Sensor Fault Diagnosis for an UAV Control System Based on a Strong Tracking Kalman Filter

Feng Tian; Jian Yang Zheng; Tong Zhang

doi:10.4028/www.scientific.net/AMM.687-691.270

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 687-691Sensor Fault Diagnosis for an UAV Control System...

Sensor Fault Diagnosis for an UAV Control System Based on a Strong Tracking Kalman Filter

Abstract:

The fault diagnosis of unmanned aerial vehicle (UAV) flight control system is an important research of UAV in health management. The sensor is the link which easiest to have problems of the flight control system. Making timely and accurate prediction of its faults is particularly important. A strong tracking Kalman Filter method for the sensor fault diagnosis of UAV flight control system was presented in this paper. The parameters of the system were extended to the state variables, the sensor fault observer was constructed, and the joint estimation of states and parameters of flight control system were gotten. The method can be used to real-time estimate the unmeasured states and time-varying parameters. The results of simulation experiments show that the method has a good real-time and accuracy in the sensor fault diagnosis of flight control system.

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Periodical:

Applied Mechanics and Materials (Volumes 687-691)

Pages:

270-274

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.687-691.270

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Online since:

November 2014

Authors:

Feng Tian*, Jian Yang Zheng, Tong Zhang

Keywords:

Fault Diagnosis, Flight Control System, Sensor, Strong Tracking Kalman Filter

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* - Corresponding Author

References

[1] Z. Peng. Aeroengine fault diagnostics based on kalman filter[D]. Nanjing University of Aeronautics and Astronautics, (2009).

Google Scholar

[2] X. Chao. A kalman filter-based approach to sensor fault diagnosis of aeroengine [D]. Nanjing University of Aeronautics and Astronautics, (2013).

Google Scholar

[3] J. Cai-juan,Z. Xiao-ping,Z. zhou. The study and simulation of sensor fault diagnosis for an UAV control system[J]. Computer Simulation, 53-55, (2005).

Google Scholar

[4] Z. Guo-chao,L. Ping,T. Wang-ming, Meng Qing-zhi. Fault diagnosis for unmanned helicopter based on STF and wavelet threshold denoising[J]. Control Engineering of China. 517-520(2013).

Google Scholar

[5] L. Zhi-cheng, L. Jian-hua, Q. Hai-lian. Improved method for sensor fault diagnosis based on strong tracking filtering[J]. Computer Engineering and Applications. 229-231(2010).

Google Scholar