Abstract

Purpose – The main purpose of this paper is to enhance the control performance of the robotic arm by the controller of fuzzy neural network (FNN). Design/methodology/approach – The robot system has characters of high order, time delay, time variation and serious nonlinearity. The classical PID controller cannot achieve satisfactory performance in control of such a complex system. This paper combined the fuzzy control with neural networks and established the FNN controller and applied it in control of the robot. Findings – The experimental results showed that the FNN controller had excellent performances in position control of the rehabilitation robotic arm such as fast response, small overshoot and small vibration. Research limitations/implications – This work is focused on the static FNN algorithm by updating the second and fifth layers of the membership functions. The performance can be improved further if the third layer (reasoning layer) can be updated online. Originality/value – Based on a hierarchical structure of the FNN controller, this paper designed the FNN controller and applied it in control of the rehabilitation robot driven by pneumatic muscles.

Journal

Industrial Robot: An International JournalEmerald Publishing

Published: Jan 19, 2015

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