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Improved neural network-based sensor fault detection and estimation strategy for an autonomous aerial vehicle

Mati Ullah (School of Automation, Northwestern Polytechnical University, Xi'an, China)
Chunhui Zhao (School of Automation, Northwestern Polytechnical University, Xi'an, China)
Hamid Maqsood (Department of Electrical Technology, Government Polytechnic Institute, Karak, Pakistan)
Mahmood Ul Hassan (Department of Electrical Engineering, Balochistan University of Engineering and Technology Khuzdar, Khuzdar, Pakistan)
Muhammad Humayun (Department of Automation, School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, China)

International Journal of Intelligent Unmanned Systems

ISSN: 2049-6427

Article publication date: 13 December 2021

Issue publication date: 10 April 2023

212

Abstract

Purpose

This paper aims to design an adaptive nonlinear strategy capable of timely detection and reconstruction of faults in the attitude’s sensors of an autonomous aerial vehicle with greater accuracy concerning other conventional approaches in the literature.

Design/methodology/approach

The proposed scheme integrates a baseline nonlinear controller with an improved radial basis function neural network (IRBFNN) to detect different kinds of anomalies and failures that may occur in the attitude’s sensors of an autonomous aerial vehicle. An integral sliding mode concept is used as auto-tune weight update law in the IRBFNN instead of conventional weight update laws to optimize its learning capability without computational complexities. The simulations results and stability analysis validate the promising contributions of the suggested methodology over the other conventional approaches.

Findings

The performance of the proposed control algorithm is compared with the conventional radial basis function neural network (RBFNN), multi-layer perceptron neural network (MLPNN) and high gain observer (HGO) for a quadrotor vehicle suffering from various kinds of faults, e.g. abrupt, incipient and intermittent. From the simulation results obtained, it is found that the proposed algorithm’s performance in faults detection and estimation is relatively better than the rest of the methodologies.

Practical implications

For the improvement in the stability and safety of an autonomous aerial vehicle during flight operations, quick identification and reconstruction of attitude’s sensor faults and failures always play a crucial role. Efficient fault detection and estimation scheme are considered indispensable for an error-free and safe flight mission of an autonomous aerial vehicle.

Originality/value

The proposed scheme introduces RBFNN techniques to detect and estimate the quadrotor attitude’s sensor faults and failures efficiently. An integral sliding mode effect is used as the network’s backpropagation law to automatically modify its learning parameters accordingly, thereby speeding up the learning capabilities as compared to the conventional neural network backpropagation laws. Compared with the other investigated techniques, the proposed strategy achieve remarkable results in the detection and estimation of various faults.

Keywords

Acknowledgements

This research work is supported by the National Natural Science Foundation of China under Grant No. 62073264 and the Key Research and Development Project of Shaanxi Province under Grant No. 2021ZDLGY01-01.

Disclosure statement: No potential conflict of interest was reported by the authors.

Citation

Ullah, M., Zhao, C., Maqsood, H., Ul Hassan, M. and Humayun, M. (2023), "Improved neural network-based sensor fault detection and estimation strategy for an autonomous aerial vehicle", International Journal of Intelligent Unmanned Systems, Vol. 11 No. 2, pp. 226-248. https://doi.org/10.1108/IJIUS-09-2021-0109

Publisher

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Emerald Publishing Limited

Copyright © 2021, Emerald Publishing Limited

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