A comparative study of NN- and EKF-based SFDA schemes with application to a nonlinear UAV model

Abstract

In this article, we propose two schemes for sensor fault detection and accommodation (SFDA): one based on a neural network (NN) and the other on an extended Kalman filter (EKF). The objective of this article is to compare both approaches in terms of execution time, robustness to poorly modelled dynamics and sensitivity to different fault types. The schemes are tested on an unmanned air vehicle (UAV) application where traditional sensor redundancy methods can be too heavy and/or costly. In an attempt to reduce the false alarm rates and the number of undetected faults, a modified residual generator, originally proposed in Samy, Postlethwaite, and Gu in 2008 (Samy, I., Postlethwaite, I., and Gu, D.-W. (2008a). Neural Network Sensor Validation Scheme Demonstrated on a UAV Model, in IEEE Proceedings of CDC, Cancun, Mexico, pp. 1237–1242) is implemented. Simulation work is presented for use on a UAV demonstrator under construction with support from BAE systems and EPSRC. Results have shown that the NN-SFDA scheme outperforms the EKF-SFDA scheme with only one missed fault, zero false alarms and an average estimation error of 0.31°/s for 112 different test conditions.

Keywords:

• neural networks
• extended Kalman filter
• sensor validation
• residual generator

Acknowledgements

The work done here is part of a larger project, FLAVIIR which is jointly funded by BAE Systems and ESPRC. The authors would like to thank Cranfield University for providing the UAV model.