Share:


Flight phase classification for small unmanned aerial vehicles

    Jakub Leško   Affiliation
    ; Rudolf Andoga   Affiliation
    ; Róbert Bréda Affiliation
    ; Miriam Hlinková   Affiliation
    ; Ladislav Fözö   Affiliation

Abstract

This article describes research on the classification of flight phases using a fuzzy inference system and an artificial neural network. The aim of the research was to identify a small set of input parameters that would ensure correct flight phase classification using a simple classifier, meaning a neural network with a low number of neurons and a fuzzy inference system with a small rule base. This was done to ensure that the created classifier could be implemented in control units with limited computational power in small affordable UAVs. The functionality of the designed system was validated by several experimental flights using a small fixed-wing UAV. To evaluate the validity of the proposed system, a set of special maneuvers was performed during test flights. It was found that even a simple feedforward artificial neural network could classify basic flight phases with very high accuracy and a limited set of three input parameters.

Keyword : flight phase, classification, fuzzy logic, artificial neural network, unmanned aerial vehicles

How to Cite
Leško, J., Andoga, R., Bréda, R., Hlinková, M., & Fözö, L. (2023). Flight phase classification for small unmanned aerial vehicles. Aviation, 27(2), 75–85. https://doi.org/10.3846/aviation.2023.18909
Published in Issue
May 5, 2023
Abstract Views
82
PDF Downloads
71
Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

References

Basheer, I. A., & Hajmeer, M. N. Artificial neural networks: Fundamentals, computing, design, and application. Journal of Microbiological Methods, 43(1), 3–31. https://doi.org/10.1016/S0167-7012(00)00201-3

Chin, H. J. H., Payan, A., Johnson, C., & Mavris, D. N. (2019). Phases of flight identification for rotorcraft operations. In AIAA Scitech 2019 Forum. San Diego, California. https://doi.org/10.2514/6.2019-0139

Donato, P. F., Balachandran, S., McDonough, K., Atkins, E. M., & Kolmanovsky, I. V. (2017). Envelope-aware flight management for loss of control prevention given rudder jam. Journal of Guidance Control and Dynamics, 40(4), 1027–1041. https://doi.org/10.2514/1.G000252

Gill, S. J., Lowenberg, M. H., Neild S. A., Krauskopf, B., Puyou, G., & Goetzee, E. (2013). Upset dynamics of an airliner model: A nonlinear bifurcation analysis. Journal of Aircraft, 50(6), 1832–1842. https://doi.org/10.2514/1.C032221

InvenSense. (2013). MPU-6000 and MPU-6050 product specification revision 3.4. https://invensense.tdk.com/wp-content/uploads/2015/02/MPU-6000-Datasheet1.pdf

Kaleva, O. (1987). Fuzzy differential equations. Fuzzy Sets and Systems, 24(3), 301–317. https://doi.org/10.1016/0165-0114(87)90029-7

Kanzow, Ch., Yamashita, N., & Fukushima, M. (2004). Levenberg–Marquardt methods with strong local convergence properties for solving nonlinear equations with convex constraints. Journal of Computational and Applied Mathematics, 172(2), 375–397. https://doi.org/10.1016/j.cam.2004.02.013

Kim, D., Seth, A., & Liem, R. P. (2022). Data-enhanced dynamic flight simulations for flight performance analysis. Aerospace Science and Technology, 121, 107357. https://doi.org/10.1016/j.ast.2022.107357

Kovarik, S., Doherty, L., Korah, K., Mulligan, B., Rasool, G., Mehta, Y., Bhavsar, P., & Paglione, M. (2020). Comparative analysis of machine learning and statistical methods for aircraft phase of flight prediction. In The 9th International Conference on Research in Air Transportation. National Science Foundation.

Klir, G. J., & Yuan, B. (1995). Fuzzy sets and fuzzy logic – theory and applications. Choice Reviews. https://doi.org/10.5860/CHOICE.33-2786

Kurdel, P., Češkovič, M., Gecejová, N., Adamčík, F., & Gamcová, M. (2022). Local control of unmanned air vehicles in the mountain area. Drones, 6(2), 54. https://doi.org/10.3390/drones6020054

Lassak, M., Draganova, K., Blistanova, M., Kalapos, G., & Miklos, J. (2020). Small UAV camera gimbal stabilization using digital filters and enhanced control algorithms for aerial survey and monitoring. Acta Montanistica Slovaca, 25(1), 127–137. https://doi.org/10.46544/AMS.v25i1.12

Leško, J., Schreiner, M., Megyesi, D., & Kovács, L. (2019, 28–29 November). Pixhawk PX-4 autopilot in control of a small unmanned airplane. In 2019 Modern Safety Technologies in Transportation (MOSATT). Kosice, Slovakia. https://doi.org/10.1109/MOSATT48908.2019.8944101

Liu, D., Xiao, N., Zhang, Y., Peng, X. (2020). Unsupervised flight phase recognition with flight data clustering based on GMM. In 2020 IEEE International Instrumentation and Measurement Technology Conference (I2MTC) (pp. 1–6). IEEE. https://doi.org/10.1109/I2MTC43012.2020.9128596

Lombaerts, T., Looye, G., Seefried, A., Neves, M., & Bellmann, T. (2016). Development and concept demonstration of a physics based adaptive flight envelope protection algorithm. IFAC-PapersOnLine, 49(5), 248–253. https://doi.org/10.1016/j.ifacol.2016.07.121

Lombaerts, T., Looye, G., Seefried, A., Neves, M., & Bellmann, T. (2018). Proof of concept simulator demonstration of a physics based self-preserving flight envelope protection algorithm. Engineering Applications of Artificial Intelligence, 67, 368–380. https://doi.org/10.1016/j.engappai.2017.08.014

Measurement specialties. (2013). MS4525DO. https://www.mouser.com/datasheet/2/418/MS4525DO-710170.pdf

Measurement specialties. (2012). MS5611. https://datasheetspdf.com/pdf-file/921406/measurement/MS5611-01BA03/1

Norouzi, R., Kosari, A., & Sabour, M. H. (2019). Real time estimation of impaired aircraft flight envelope using feedforward neural networks. Aerospace Science and Technology, 90, 434–451. https://doi.org/10.1016/j.ast.2019.04.048

Olson, D. L., & Delen, D. (2008). Advanced data mining techniques (1st ed.). Springer.

Paglione, M., & Oaks, R. (2006). Determination of horizontal and vertical phase of flight in recorded air traffic data. In AIAA Guidance, Navigation, and Control Conference and Exhibit. Aerospace Research Central. https://doi.org/10.2514/6.2006-6772

Perhinschi, M. G., Moncayo, H., & Davis, J. (2010). Integrated framework for artificial immunity-based aircraft failure detection, identification, and evaluation. Journal of Aircraft, 47(6), 1847–1859. https://doi.org/10.2514/1.45718

Schuet, S., Lombaerts, T., Acosta, D., Kaneshige, J., Wheeler, K., & Shish, K. (2016). Autonomous flight envelope estimation for loss-of-control prevention. Journal of Guidance Control and Dynamics, 40(4), 847–862. https://doi.org/10.2514/1.G001729

Shin, H., & Kim, Y. (2016). Flight envelope protection of aircraft using adaptive neural network and online linearisation. International Journal of Systems Science, 47(4), 868–885. https://doi.org/10.1080/00207721.2014.906769

Tang, L., Roemer, M. J., Ge, J., Crassidis, A. L., Prasad, J. V., & Belcastro, C. M. (2009, 10–13 August). Methodologies for adaptive flight envelope estimation and protection. In Proceedings of the AIAA Guidance, Navigation, and Control Conference. Chicago, Illinois. https://doi.org/10.2514/6.2009-6260

Tian, F., Cheng, X., Meng, G., & Xu, Y. (2017). Research on flight phase division based on decision tree classifier. In The 2nd IEEE International Conference on Computational Intelligence and Applications (ICCIA) (pp. 372–375). IEEE. https://doi.org/10.1109/CIAPP.2017.8167242

Wang, D., Wu, D., He, J., & Gu, H. (2019). A study of EEG-based flight phase recognition. In 2019 IEEE 1st International Conference on Civil Aviation Safety and Information Technology (ICCASIT) (pp. 640–645). IEEE. https://doi.org/10.1109/ICCASIT48058.2019.8972996

Wilborn, J. E., & Foster, J. V. (2004, 16–19 August). Defining commercial transport loss-of-control: A quantitative approach. In AIAA Atmospheric Flight Mechanics Conference and Exhibit. Providence, Rhode Island. https://doi.org/10.2514/6.2004-4811

Zhang, Q., Mott, J. H., Johnson, M. E., & Springer, J. A. (2022). Development of a reliable method for general aviation flight phase identification. In IEEE Transactions on Intelligent Transportation Systems, 23(8), 11729–11738. https://doi.org/10.1109/TITS.2021.3106774