Advertisement

Nonlinear Dynamics

, Volume 96, Issue 4, pp 2307–2326 | Cite as

Nonlinear system identification and trajectory tracking control for a flybarless unmanned helicopter: theory and experiment

  • Bin ZhouEmail author
  • Xingju Lu
  • Shuai Tang
  • Zhiqiang Zheng
Original Paper
  • 270 Downloads

Abstract

This paper proposes a new nonlinear system identification method in time domain for a small-scale flybarless unmanned helicopter based on an adaptive differential evolution algorithm. By analyzing and processing the real input and output data, a nonlinear parametric model including heave dynamic model, yaw dynamic model and longitudinal–latitudinal subsystem is established. The validity of the identified model is confirmed through comparing the output of the mathematical model with the actual flight response under the same control input. In addition, a power-based identification strategy is propounded to reduce the difficulty of data acquisition and improve the efficiency of the identification process. Moreover, the trajectory tracking controller is designed and implemented based on the identified model using the backstepping control technology. The actual autonomous flight experimental results further demonstrate the accuracy of the identified nonlinear model and the effectiveness of the proposed control method.

Keywords

Small-scale flybarless unmanned helicopter Nonlinear system identification Differential evolution algorithm Trajectory tracking control 

Notes

Acknowledgements

This work was supported in part by the National Natural Science Foundation of China (Grant No. 61603407) and the Basic and Advanced Research Project of ChongQing (Grant No. cstc2016jcyjA0563).

The authors of this paper owe great thanks to Dr. Peng Li and Mr. Xuanying Li for their disinterested assistance in outdoor experiments.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Human and animal rights

This article does not contain any studies with human participants or animals performed by any of the authors.

Informed consent

Informed consent was obtained from all individual participants included in the study.

References

  1. 1.
    Abas, N., Legowo, A., Ibrahim, Z., Rahim, N., Kassim, A.M.: Modeling and system identification using extended kalman filter for a quadrotor system. Appl. Mech. Mater. 313, 976–981 (2013)CrossRefGoogle Scholar
  2. 2.
    Bergamasco, M., Lovera, M.: Identification of linear models for the dynamics of a hovering quadrotor. IEEE Trans. Control Syst. Technol. 22(5), 1696–1707 (2014)CrossRefGoogle Scholar
  3. 3.
    Bian, Q., Zhao, K., Wang, X., Xie, R.: System identification method for small unmanned helicopter based on improved particle swarm optimization. J. Bionic Eng. 13(3), 504–514 (2016)CrossRefGoogle Scholar
  4. 4.
    Bolourchi, A., Masri, S.F., Aldraihem, O.J.: Development and application of computational intelligence approaches for the identification of complex nonlinear systems. Nonlinear Dyn. 79(2), 765–786 (2015)CrossRefGoogle Scholar
  5. 5.
    Cai, G., Chen, B.M., Dong, X., Lee, T.H.: Design and implementation of a robust and nonlinear flight control system for an unmanned helicopter. Mechatronics 21(5), 803–820 (2011)CrossRefGoogle Scholar
  6. 6.
    Cai, G., Chen, B.M., Peng, K., Dong, M., Lee, T.H.: Modeling and control of the yaw channel of a UAV helicopter. IEEE Trans. Ind. Electron. 55(9), 3426–3434 (2008)CrossRefGoogle Scholar
  7. 7.
    Cai, G., Chen, B.M., Tong, H.L., Kai, Y.L.: Comprehensive nonlinear modeling of an unmanned-aerial-vehicle helicopter. In: AIAA Guidance, Navigation and Control Conference and Exhibit (2008)Google Scholar
  8. 8.
    Casbeer, D.W., Kingston, D.B., Beard, R.W., McLain, T.W.: Cooperative forest fire surveillance using a team of small unmanned air vehicles. Int. J. Syst. Sci. 37(6), 351–360 (2006)CrossRefzbMATHGoogle Scholar
  9. 9.
    Dalei, S., Juntong, Q., Jianda, H.: Model identification and active modeling control for small-size unmanned helicopters: theory and experiment. In: AIAA Guidance, Navigation, and Control Conference (2010)Google Scholar
  10. 10.
    Ding, L., Wu, H., Yao, Y.: Chaotic artificial bee colony algorithm for system identification of a small-scale unmanned helicopter. Int. J. Aerosp. Eng. 2015, 1–15 (2015)CrossRefGoogle Scholar
  11. 11.
    Dorobantu, A., Murch, A., Mettler, B., Balas, G.: System identification for small, low-cost, fixed-wing unmanned aircraft. J. Aircr. 50(4), 1117–1130 (2013)CrossRefGoogle Scholar
  12. 12.
    Gurtner, A., Greer, D.G., Glassock, R., Mejias, L., Walker, R.A., Boles, W.W.: Investigation of fish-eye lenses for small-UAV aerial photography. IEEE Trans. Geosci. Remote Sens. 47(3), 709–721 (2009)CrossRefGoogle Scholar
  13. 13.
    Hausamann, D., Zirnig, W., Schreier, G., Strobl, P.: Monitoring of gas pipelines—a civil UAV application. Aircr. Eng. Aerosp. Technol. 77(5), 352–360 (2005)CrossRefGoogle Scholar
  14. 14.
    Heffley, R.K., Mnich, M.A.: Minimum-complexity helicopter simulation math model. Technical Report NASA-CR-177476, USAAVSCOM-TR-87-A-7, NASA, Moffett Field, CA, USA (1988)Google Scholar
  15. 15.
    Ho, W.H., Chou, J.H., Guo, C.Y.: Parameter identification of chaotic systems using improved differential evolution algorithm. Nonlinear Dyn. 61(1), 29–41 (2010)MathSciNetCrossRefzbMATHGoogle Scholar
  16. 16.
    Jiang, S., Wang, Y., Ji, Z.: A new design method for adaptive IIR system identification using hybrid particle swarm optimization and gravitational search algorithm. Nonlinear Dyn. 79(4), 2553–2576 (2015)MathSciNetCrossRefGoogle Scholar
  17. 17.
    Lei, X., Du, Y.: A linear domain system identification for small unmanned aerial rotorcraft based on adaptive genetic algorithm. J. Bionic Eng. 7(2), 142–149 (2010)CrossRefGoogle Scholar
  18. 18.
    Lei, X., Guo, K.: The model identification for small unmanned aerial rotorcraft based on adaptive ant colony algorithm. J. Bionic Eng. 9(4), 508–514 (2012)CrossRefGoogle Scholar
  19. 19.
    Ljung, L.: System Identification: Theory for the User, 2nd edn. Prentice-Hall, Englewood Cliffs (1999)zbMATHGoogle Scholar
  20. 20.
    Mettler, B.: Identification Modeling and Characteristics of Miniature Rotorcraft. Kluwer Academic Publishers, Norwell (2003)CrossRefGoogle Scholar
  21. 21.
    Mettler, B., Dever, C., Feron, E.: Scaling effects and dynamic characteristics of miniature rotorcraft. J. Guid. Control Dyn. 27(3), 466–478 (2004)CrossRefGoogle Scholar
  22. 22.
    Price, K.V., Storn, R.M., Lampinen, J.A.: Differential Evolution: A Practical Approach to Global Optimization. Springer, New York (2006)zbMATHGoogle Scholar
  23. 23.
    Raptis, I.A., Valavanis, K.P., Moreno, W.A.: System identification and discrete nonlinear control of miniature helicopters using backstepping. J. Intell. Robot. Syst. 55(2), 223–243 (2009)CrossRefzbMATHGoogle Scholar
  24. 24.
    Raptis, I.A., Valavanis, K.P., Moreno, W.A.: A novel nonlinear backstepping controller design for helicopters using the rotation matrix. IEEE Trans. Control Syst. Technol. 19(2), 465–473 (2011)CrossRefGoogle Scholar
  25. 25.
    Storn, R., Price, K.: Differential evolution—a simple and efficient heuristic for global optimization over continuous spaces. J. Glob. Optim. 11(4), 341–359 (1997)MathSciNetCrossRefzbMATHGoogle Scholar
  26. 26.
    Tang, S., Zheng, Z., Qian, S., Zhao, X.: Nonlinear system identification of a small-scale unmanned helicopter. Control Eng. Pract. 25(1), 1–15 (2014)CrossRefGoogle Scholar
  27. 27.
    Wang, F., Cui, J., Chen, B.M., Lee, T.H.: Flight Dynamics Modeling of Coaxial Rotorcraft UAVs, pp. 1217–1256. Springer, Dordrecht (2015)Google Scholar
  28. 28.
    Wang, T., Chen, Y., Liang, J., Wu, Y., Wang, C., Zhang, Y.: Chaos-genetic algorithm for the system identification of a small unmanned helicopter. J. Intell. Robot. Syst. 67(3), 323–338 (2012)CrossRefzbMATHGoogle Scholar
  29. 29.
    Zhou, B., Li, Z., Zheng, Z., Tang, S.: Nonlinear adaptive tracking control for a small-scale unmanned helicopter using a learning algorithm with the least parameters. Nonlinear Dyn. 89(2), 1289–1308 (2017)CrossRefzbMATHGoogle Scholar
  30. 30.
    Zhu, B., Zuo, Z.: Approximate analysis for main rotor flapping dynamics of a model-scaled helicopter with Bell–Hiller stabilizing bar in hovering and vertical flights. Nonlinear Dyn. 85(3), 1705–1717 (2016)CrossRefGoogle Scholar

Copyright information

© Springer Nature B.V. 2019

Authors and Affiliations

  1. 1.College of Artificial IntelligenceNational University of Defense TechnologyChangshaPeople’s Republic of China
  2. 2.Department of Machinery and Electrical EngineeringLogistical Engineering UniversityChongqingPeople’s Republic of China

Personalised recommendations