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Title: Neural Network Based Sensor Validation Scheme Demonstrated on an Unmanned Air Vehicle (UAV) Model.
Authors: Samy, Ihab
Postlethwaite, Ian
Gu, Da-Wei
First Published: Dec-2008
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Citation: Decision and Control, 2008. CDC 2008. 47th IEEE Conference on, Proceedings of, pp. 1237-1242.
Abstract: Nowadays model-based fault detection and isolation (FDI) systems have become a crucial step towards autonomy in aerospace engineering. However few publications consider FDI applications to unmanned air vehicles (UAV) where full-autonomy is obligatory. In this paper we demonstrate a sensor fault detection and accommodation (SFDA) system, which makes use of analytical redundancy between flight parameters, on a UAV model. A Radial-Basis Function (RBF) neural network (NN) trained online with Extended Minimum Resource Allocating Network (EMRAN) algorithms is chosen for modelling purposes due to its ability to adapt well to nonlinear environments while maintaining high computational speeds. Furthermore, in an attempt to reduce false alarms (FA) and missed faults (MF) in current SFDA systems, we introduce a novel residual generator. After 47 minutes (CPU running time) of NN offline training, the SFDA scheme is able to detect additive and constant bias sensor faults with zero FA and MF. It also shows good global approximation capabilities, essential for fault accommodation, with an average pitch gyro estimation error of 0.0075 rad/s.
DOI Link: 10.1109/CDC.2008.4738703
ISSN: 0191-2216
ISBN: 9781424431236
Type: Conference paper
Rights: Copyright © 2008 IEEE. Reprinted from Decision and Control, 2008. CDC 2008. 47th IEEE Conference on, Proceedings of, pp. 1237-1242. Doi: 10.1109/CDC.2008.4738703 This material is posted here with permission of the IEEE. Such permission of the IEEE does not in any way imply IEEE endorsement of any of the University of Leicester’s products or services. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to By choosing to view this document, you agree to all provisions of the copyright laws protecting it.
Appears in Collections:Conference Papers & Presentations, Dept. of Engineering

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