Quadrotor Accelerometer and Gyroscope Sensor Fault Diagnosis with Experimental Results

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Remus C Avram Xiaodong Zhang Jonathan Muse

Abstract

This paper presents the design and real-time experimental results of a fault diagnosis scheme for inertial measurement unit (IMU) measurements of quadrotor unmanned air vehicles (UAVs). The objective is to detect, isolate, and estimate sensor bias fault in accelerometer and gyroscope measurements. Based on the quadrotor dynamics and sensor models under consideration, the effects of sensor faults are represented as virtual actuator faults in the quadrotor state equations. Two nonlinear diagnostic estimators are designed to provide structured residuals enabling the simultaneous detection and isolation of the sensor faults. Additionally, based on the detection and isolation scheme, two nonlinear adaptive estimators are employed for the estimation of the fault magnitude. The performance of the diagnosis method is evaluated and demonstrated through real-time flight experiments.

How to Cite

C Avram, . R. ., Zhang, X. ., & Muse, J. . (2015). Quadrotor Accelerometer and Gyroscope Sensor Fault Diagnosis with Experimental Results. Annual Conference of the PHM Society, 7(1). https://doi.org/10.36001/phmconf.2015.v7i1.2720
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Keywords

Accelerometer and Gyro Fault, Sensor Fault Detection Isolation and Estimation

References
Avram, R. C. (2015). Fault Diagnosis and Fault Tolerant Control of Quadrotor UAVs (Tech. Rep.). Wright State University, Dayton, OH.

Bastin, G., & Gevers, M. (1988, Jul). Stable adaptive observers for nonlinear time-varying systems. IEEE Transactions on Automatic Control, 33(7), 650-658.

Guenard, N., Hamel, T., & Mahony, R. (2008). A practical visual servo control for an unmanned aerial vehicle. IEEE Transaction on Robotics, 24(2).

Gustafsson, F. (2000). Adaptive filtering and change detection. Wiley, West Sussex, England.

Ioannou, P. A., & Sun, J. (1996). Robust Adaptive Control. Mineola, New York: Dover Publications, Inc.

Ireland, M., & Anderson, D. (2012). Development of navigation algorithms for NAP-of-the-earth UAV flight in a constrained urban environment. In 28th International Congress of the Aeronautical Sciences.

Lantos, B., & Marton, L. (2011). Nonlinear Control of Vehicles and Robots. Springer-London.

Leishman, R. C., Jr., J. C. M., Beard, R. W., & McLain, T. (2014). Quadrotors and accelerometers. State estimation with an improved dynamic model. IEEE Control Systems Magazine, 34(1).

Macdonald, J., Leishman, R., Beard, R., & McLain, T. (2014). Analysis of an improved IMU-based ob- server for multirotor helicopters. Journal of Intelligent Robotic Systems, 64, 1049-1061.

Martin, P., & Salau ̈n, E. (2010). The true role of accelerometer feedback in quadrotor control. In IEEE Internaional Conference on Robotics and Automtation.

Shima, T., & Rasmussen, S. (2008). UAV cooperative decision and control: Challenges and practical approaches. In SIAM.

Zhang, X. (2011). Sensor bias fault detection and isolation in a class of nonlinear uncertain systems using adaptive estimation. IEEE Transaction on Automatic Control, 56(5).

Zhang, X., Polycarpou, M., & Parsini, T. (2001). Robust fault isolation for a class of non-linear input-output systems. International Journal Control, 74(13).
Section
Technical Papers