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Adaptive Sliding Mode Trajectory Tracking Control of Quadrotor UAV with Unknown Control Direction

  • Lijun WangEmail author
  • Wencong Deng
  • Jinkun Liu
  • Rong Mei
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 582)

Abstract

For quadrotor unmanned aerial vehicle(UAV) with unknown control direction, the Nussbaum gain method is introduced into adaptive sliding mode method to control the position and attitude of the quadrotor UAV. By decomposing the quadrotor UAV system into position subsystem and attitude subsystem, the intermediate control input is introduced to track the 3-DOF position information. The Nussbaum gain function is used to solve the problem of unknown control direction, and an adaptive law is designed to ensure the boundedness of all signals. Based on the Lyapunov theory, the stability of the closed-loop system is guaranteed. Finally the effectiveness of the proposed control method is verified by the simulation results.

Keywords

Quadrotor UAV Unknown control direction Trajectory tracking control Adaptive sliding mode control Nussbaum gain function 

Notes

Acknowledgements

This work was supported by the National Natural Science Foundation of China [grant number 61873296] and Beijing Key Disciplines to Build Projects [grant number XK100080537].

References

  1. 1.
    Cole, D.T., Sukkarieh, S., Göktoğan, A.H.: System development and demonstration of a uav control architecture for information gathering missions. J. Field Robot. 23(6–7), 417–440 (2006)CrossRefGoogle Scholar
  2. 2.
    Zuo, Z.: Trajectory tracking control design with command-filtered compensation for a quadrotor. IET Control. Theory Appl. 4(11), 2343–2355 (2010)MathSciNetCrossRefGoogle Scholar
  3. 3.
    Gautam, D., Ha, C.: Control of a quadrotor using a smart self-tuning fuzzy pid controller. Int. J. Adv. Robot. Syst. 10(11), 380 (2013)CrossRefGoogle Scholar
  4. 4.
    Erginer, B., Altug, E.: Modeling and pd control of a quadrotor vtol vehicle. In: 2007 IEEE Intelligent Vehicles Symposium. pp. 894–899. IEEE (2007)Google Scholar
  5. 5.
    Huang, M., Xian, B., Diao, C., Yang, K., Feng, Y.: Adaptive tracking control of underactuated quadrotor unmanned aerial vehicles via backstepping. In: Proceedings of the 2010 American Control Conference. pp. 2076–2081. IEEE (2010)Google Scholar
  6. 6.
    Wang, R., Liu, J.: Trajectory tracking control of a 6-dof quadrotor uav with input saturation via backstepping. J. Frankl. Inst. 355(7), 3288–3309 (2018)MathSciNetCrossRefGoogle Scholar
  7. 7.
    Johnson, Y., Ahamed, T.I.: Nonlinear modelling of leader-follower uav close formation flight with dynamic inversion-based control. Int. J. Model. Identif. Control. 30(2), 83–92 (2018)CrossRefGoogle Scholar
  8. 8.
    Aguiar, A.P., Hespanha, J.P.: Trajectory-tracking and path-following of underactuated autonomous vehicles with parametric modeling uncertainty. IEEE Trans. Autom. Control. 52(8), 1362–1379 (2007)MathSciNetCrossRefGoogle Scholar
  9. 9.
    Abdessameud, A., Tayebi, A.: Global trajectory tracking control of vtol-uavs without linear velocity measurements. Automatica 46(6), 1053–1059 (2010)MathSciNetCrossRefGoogle Scholar
  10. 10.
    Wang, X., Liu, J., Cai, K.Y.: Tracking control for a velocity-sensorless vtol aircraft with delayed outputs. Automatica 45(12), 2876–2882 (2009)MathSciNetCrossRefGoogle Scholar
  11. 11.
    Wang, R., Liu, J.: Adaptive formation control of quadrotor unmanned aerial vehicles with bounded control thrust. Chin. J. Aeronaut. 30(2), 807–817 (2017)CrossRefGoogle Scholar
  12. 12.
    Dinh, T.X., Ahn, K.K.: Int. J. Precis. Eng. Manuf. International Journal of Precision Engineering and Manufacturing 18(2), 163–173 (2017)CrossRefGoogle Scholar
  13. 13.
    Jasim, W., Gu, D.: Robust path tracking control for quadrotors with experimental validation. Int. J. Model. Identif. Control. 29(1), 1–13 (2018)CrossRefGoogle Scholar
  14. 14.
    Boskovic, J.D., Bergstrom, S., Mehra, R.K.: Robust integrated flight control design under failures, damage, and state-dependent disturbances. J. Guid. Control. Dyn. 28(5), 902–917 (2005)CrossRefGoogle Scholar
  15. 15.
    Nussbaum, R.D.: Some remarks on a conjecture in parameter adaptive control. Syst. Control. Lett. 3(5), 243–246 (1983)MathSciNetCrossRefGoogle Scholar
  16. 16.
    Oliveira, T.R., Hsu, L., Peixoto, A.J.: Output-feedback global tracking for unknown control direction plants with application to extremum-seeking control. Automatica 47(9), 2029–2038 (2011)MathSciNetCrossRefGoogle Scholar
  17. 17.
    Ma, J., Zheng, Z., Li, P.: Adaptive dynamic surface control of a class of nonlinear systems with unknown direction control gains and input saturation. IEEE Trans. Cybern. 45(4), 728–741 (2014)CrossRefGoogle Scholar
  18. 18.
    Ma, H., Liang, H., Zhou, Q., Ahn, C.K.: Adaptive dynamic surface control design for uncertain nonlinear strict-feedback systems with unknown control direction and disturbances. IEEE Trans. Syst. Man Cybern. Syst. 99, 1–10 (2018)Google Scholar
  19. 19.
    Ge, S.S., Hong, F., Lee, T.H.: Adaptive neural control of nonlinear time-delay systems with unknown virtual control coefficients. IEEE Trans. Syst. Man Cybern. Part B (Cybern.) 34(1), 499–516 (2004)CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • Lijun Wang
    • 1
    • 2
    Email author
  • Wencong Deng
    • 1
    • 2
  • Jinkun Liu
    • 3
  • Rong Mei
    • 1
    • 2
  1. 1.School of Automation and Electrical EngineeringUniversity of Science and Technology BeijingBeijingChina
  2. 2.Key Laboratory of Knowledge Automation for Industrial ProcessesMinistry of Education, University of Science and Technology BeijingBeijingChina
  3. 3.School of Automation Science and Electrical EngineeringBeihang UniversityBeijingChina

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