Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
Skip to main content

Advertisement

Springer Nature Link
Log in

Active model-based nonlinear system identification of quad tilt-rotor UAV with flight experiments

  • Research Paper
  • Published:
Science China Information Sciences Aims and scope Submit manuscript

Abstract

To propose an accurate and less complex model of a special unmanned aerial vehicle (UAV), namely tilt-rotor UAV (TRUAV), this study identifies the active model-based nonlinear system of a quad-TRUAV. First, a nominal nonlinear model of the vehicle is formulated. Some unstructured nonlinearities are ignored to reduce the complexity of the model. Then, due to the unstable dynamics of the open-loop system, the format of this nominal model is considered to design an innovative smooth-switch attitude control via interconnection and damping assignment passivity-based control (IDA-PBC). Active model-based nonlinear system identification is studied for the vehicle with the designed control method and flight experiments. The model error vector is defined and estimated by the unscented Kalman filter (UKF) to improve the accuracy of the model. The main contribution of this paper is to identify the nominal nonlinear model and to develop an active model method of the quad-TRUAV. The attitude control method with an innovative smooth-switch structure is another contribution to flight experiments. Numerical results are displayed to present the experimental results and effectiveness of the proposed nonlinear models.

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

Similar content being viewed by others

References

  1. Liu Z, He Y Q, Yang L Y, et al. Control techniques of tilt rotor unmanned aerial vehicle systems: a review. Chin J Aeronaut, 2017, 30: 135–148

    Article  Google Scholar 

  2. Ferguson S W. A mathematical model for real time flight simulation of a generic tilt-rotor aircraft. NASA CR-166536. 1988

  3. Ye L, Zhang Y, Yang S, et al. Numerical simulation of aerodynamic interaction for a tilt rotor aircraft in helicopter mode. Chin J Aeronaut, 2016, 29: 843–854

    Article  Google Scholar 

  4. Du S L, Wang C, Sun H J, et al. Numerical analysis of aerodynamic interference of rotor/fuselage in transition state of tilting four-rotor UAV. J Nanjing Univ Aeronaut Astronaut, 2018, 50: 179–185

    Google Scholar 

  5. Chen K, Shi Z W, Tong S X, et al. Aerodynamic interference test of quad tilt rotor aircraft in wind tunnel. Proc I Mech Eng Part G-J Aer Eng, 2019, 233: 5553–5566

    Article  Google Scholar 

  6. Papachristos C, Alexis K, Tzes A. Dual-authority thrust-vectoring of a tri-tiltrotor employing model predictive control. J Intell Robot Syst, 2016, 81: 471–504

    Article  Google Scholar 

  7. Cardoso D N, Esteban S, Raffo G V. A nonlinear W∞ controller of a tilt-rotor UAV for trajectory tracking. In: Proceedings of European Control Conference (ECC), Napoli, 2019. 928–934

  8. Yuksek B, Vuruskan A, Ozdemir U, et al. Transition flight modeling of a fixed-wing VTOL UAV. J Intell Robot Syst, 2016, 84: 83–105

    Article  Google Scholar 

  9. Chen C. Research on control method for the transition model of the small tilt-rotor UAV. Dissertation for Ph.D. Degree. Changsha: National University of Defense Technology, 2017

    Google Scholar 

  10. Morelli E A, Klein V. Aircraft System Identification: Theory and Practice. Williamsburg: Sunflyte Enterprises, 2016

    Google Scholar 

  11. Liu Z, Theilliol D, Yang L Y, et al. Transition control of tilt rotor unmanned aerial vehicle based on multi-model adaptive method. In: Proceedings of International Conference on Unmanned Aircraft Systems (ICUAS), Miami, 2017. 560–566

  12. Wang Z G, Li J B, Duan D Y. Manipulation strategy of tilt quad rotor based on active disturbance rejection control. Proc Inst Mech Eng Part G-J Aerospace Eng, 2020, 234: 573–584

    Article  Google Scholar 

  13. Muscarello V, Colombo F, Quaranta G, et al. Aeroelastic rotorcraft-pilot couplings in tiltrotor aircraft. J Guidance Control Dyn, 2019, 42: 524–537

    Article  Google Scholar 

  14. Liu Z, Theilliol D, Yang L Y, et al. Observer-based linear parameter varying control design with unmeasurable varying parameters under sensor faults for quad-tilt rotor unmanned aerial vehicle. Aerosp Sci Tech, 2019, 92: 696–713

    Article  Google Scholar 

  15. Hartmann P, Meyer C, Moormann D. Unified velocity control and flight state transition of unmanned tilt-wing aircraft. J Guidance Control Dyn, 2017, 40: 1348–1359

    Article  Google Scholar 

  16. Liu Z, Theilliol D, He Y Q, et al. Interconnection and damping assignment passivity-based control design under loss of actuator effectiveness. J Intell Robot Syst, 2020, 100: 29–45

    Article  Google Scholar 

  17. Donaire A, Romero J G, Ortega R, et al. Robust IDA-PBC for underactuated mechanical systems subject to matched disturbances. Int J Robust nonlinear Control, 2017, 27: 1000–1016

    Article  MathSciNet  MATH  Google Scholar 

  18. Han W X, Wang Z H, Shen Y. Fault estimation for a quadrotor unmanned aerial vehicle by integrating the parity space approach with recursive least squares. Proc I Mech Eng Part G-J Aer Eng, 2018, 232: 783–796

    Article  Google Scholar 

  19. Cui J, Lai M, Chu Z Y, et al. Experiment on impedance adaptation of under-actuated gripper using tactile array under unknown environment. Sci China Inf Sci, 2018, 61: 122202

    Article  Google Scholar 

  20. Li B B, He Y Q, Han J D, et al. A new modeling scheme for powered parafoil unmanned aerial vehicle platforms: theory and experiments. Chin J Aeronaut, 2019, 32: 2466–2479

    Article  Google Scholar 

  21. Yi K, Liang X, He Y Q, et al. Active-model-based control for the quadrotor carrying a changed slung load. Electronics, 2019, 8: 461

    Article  Google Scholar 

  22. Zhang D H, Zhao X G, Han J D. Active model-based control for pneumatic artificial muscle. IEEE Trans Ind Electron, 2017, 64: 1686–1695

    Article  Google Scholar 

  23. Han J D, Xiong J F, He Y Q, et al. Nonlinear modeling for a water-jet propulsion USV: an experimental study. IEEE Trans Ind Electron, 2017, 64: 3348–3358

    Article  Google Scholar 

  24. Wan E A, Merwe R V D. The unscented kalman filter for nonlinear estimation. In: Proceedings of Adaptive Systems for Signal Processing, Communications, and Control Symposium, Alberta, 2000. 1–6

  25. Krznar M, Kotarski D, Piljek P, et al. On-line inertia measurement of unmanned aerial vehicles using on board sensors and bifilar pendulum. Interdiscip Descr Complex Syst, 2018, 16: 149–161

    Article  Google Scholar 

  26. Robert F S. Flight Dynamics. Princeton: Princeton University Press, 2004

    Google Scholar 

  27. Khalil H K. Nonlinear Systems. 3rd ed. New Jersey: Prentice Hall, 2002

    MATH  Google Scholar 

  28. Yuan Y, Cheng L, Wang Z D, et al. Position tracking and attitude control for quadrotors via active disturbance rejection control method. Sci China Inf Sci, 2019, 62: 010201

    Article  Google Scholar 

  29. Chen C, Zhang J Y, Zhang D B, et al. Control and flight test of a tilt-rotor unmanned aerial vehicle. Int J Adv Robotic Syst, 2017, 14: 172988141667814

    Article  Google Scholar 

  30. He T P, Liu H, Li S. Quaternion-based robust trajectory tracking control for uncertain quadrotors. Sci China Inf Sci, 2016, 59: 122902

    Article  Google Scholar 

  31. Qi X. The research on fault-tolerant control and re-planning for unmanned aerial vehicles. Dissertation for Ph.D. Degree. Shenyang: Shenyang Institute of Automation, Chinese Academy of Sciences, 2017

    Google Scholar 

  32. Brunot M, Janot A, Young P C, et al. An improved instrumental variable method for industrial robot model identification. Control Eng Practice, 2018, 74: 107–117

    Article  MATH  Google Scholar 

Download references

Acknowledgements

This work was supported by the Major Research Program of National Natural Science Foundation of China (Grant No. 91748130), National Natural Science Foundation of China (Grant No. U1608253), and Chinese Academy of Sciences (Grant No. 6141A01061601).

Author information

Authors and Affiliations

  1. Xi’an Modern Control Technology Research Institute, Xi’an, 710065, China

    Zhong Liu

  2. Faculte des Sciences et Techniques, CRAN UMR 7039, CNRS, University of Lorraine, Vandoeuvre-les-Nancy, 54506, France

    Didier Theilliol

  3. State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, 110016, China

    Yuqing He, Feng Gu, Liying Yang & Jianda Han

  4. Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang, 110169, China

    Yuqing He, Feng Gu, Liying Yang & Jianda Han

  5. College of Artificial Intelligence, Nankai University, Tianjin, 300350, China

    Jianda Han

Authors
  1. Zhong Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Didier Theilliol
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yuqing He
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Feng Gu
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Liying Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Jianda Han
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Zhong Liu.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Liu, Z., Theilliol, D., He, Y. et al. Active model-based nonlinear system identification of quad tilt-rotor UAV with flight experiments. Sci. China Inf. Sci. 65, 182202 (2022). https://doi.org/10.1007/s11432-020-3074-5

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s11432-020-3074-5

Keywords

Search

Navigation