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Small unmanned helicopter system identification based on the weighted least square method and improved grey wolf optimisation algorithm

Published online by Cambridge University Press:  03 February 2025

SY. Liu Open the ORCID record for SY. Liu [Opens in a new window] ,
J. Zhou Open the ORCID record for J. Zhou [Opens in a new window] ,
JY. Shi Open the ORCID record for JY. Shi [Opens in a new window]  and
J. Lu Open the ORCID record for J. Lu [Opens in a new window]
SY. Liu
Affiliation:
School of Electronics and Information, Xi’an Polytechnic University, Xi’an, Shanxi, China
J. Zhou*
Affiliation:
School of Electronics and Information, Xi’an Polytechnic University, Xi’an, Shanxi, China
JY. Shi
Affiliation:
School of Electronics and Information, Xi’an Polytechnic University, Xi’an, Shanxi, China
J. Lu
Affiliation:
School of Electronics and Information, Xi’an Polytechnic University, Xi’an, Shanxi, China
*
Corresponding author: J. Zhou; Email: zhoujian0627@163.com
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Abstract

Aiming to address the issue of low accuracy in model predictions obtained from fitting frequency domain response curves for small unmanned helicopters during the process of modeling their flight dynamics, this study proposes a system identification algorithm based on the combination of weighted least squares and improved grey wolf optimisation algorithm. The algorithm utilises the weighted least squares method to obtain the initial model structure, optimises the initial model parameters using the improved grey wolf optimisation algorithm, and enhances the local search and global optimisation ability of the grey wolf optimisation algorithm by introducing an improved grey wolf subgrouping rule, nonlinear convergence factor and dynamic cooperative rule. Ultimately, this approach establishes a dynamic model for small, unmanned helicopters. The identified model is validated using flight test data, with findings demonstrating that this method achieves higher accuracy in model identification and better fits to frequency domain response curves, thus providing a more accurate reflection of the flight dynamics of small unmanned helicopters.

Keywords

small unmanned helicoptersystem identificationweighted least squaresimproved grey wolf optimisation algorithm

Information

Type
Research Article
Information
The Aeronautical Journal , Volume 129 , Issue 1337 , July 2025 , pp. 1886 - 1902
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Copyright
© The Author(s), 2025. Published by Cambridge University Press on behalf of Royal Aeronautical Society

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References

Leshikar, C., Valasek, J. and McQuinn, C.-K. System identification of unmanned air systems at Texas A&M University, J. Aircraft, 2023, 60, (5), pp 1437–1460.CrossRefGoogle Scholar
Hosseini, B., Steinert, A., Hofmann, R., Fang, X., Steffensen, R., Holzapfel, F. and Göttlicher, C. Advancements in the theory and practice of flight vehicle system identification, J. Aircraft, 2023, 60, (5), pp 1419–1436.CrossRefGoogle Scholar
Venkataraman, R. and Seiler, P. System identification for a small, rudderless, fixed-wing unmanned aircraft, J. Aircraft, 2019, 56, (3), pp 1126–1134.CrossRefGoogle Scholar
Kapeel, E.H., Safwat, E., Kamel, A.M., Khalil, M.K., Elhalwagy, Y.Z. and Hendy, H. Physical modeling, simulation and validation of small fixed-wing UAV, Unmanned Syst., 2023, 11, (04), pp 327–350.CrossRefGoogle Scholar
Dayhoum, A., Zakaria, M.Y. and Abdelhamid, O.E. Experimental investigation for a small helicopter in hovering and forward flight regimes, J. Aerospace Eng., 2023, 36, (4), p 06023001.CrossRefGoogle Scholar
Tischler, M.B., Scepanovic, P., Gubbels, A., et al. Bell 412 system identification and model fidelity assessment for hover and forward flight, J. Am. Helicopter Soc., 2021, 66, (1), pp 1–13.Google Scholar
Hoshu, A.A., Wang, L., Ansari, S., Sattar, A. and Bilal, M.H.A. System identification of heterogeneous multirotor unmanned aerial vehicle, Drones, 2022, 6, (10), p 309.CrossRefGoogle Scholar
Maleki, K.N., Karimi, S., Mohammadi, S. and Ashenayi, K. System identification and modeling of a multirotor UAV: A comparative study, AIAA SCITECH 2024 Forum, 2024, p 0567.Google Scholar
Huang, L., Pei, H. and Cheng, Z. System identification and improved internal model control for yaw of unmanned helicopter, Asian J. Control, 2023, 25, (2), pp 1619–1638.CrossRefGoogle Scholar
Xia, H. Modeling and control strategy of small unmanned helicopter rotation based on deep learning, Syst. Soft Comput., 2024, 6, p 200146.CrossRefGoogle Scholar
Steen, C. System identification of a multirotor UAV using a prediction error method, 2024.Google Scholar
Tischler, M.B. System identification methods for aircraft flight control development and validation, Advances in Aircraft Flight Control, 2018, pp 35–69.CrossRefGoogle Scholar
Geluardi, S., Nieuwenhuizen, F.M., Venrooij, J., Pollini, L. and Bülthoff, H.H. Frequency domain system identification of a robinson r44 in hover, J. Am. Helicopter Soc., 2018, 63, (1), pp 1–18.Google Scholar
TK, K.N., VP, L. and Singh, J. System identification of flybar-less rotorcraft UAV, Aircraft Eng. Aerospace Technol., 2020, 92, (10), pp 1483–1493.Google Scholar
Bauer, P. and Nagy, M. Flight-data-based high-fidelity system identification of DJI M600 pro hexacopter, Aerospace, 2024, 11, (1), p 79.CrossRefGoogle Scholar
Jianhong, W. and Ramirez-Mendoza, R.A. Synthesis cascade estimation for aircraft system identification, Aircraft Eng. Aerospace Technol., 2023, 95, (1), pp 73–84.CrossRefGoogle Scholar
Juhasz, O., Celi, R. and Tischler, M.B. Flight dynamics simulation modeling of a large flexible tiltrotor aircraft, J. Am. Helicopter Soc., 2022, 67, (2), pp 1–16.CrossRefGoogle Scholar
Babcock, J.T. System identification of an s500 quadrotor UAV. Department of Aeronautics, Defense Technical Information Center, 2023.Google Scholar
Chen, T., Zhang, X., Wang, C., Yu, X., Wang, S. and Chen, X. Domain adversarial neural network-based nonlinear system identification for helicopter transmission system, Nonlinear Dyn., 2023, 111, (16), pp 14695–14711.CrossRefGoogle Scholar
Liu, J., Wei, X. and Huang, H. An improved grey wolf optimization algorithm and its application in path planning, IEEE Access, 2021, 9, pp 121944–121956.CrossRefGoogle Scholar
Xiaolin, L.W.Q., Gang, L. and Guohua, Z. Overview of cluster intelligence algorithms, Unmanned Syst. Technol., 2021, 4, (3), pp 1–10.Google Scholar
Li, Y., Lin, X. and Liu, J. An improved gray wolf optimization algorithm to solve engineering problems, Sustainability, 2021, 13, (6), p 3208.CrossRefGoogle Scholar
Berger, T., Tobias, E.L., Tischler, M.B. and Juhasz, O. Advances and modern applications of frequency-domain aircraft and rotorcraft system identification, J. Aircraft, 2023, 60, (5), pp 1331–1353.CrossRefGoogle Scholar
Nadimi-Shahraki, M.H., Taghian, S. and Mirjalili, S. An improved grey wolf optimizer for solving engineering problems, Expert Syst. Appl., 2021, 166, p 113917.CrossRefGoogle Scholar
Luo, J., He, F. and Gao, X. An enhanced grey wolf optimizer with fusion strategies for identifying the parameters of photovoltaic models, Integr. Comput.-Aided Eng., 2023, 30, (1), pp 89–104.CrossRefGoogle Scholar
Ou, Y., Yin, P. and Mo, L. An improved Grey Wolf optimizer and its application in robot path planning, Biomimetics, 2023, 8, (1), p 84.CrossRefGoogle ScholarPubMed
Qiu, Y., Yang, X. and Chen, S. An improved gray wolf optimization algorithm solving to functional optimization and engineering design problems, Sci. Rep., 2024, 14, (1), p 14190.CrossRefGoogle ScholarPubMed
Altay, O. and Altay, E.V. A novel hybrid multilayer perceptron neural network with improved grey wolf optimizer, Neural Comput. Appl., 2023, 35, (1), pp 529–556.CrossRefGoogle Scholar