Imperial College London
Alternate

ProfessorFernandoBello

Faculty of Medicine, Department of Surgery & Cancer

Professor of Surgical Computing and Simulation Science
 
 
 
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Contact

 

+44 (0)20 3315 8231f.bello Website

 
 
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Location

 

G3.50Chelsea and Westminster HospitalChelsea and Westminster Campus

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Summary

 

Publications

Citation

BibTex format

@article{Kebria:2020:10.1109/TFUZZ.2019.2941173,
author = {Kebria, PM and Khosravi, A and Nahavandi, S and Wu, D and Bello, F},
doi = {10.1109/TFUZZ.2019.2941173},
journal = {IEEE Transactions on Fuzzy Systems},
pages = {2543--2554},
title = {Adaptive type-2 fuzzy neural-network control for teleoperation systems with delay and uncertainties},
url = {http://dx.doi.org/10.1109/TFUZZ.2019.2941173},
volume = {28},
year = {2020}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - Interacting with human operators, remote environment, and communication networks, teleoperation systems are considerably suffering from complexities and uncertainties. Managing these is of paramount importance for safe and smooth performance of teleoperation systems. Among the countless solutions developed by researchers, type-2 fuzzy (T2F) algorithms have shown an outstanding performance in modeling complex systems and tackling uncertainties. Moreover, artificial neural networks (NNs) are well known for their adaptive learning potentials. This article proposes an adaptive interval type-2 fuzzy neural-network control scheme for teleoperation systems with time-varying delays and uncertainties. The T2F models are developed based on the experimental data collected from a teleoperation setup over a local computer network. However, the resulted controller is evaluated on an intercontinental communication network through the Internet between Australia and Scotland. Moreover, the slave robot and the remote workspace are completely different and unforeseen. Stability and performance of the proposed control is analyzed by Lyapunov-Krasovskii method. Comprehensive comparative studies demonstrate that the proposed controller outperforms traditional techniques in experimental evaluations.
AU  - Kebria,PM
AU  - Khosravi,A
AU  - Nahavandi,S
AU  - Wu,D
AU  - Bello,F
DO  - 10.1109/TFUZZ.2019.2941173
EP  - 2554
PY  - 2020///
SN  - 1063-6706
SP  - 2543
TI  - Adaptive type-2 fuzzy neural-network control for teleoperation systems with delay and uncertainties
T2  - IEEE Transactions on Fuzzy Systems
UR  - http://dx.doi.org/10.1109/TFUZZ.2019.2941173
UR  - http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000578002800021&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=1ba7043ffcc86c417c072aa74d649202
UR  - https://ieeexplore.ieee.org/document/8834869
UR  - http://hdl.handle.net/10044/1/92117
VL  - 28
ER  -
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