A Novel Sensor Fault Detection in an Unmanned Quadrotor Based on Adaptive Neural Observer
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Prompt detection and isolation of faults and failures in flight control systems are crucial to avoid negative impacts on human and environmental systems, and to the system itself. In this study, a new scheme based on a nonlinear dynamic model is designed for sensor fault detection and isolation in an unmanned aerial vehicle (UAV) system. In the proposed design, a neural network is used as an observer for faults in the UAV sensors. The weighting parameters of the neural network are updated by the Extended Kalman Filter (EKF). The designed fault detection (FD) system is applied to an unmanned quadrotor model, and the simulation results show that the proposed design is capable of the prompt detection of sensor faults.
KeywordsMalfunction Sensor Flight dynamic Nonlinear model Control Extended Kalman Filter
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- 1.Heredia, G., Ollero, A.: Detection of sensor faults in small helicopter UAVs using observer/Kalman filter identification. Math. Probl. Eng. 2011 (2011)Google Scholar
- 14.Talebi, H., Patel, R.: An intelligent fault detection and recovery scheme for reaction wheel actuator of satellite attitude control systems. In: 2006 IEEE Conference on Computer Aided Control System Design, 2006 IEEE International Conference on Control Applications, 2006 IEEE International Symposium on Intelligent Control, pp. 3282–3287 (2006)Google Scholar
- 19.Wu, Q., Saif, M.: Neural adaptive observer based fault detection and identification for satellite attitude control systems. In: Proceedings of the 2005, American Control Conference 2005, pp. 1054–1059 (2005)Google Scholar
- 29.Smeur, E.J., Chu, Q., de Croon, G.C.: Adaptive incremental nonlinear dynamic inversion for attitude control of micro air vehicles. J. Guid. Control. Dyn. 38, 450–461 (2015)Google Scholar