Hostname: page-component-78c5997874-mlc7c Total loading time: 0 Render date: 2024-11-02T06:04:10.307Z Has data issue: false hasContentIssue false
  • English
  • Français

Multivariable adaptive distributed leader-follower flight control for multiple UAVs formation

Published online by Cambridge University Press:  15 June 2017

Y. Xu  and
Z. Zhen
Y. Xu
Affiliation:
Nanjing University of Aeronautics and Astronautics, Nanjing, China
Z. Zhen*
Affiliation:
Nanjing University of Aeronautics and Astronautics, Nanjing, China
*
[email protected]
Get access Rights & Permissions [Opens in a new window]

Abstract

The Unmanned Aerial Vehicles (UAVs) become more and more popular due to various potential application fields. This paper studies the distributed leader-follower formation flight control problem of multiple UAVs with uncertain parameters for both the leader and followers. This problem has not been addressed in the literature. Most of the existing literature considers the leader-follower formation control strategy with parametric uncertainty for the followers. However, they do not take the leader parametric uncertainty into account. Meanwhile, the distributed control strategy depends on less information interactions and is more likely to avoid information conflict. The dynamic model of the UAVs is established based on the aerodynamic parameters. The establishment of the topology structure between a collection of UAVs is based on the algebraic graph theory. To handle the parametric uncertainty of the UAVs dynamics, a multivariable model reference adaptive control (MRAC) method is addressed to design the control law, which enables follower UAVs to track the leader UAV. The stability of the formation flight control system is proved by the Lyapunov theory. Simulation results show that the proposed distributed adaptive leader-following formation flight control system has stronger robustness and adaptivity than the fixed control system, as well as the existing adaptive control system.

Keywords

UAVsformation flight controladaptive control
Type
Research Article
Information
The Aeronautical Journal , Volume 121 , Issue 1241 , July 2017 , pp. 877 - 900
Copyright
Copyright © Royal Aeronautical Society 2017 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES

1. Qin, B. and Wang, L. Development review of unmanned aerial vehicle, Aerodynamic Missile J, 2002, 8, pp 4-10.Google Scholar
2. Zou, X.F., He, Q.H. and He, J.L. Development status and related technology of unmanned aerial vehicle, Aerodynamic Missile J, 2006, 10, pp 9-14.Google Scholar
3. Pu, H.Z., Zhen, Z.Y. and Xia, M. Flight control system of unmanned aerial vehicle, Transactions of Nanjing University of Aeronautics and Astronautics, 2015, 32, (1), pp 1-8.Google Scholar
4. Bennet, D.J., McInnes, C.R., Suzuki, M. and Uchiyama, K. Autonomous three-dimensional formation flight for a swarm of unmanned aerial vehicles, J Guidance, Control, and Dynamics, 2011, 34, (6), pp 1899-1908.Google Scholar
5. Madhavan, S., Antonios, T., Rafal, Z. and Brian, A.W. 3D path planning for multiple UAVs using pythagorean hodograph curves, AIAA Guidance, Navigation, and Control Conference, 2007, Hilton Head, South Carolina, US, pp 1576-1589.Google Scholar
6. Zhen, Z.Y., Gao, C., Zheng, F.Y. and Jiang, J. Cooperative path replanning method for multiple UAVs with obstacle collision avoidance under timing constraints, Proceedings of the Institution of Mechanical Engineers, Part G: J of Aerospace Engineering, 2015, 229, (10), pp 1813-1823.Google Scholar
7. Gao, C., Zhen, Z.Y. and Gong, H.J. A self-organized search and attack algorithm for multiple unmanned aerial vehicles, Aerospace Science and Technology, 2016, 54, pp 229-240.Google Scholar
8. Zhen, Z.Y., Hao, Q.S., Gao, C. and Jiang, J. Information fusion distributed navigation for UAVs formation flight, Proceedings of 2014 IEEE Chinese Guidance, Navigation and Control Conference, August 2014, Yantai, China, pp 1520-1525.Google Scholar
9. Wei, L. Distributed UAV formation control using differential game approach, Aerospace Science and Technology, 2014, pp 54-62.Google Scholar
10. Lorenz, S. and Walter, F. Collision-avoidance framework for small fixed-wing unmanned aerial vehicles, J Guidance, Control, and Dynamics, 2014, 37, (4), pp 1323-1328.Google Scholar
11. Renan, L.P. and Karl, H.K. Tight formation flight control based on H∞ approach, 24th Mediterranean Conference on Control and Automation (MED), June 2016, Athens, Greece, pp 268-274.Google Scholar
12. Zuo, B. and Hu, Y.A. UAV tight formation flight modeling and autopilot designing, Proceedings of the 5th World Congress on Intelligent Control and Automation, 15–19 June 15–19 2004, Hangzhou, China, pp 180-183.Google Scholar
13. Mohammad, A.D. and Mohammad, B.M. Communication free leader-follower formation control of unmanned aircraft systems, Robotics and Autonomous Systems, 2016, pp 69-75.Google Scholar
14. Giulietti, F., Innocenti, M., Napolitano, M. and Pollini, L. Dynamic and control issues of formation flight, Aerospace Science and Technology (S0034-1223), 2005, 36, (9), pp 65-71.Google Scholar
15. Rinaldi, F., Chiesa, S. and Quagliotti, F. Linear quadratic control for quadrotors UAVs dynamics and formation flight, J Intelligent and Robotic Systems, 2013, 70, pp 203-220.Google Scholar
16. Abbas, R. and Wu, Q. Tracking formation control for multiple quadrotors based on fuzzy logic controller and least square oriented by genetic algorithm, The Open Automation and Control Systems J, 2015, 7, pp 842-850.Google Scholar
17. Semsar, E. Adaptive formation control of UAVs in the presence of unknown vortex forces and leader commands, Proceedings of the 2006 American Control Conference, 14–16 June 2006, Minneapolis, Minnesota, US, pp 3563-3569.Google Scholar
18. Zhu, X. Research on Multi-UAV Formation Control Based on Information Consensus,, Thesis of Master, 2014, Northwestern Polytechnical University, Fremont, California, US.Google Scholar
19. Joongbo, S., Chaeik, A. and Youdan, K. Controller design for UAV formation flight using consensus based decentralized approach, AIAA Infotech Aerospace Conference, 2009, Seattle, Washington, US, pp 248-259.Google Scholar
20. Lechevin, N. Towards decentralized fault detection in UAV formations, Proceedings of the 2007 American Control Conference, 2007, New York, New York, US, pp 5759-5764.Google Scholar
21. Sang, Q. Model Reference Adaptive Control of Piecewise Linear Systems with Applications to Aircraft Flight Control, PhD thesis of Science, 2012, University of Virginia, Charlottesville, Virginia, US.Google Scholar
22. Tao, G. Adaptive Control Design and Analysis, 2003, John Wiley and Sons Inc, New York, New York, US.Google Scholar
23. Zhen, Z.Y., Wang, D.B. and Kang, Q. UAV flight trajectory control based on information fusion control method, Proceedings of 2010 IEEE Chinese Guidance, Navigation and Control Conference, 2010, pp 337-341.Google Scholar
24. Guo, J.X., Tao, G. and Liu, Y. A multivariable MRAC scheme with application to a nonlinear aircraft model, Automatica, 2011, 47, pp 804-812.Google Scholar
25. Song, G. Adaptive Control for Distributed Leader-Following Consensus of Multi-Agent Systems, Thesis of Master of Science, 2015, University of Virginia,Charlottesville, Virginia, US.Google Scholar
26. Choon, S.C. Generic UAV Modeling to Obtain its Aerodynamic and Ccontrol Derivatives, Thesis of Master of Science, 2008, Naval Postgraduate School, Monterey, California, US.Google Scholar
27. Eugene, L., Ross, G. and Irene, M.G. Predictor-based model reference adaptive control, AIAA Guidance, Navigation, and Control Conference, August 2009, pp 1-12.Google Scholar
28. Eugene, L. Combined/composite model reference adaptive control, AIAA Guidance, Navigation, and Control Conference, August 2009, pp 1-12.Google Scholar
29. Luis, G.C., Megumi, M., Jinho, J., Travis, G. and Anuradha, A. Design and verification of an adaptive controller for the generic transport model, AIAA Guidance, Navigation, and Control Conference, August 2009, pp 1-22.Google Scholar
30. Sang, Q. and Tao, G. Adaptive control of piecewise linear systems: The state tracking case, IEEE Transactions on Automatic Control, February 2012, 57, (2), pp 522-528.Google Scholar
31. Sang, Q. and Tao, G. Adaptive control of piecewise linear systems with applications to NASA GTM, 2011 American Control Conference, 29 June -1 July 2011, pp 1157-1162.Google Scholar