Neural network based sensor validation scheme demonstrated on an unmanned air vehicle (UAV) model

Ihab Samy, Ian Postlethwaite, Da-Wei Gu

    Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

    19 Citations (Scopus)

    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.
    Original languageEnglish
    Title of host publicationProceedings of the 2008 47th IEEE Conference on Decision and Control
    Place of PublicationPiscataway, NJ
    PublisherIEEE
    Pages1237-1242
    ISBN (Print)978-1424431236
    DOIs
    Publication statusPublished - 2008
    Event47th IEEE Conference on Decision and Control, 2008 - Cancun Mexico
    Duration: 1 Jan 2008 → …

    Conference

    Conference47th IEEE Conference on Decision and Control, 2008
    Period1/01/08 → …

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