Nothing Special   »   [go: up one dir, main page]
Skip to main content

Advertisement

Springer Nature Link
Log in

Small unmanned helicopter modeling method based on a hybrid kernel function PSO-LSSVM

  • Published:
The Journal of Supercomputing Aims and scope Submit manuscript

Abstract

The mathematical modeling of a small unmanned helicopter (SUH) with multivariable, highly nonlinear and complex dynamic characteristics is considered. This paper presents a modeling method for SUHs based on a particle swarm optimization least squares support vector machine (PSO-LSSVM) with a hybrid kernel function. The proposed method is based on a least square support vector machine and uses linear weighting of the polynomial kernel function (POLY) and Gaussian kernel function (RBF) to form a hybrid kernel function, and uses a particle swarm optimization algorithm to search for the optimal parameters. Finally, a mathematical model of the longitudinal and lateral passages of a SUH is established. According to the flight test data, the longitudinal and lateral channel models are trained and verified in the hover and low-speed forward flight states of a SUH. The experimental and comparison results demonstrate that the model established via this method has higher prediction accuracy and more accurate prediction results than a model established using a least squares support vector machine with a single kernel function. The identification accuracy of the SUH model is improved effectively.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

Data availability

This declaration is not applicable.

References

  1. Lu Y, Chang ZF, Ye NJ (2021) Development and dynamics of a 2SPU+UPU+SP parallel rotor of helicopter. Aerosp Sci Technol 118:107066. https://doi.org/10.1016/j.ast.2021.107066

    Article  Google Scholar 

  2. Gao H, Shi H, Zou X (2022) Flight dynamics modeling and control of a new type high-speed helicopter in take-off and landing. In: 2022 International Conference on Unmanned Aircraft Systems, pp 696–704. https://doi.org/10.1109/ICUAS54217.2022.9836203

  3. Zhao D, Yang H, Giuseppe C et al (2021) Modeling and analysis of landing collision dynamics for a shipborne helicopter. Front Mech Eng 16:151–162. https://doi.org/10.1007/s11465-020-0617-z

    Article  Google Scholar 

  4. Cui C, Shi Y, Wu K, Sheng S (2023) Research on attitude control of unmanned helicopter with slung Load combined input shaper and linear active disturbance rejection control. Adv Guid Navig Control 845:5158–5168. https://doi.org/10.1007/978-981-19-6613-2_498

    Article  Google Scholar 

  5. Civita ML, Papageorgiou G, Messner WC, Kanade T (2003) Integrated modeling and robust control for full-envelope flight of robotic helicopters. In: IEEE International Conference on Robotics and Automation, vol 1. IEEE, pp 552–557. https://doi.org/10.1109/ROBOT.2003.1241652

  6. Gao TY, Wang DD, Tao F, Ge HL (2014) Attitude model identification for sub-mini dual ducted UAV under hovering. Appl Mech Mater 513–517:2812–2815. https://doi.org/10.4028/www.scientific.net/AMM.513-517.2812

    Article  Google Scholar 

  7. Chang S, Bang H, Lee W, Park B (2013) Dynamic modeling and fuzzy control for a small tiltrotor unmanned aerial vehicle. Proc Inst Mech Eng Part G J Aerosp Eng 227:1468–1487. https://doi.org/10.1177/0954410012457620

    Article  Google Scholar 

  8. Bhandari S, Colgren R (2015) High-order dynamics models of a small UAV helicopter using analytical and parameter identification techniques. J Am Helicopter Soc 60:1–10. https://doi.org/10.4050/JAHS.60.022012

    Article  Google Scholar 

  9. Liu Z et al (2021) System identification based on generalized orthonormal basis function for unmanned helicopters: a reinforcement learning approach. IEEE Trans Veh Technol 70:1135–1145. https://doi.org/10.1109/TVT.2021.3051696

    Article  Google Scholar 

  10. Khalesi MH, Salarieh H, Saadat Foumani M (2020) System identification and robust attitude control of an unmanned helicopter using novel low-cost flight control system. Proc Inst Mech Eng Part I J Syst Control Eng 234:634–645. https://doi.org/10.1177/0959651819869718

    Article  Google Scholar 

  11. Christina M, Elizabeth S, Martin J, Mark JS, Mark B (2021) System identification guidance for multirotor aircraft: dynamic scaling and test techniques. J Am Helicopter Soc 66:1–16. https://doi.org/10.4050/JAHS.66.022006

    Article  Google Scholar 

  12. Zhou J, Wang G, Hong L (2019) A parameter identification method for dynamic model of a small unmanned helicopter. Flight Dyn 37:89–96

    Google Scholar 

  13. Secco NR, Mattos BS (2017) Artificial neural net-works to predict aerodynamic coefficients of transport airplanes. Aircr Eng Aerosp Technol 89:211–230. https://doi.org/10.1108/AEAT-05-2014-0069

    Article  Google Scholar 

  14. Sarotama A, Kusumoputro B (2013) System identification of UAV alap-alap using back propagation neural network. Appl Mech Mater 373–375:1212–1219. https://doi.org/10.4028/www.scientific.net/AMM.373-375.1212

    Article  Google Scholar 

  15. Baskın M, Leblebicioğlu K (2023) Frequency-domain estimation of a transfer matrix of an uncommon quadrotor in hover. IEEE Trans Control Syst Technol 31:555–569. https://doi.org/10.1109/TCST.2022.3185936

    Article  Google Scholar 

  16. Tijani IB, Akmeliawati R, Legowo A (2014) Hybrid DE-PEM algorithm for identification of UAV helicopter. Aircr Eng Aerosp Technol 86:385–405. https://doi.org/10.1108/AEAT-11-2012-0226

    Article  Google Scholar 

  17. Dai J, Nie H, Ying J et al (2020) Modeling and tracking control of unmanned helicopter. In: 2020 IEEE 6th International Conference on Control Science and Systems Engineering, pp 149–155. https://doi.org/10.1109/ICCSSE50399.2020.9171959

  18. Qi XM, Silvestrov S, Nazir T (2017) Data classification with support vector machine and generalized support vector machine. AIP Conf Proc 1798:1. https://doi.org/10.1063/1.4972718

    Article  Google Scholar 

  19. Huang WC, Liu HY et al (2021) Railway dangerous goods transportation system risk identification: comparisons among SVM, PSO-SVM. GA-SVM and GS-SVM 109:107541. https://doi.org/10.1016/j.asoc.2021.107541

    Article  Google Scholar 

  20. Nedumaran A, Babu RG, Kassa MM, Karthika P (2020) Machine level classification using support vector machine. AIP Conf Proc 2207:1. https://doi.org/10.1063/5.0000041

    Article  Google Scholar 

  21. Bhandari S, Chen B, Colgren R (2007) Application of Support Vector Machines to the modeling and Control of a UAV Helicopter. In: Aiaa Modeling and Simulation Technologies Conference and Exhibit, pp 20–23. https://doi.org/10.2514/6.2007-6708

  22. Wang J, Shao W, Kim J (2020) Combining MF-DFA and LSSVM for retina images classification. Biomed Signal Process Control 60:101943. https://doi.org/10.1016/j.bspc.2020.101943

    Article  Google Scholar 

  23. Fang Z, Li P, Han B (2009) Modeling hover dynamics of small-scale unmanned helicopter based on least square support vector machine. Acta Aeronaut et Astronaut Sin 30:1508–1514

    Google Scholar 

  24. Kulamala VK, Kumar L, Mohapatra DP (2021) Software fault prediction using LSSVM with different kernel functions. Rock Soil Mech 46:8655–8664. https://doi.org/10.1007/s13369-021-05643-2

    Article  Google Scholar 

  25. Lua CA, Garcia CCV et al (2020) Real-time hovering control of unmanned aerial vehicles. Math Probl Eng 2020:1–8. https://doi.org/10.1155/2020/2314356

    Article  MathSciNet  MATH  Google Scholar 

  26. Abdelmaksoud SI, Mailah M, Abdallah AM (2020) Control strategies and novel techniques for autonomous rotorcraft unmanned aerial vehicles: a review. IEEE Access 8:195142–195169. https://doi.org/10.1109/ACCESS.2020.3031326

    Article  Google Scholar 

  27. Ma WL, Liu H (2021) Least squares support vector machine regression based on sparse samples and mixture kernel learning. Inf Technol Control 50:319–331. https://doi.org/10.5755/j01.itc.50.2.27752

    Article  Google Scholar 

  28. Zeng F, Amar MN, Mohammed AS, Motahari MR (2021) Improving the performance of LSSVM model in predicting the safety factor for circular failure slope through optimization algorithms. Eng Comput. https://doi.org/10.1007/s00366-021-01374-y

    Article  Google Scholar 

  29. Wu YY, Xue W, Xu L, Guo X (2020) Optimized least-squares support vector machine for predicting aero-optic imaging deviation based on chaotic particle swarm optimization. Optik 206:12. https://doi.org/10.1016/j.ijleo.2019.163215

    Article  Google Scholar 

  30. Li JC, Sun LP (2020) Forecasting of wood moisture content based on modified ant colony algorithm to optimize LSSVM parameters IEEE. Access 8:85116–85127. https://doi.org/10.1109/ACCESS.2020.2991889

    Article  Google Scholar 

  31. Xu DX, Hu AY, Han XL, Zhang L (2021) A nonlinear system state estimation method based on adaptive fusion of multiple kernel functions. Complexity. https://doi.org/10.1155/2021/5124841

    Article  Google Scholar 

  32. Li LM, Cheng SK, Wen ZZ (2021) Landslide prediction based on improved principal component analysis and mixed kernel function least squares support vector regression model. J Mt Sci 18:2130–2142. https://doi.org/10.1007/s11629-020-6396-5

    Article  Google Scholar 

Download references

Funding

No funds.

Author information

Author notes

  1. Weixin Wang, Jian Lu and Lingzhe Liu have contributed equally to this work.

Authors and Affiliations

  1. School of Electronics and Information, Xi’an Polytechnic University, Xi’an, 710048, Shaanxi Province, China

    Jian Zhou, Weixin Wang, Jian Lu & Lingzhe Liu

Authors
  1. Jian Zhou
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Weixin Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jian Lu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Lingzhe Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Jian Zhou and Weixin Wang wrote the manuscript text, Jian Zhou prepared Figs. 1, 2 and 3, and Jian Lu and Lingzhe Liu prepared Figs. 4, 5, 6, 7, 8 and 9. All authors reviewed the manuscript.

Corresponding author

Correspondence to Jian Zhou.

Ethics declarations

Conflict of interest

The authors declare that they have no competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Ethical approval

This declaration is not applicable.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhou, J., Wang, W., Lu, J. et al. Small unmanned helicopter modeling method based on a hybrid kernel function PSO-LSSVM. J Supercomput 79, 13889–13906 (2023). https://doi.org/10.1007/s11227-023-05211-5

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11227-023-05211-5

Keywords

Search

Navigation