Abstract
This paper presents a preflight diagnosis method for detecting multicopter’s motor abnormalities using jig equipment data. While operating multicopters on a regular basis, determining whether it can perform the flight or not is important. For this, we use disturbance observer’s output as a feature for detecting degree of the abnormality by Gaussian process regression. During the ground inspection test where most of the disturbances are under control, motor degradation and disturbances are significantly correlated. Then, motor degradation can be estimated using the Gaussian process regression. To create multivariate output models against different degrees of motor abnormalities, we use multitask a Gaussian process regression model. To verify the performance of the proposed approach, actual preflight tests on a ground jig device developed in-house were performed with an actual quadcopter drone.
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This work is partially supported by the Korea Agency for Infrastructure Technology Advancement (KAIA) grant funded by the Ministry of Land, Infrastructure and Transport, the Ministry of Science and ICT, and the Ministry of Trade, Industry and Energy (Grant 19DPIW-C153691-01) and by Unmanned Vehicles Advanced Core Technology Research and Development Program Through the National Research Foundation of Korea (NRF), Unmanned Vehicle Advanced Research Center (UVARC) funded by the Ministry of Science and ICT, the Republi of Korea (2016M1B3A1A02937510). This research was supported by Unmanned Vehicles Core Technology Research and Development Program through the National Research Foundation of Korea (NRF) and Unmanned Vehicle Advanced Research Center (UVARC) funded by the Ministry of Science and ICT, the Republic of Korea (2020M3C1C1A01083162).
Junghoon Kim received his B.Sc. degree in automotive engineering from Hanyang University, Seoul, Korea, in 2017, and then acquired his M.Sc. degree in electrical and computer engineering from Seoul National University in 2019. He is currently a research engineer at J.MARPLE, Inc., Korea. He is interested in variety of topics, such as Neural network pruning and human-level decision making and control for autonomus systems.
Juhee Lee received her B.Sc. degree in mathematics from Hannam University, Daejon, Korea, in 1996, and then acquired her Ph.D. degree in Cryptography from Ewha Womans University in 2010. She is a researcher at J.MARPLE, Inc., Korea. She was a research professor at Ewha Womans university from 2013 to 2018. Previously, she was a researcher at National Institute for Mathematical Sciences (NIMS) in 2012. She is interested in all aspects in mathematics for control theory, deep/machine learning and blockchain. Recently, she is very interested in lightweight deep learning techniques.
Phil Kim received his B.Sc. degree in aerospace engineering from Seoul National University (SNU), Seoul, Korea, in 1994, and then acquired his Ph.D. degree from SNU in 2002. He is currently a CEO at J.MARPLE, Inc., Korea. He was an seninor research officer at Korea National Rehabilitation Center from 2002 to 2008. Previously, he was a senior researcher at Korea Aerospace Research Institute (KARI) from 2002 to 2008.
Jangho Lee received his B.Sc. degree in mechanical engineering from Korea Aerospace University in 2001, an M.D. degree in mechanical and aerospace engineering from Seoul National University in 2003, and then acquired a Ph.D. degree from Korea Advanced Institute of Science and Technology in 2017. He is currently a researcher at the Smart Urban Air Mobility Team, Korea Aerospace Research Institute, Korea. He is interested in urban air mobility, autonomous flight, contingency management, and fault tolerant control.
Seungkeun Kim received his B.Sc. degree in mechanical and aerospace engineering from Seoul National University (SNU), Seoul, Korea, in 2002, and then acquired his Ph.D. degree from SNU in 2008. He is currently a professor at the Department of Aerospace Engineering, Chungnam National University, Korea. He was an associate professor and an assistant professor at the same university from 2012 to 2020. Previously, he was a research fellow and a lecturer at Cranfield University, United Kingdom from 2008 to 2012. He is interested in micro aerospace systems, aircraft guidance and control, estimation, sensor fusion, fault diagnosis, fault tolerant control, and decision-making for autonomous systems.
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Kim, J., Lee, J., Kim, P. et al. Preflight Diagnosis of Multicopter Thrust Abnormalities Using Disturbance Observer and Gaussian Process Regression. Int. J. Control Autom. Syst. 19, 2195–2202 (2021). https://doi.org/10.1007/s12555-020-0164-8
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DOI: https://doi.org/10.1007/s12555-020-0164-8