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Robust tracking control system design for a nonlinear IPMC using neural network-based sliding mode approach

Robust tracking control system design for a nonlinear IPMC using neural network-based sliding... In this paper, a robust nonlinear tracking control design for an ionic polymer metal composite (IPMC) with uncertainties is proposed by using neural network-based sliding mode approach. The IPMC, also called artificial muscle, is a novel smart polymer material, and many potential applications for low mass high displacement actuators in biomedical and robotic systems have been shown. In general, the IPMC has highly nonlinear property, and there exist uncertainties caused by identifying some physical parameters and approximate calculation in dynamic model. Moreover, the control input is subject to some constraints to ensure safety and longer service life of IPMC. As a result, for a nonlinear dynamic model with uncertainties and input constraints, an IPMC artificial muscles position tracking control system based on sliding mode control approach is presented, where, the improved exponential reaching law is used to design sliding mode controller, a saturation function is used in the sliding mode control law design to suppress chattering. The robust stability can be guaranteed. Moreover, in order to improve tracking performance, a quickly and precisely robust tracking system to the stabilised system is designed and the parameters of tracking controller are optimised by using neural network. Finally, the effectiveness of http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png International Journal of Advanced Mechatronic Systems Inderscience Publishers

Robust tracking control system design for a nonlinear IPMC using neural network-based sliding mode approach

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Publisher
Inderscience Publishers
Copyright
Copyright © 2015 Inderscience Enterprises Ltd.
ISSN
1756-8412
eISSN
1756-8420
DOI
10.1504/IJAMECHS.2015.074783
Publisher site
See Article on Publisher Site

Abstract

In this paper, a robust nonlinear tracking control design for an ionic polymer metal composite (IPMC) with uncertainties is proposed by using neural network-based sliding mode approach. The IPMC, also called artificial muscle, is a novel smart polymer material, and many potential applications for low mass high displacement actuators in biomedical and robotic systems have been shown. In general, the IPMC has highly nonlinear property, and there exist uncertainties caused by identifying some physical parameters and approximate calculation in dynamic model. Moreover, the control input is subject to some constraints to ensure safety and longer service life of IPMC. As a result, for a nonlinear dynamic model with uncertainties and input constraints, an IPMC artificial muscles position tracking control system based on sliding mode control approach is presented, where, the improved exponential reaching law is used to design sliding mode controller, a saturation function is used in the sliding mode control law design to suppress chattering. The robust stability can be guaranteed. Moreover, in order to improve tracking performance, a quickly and precisely robust tracking system to the stabilised system is designed and the parameters of tracking controller are optimised by using neural network. Finally, the effectiveness of

Journal

International Journal of Advanced Mechatronic Systems – Inderscience Publishers

Published: Jan 1, 2015

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