As shown in Fig. 38 and Fig. 39, once a fault is injected, the quadrotor diverges from the path near (3, 0) and fails to recover to the normal operation.
Figure 38: Tracking failed result on path 1 Figure 39: Tracking failed result on path 2 On the other hand, when applying bias correction through the camera, the quadrotor followed its path without diverging, as shown in Fig. 40 and Fig. 41. Analysis of detection and recovery is covered on the next page.
Figure 40: Tracking succeed result on path 1 Figure 41: Tracking succeed result on path 2
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Figure 42: Result of chi-square test for path 1
Figure 43: Result after applying correction for path 1
After fault injection, bias was detected andλgyroexceeded the threshold as shown in Fig. 42. Then bias is corrected andλgyrodecreased. After the failure ends, theλgyrorises again by the correction term.
In Fig. 43, it can be seen that the bias corrected value suddenly changed due to bias during the time from fault injection to detection. However, it is corrected by the controller because of short period.
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Figure 44: Result of chi-square test for path 2
Figure 45: Result after applying correction for path 2
Path 2 also shows similar results as path1. In Fig. 44, theλgyro exceeded the threshold at the start and end of the fault, and in Fig. 45, the bias was corrected, so there is a change in sensor value during the delay time, but it operates normally because of the controller.
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VI Conclusion
In this work, a system that overcomes the bias fault situation of the z-gyro sensor of the quadrotor by calculating the yaw rate through a camera attached to the bottom of the quadrotor was studied. The yaw rate calculated through the camera showed a value similar to the actually measured value, showing the possibility of restoration. However, the output frequency of the camera is much lower than the that of gyro sensor, the method to overcome through switching still needs improvement. But it was shown that constant bias gyro fault can be corrected through the camera.
Future work to create better fault detection and recovery systems is as follows. First is improving image processing to enable stable yaw rate measurement even at high frequencies. It requires filtering the incorrect feature pairs. Second is creating more accurate estimation performance with a more accurate dynamic model. There are some elements that are not considered in the dynamics. Third is reducing false detection situations through adaptive thresholds rather than constant thresholds. For certain UAVs, it may be necessary to set a stricter or more generous threshold.
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Acknowledgements
First of all, I would like to thank my adviser, Professor Hungsun Son, who gave me a lot of advice and guidance during my master’s course. There were times when I was lost and discouraged, but thanks to the support and consideration of the professor, I was able to overcome it.
I am also grateful to the members of the Electromechanical Systems and Control Laboratory (ESCL), Myunggun Kim, Seongmin Lee, Hoyoung Kim, Minho Shin, Sangheon Lee, Wonmo Chung, Sejoon Joo, Sanha Lee and Yonghyun Cho. I was able to learn a lot thanks to the members’ advice, and I really enjoyed working together.
Lastly, thank you to my family who gave me strength, and to those who were with me. I think I came this far thanks to the love you gave me. Thank you again!
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