Skip to main content
SpringerLink
Log in

Receding horizon control for multi-UAVs close formation control based on differential evolution

  • Research Papers
  • Special Focus
  • Published:
Science China Information Sciences Aims and scope Submit manuscript

Abstract

Close formation flight is one of the most complicated problems on multi-uninhabited aerial vehicles (UAVs) coordinated control. Based on the nonlinear model of multi-UAVs close formation, a novel type of control strategy of using hybrid receding horizon control (RHC) and differential evolution algorithm is proposed. The issue of multi-UAVs close formation is transformed into several on-line optimization problems at a series of receding horizons, while the differential evolution algorithm is adopted to optimize control sequences at each receding horizon. Then, based on the Markov chain model, the convergence of differential evolution is proved. The working process of RHC controller is presented in detail, and the stability of close formation controller is also analyzed. Finally, three simulation experiments are performed, and the simulation results show the feasibility and validity of our proposed control algorithm.

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

Access this article

Log in via an institution

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. Paul T, Krogstad T R, Gravdahl J T. Modeling of UAV formation flight using 3D potential field. Simul Mod Pract Th, 2008, 16: 1453–1462

    Article  Google Scholar 

  2. Keviczky T, Fregene K, Borrelli F, et al. Coordinated autonomous vehicle formations: decentralization, control synthesis and optimization. In: Proceedings of the 2006 American Control Conference. Minneapolis, 2006. 2022–2027

  3. Fan Q J, Yang Z, Fang T. Research status of coordinated formation flight control for multi-UAVs. Acta Aeronaut Astronaut Sin, 2009, 30: 683–691

    Google Scholar 

  4. Proud A W, Pachter M, D’Azzo J J. Close formation flight control. In: AIAA Guidance Navigation and Control Conference and Exhibit, Portland, Oregon, 1999. 1231–1246

  5. Binetti P, Ariyur K B, Krstic M, et al. Formation flight optimization using extremum seeking feedback. J Guid Control Dynam, 2003, 26: 132–142

    Article  Google Scholar 

  6. Dargan J L, Pachter M, D’Azzo J J. Automatic formation flight control. In: AIAA Guidance, Navigation and Control Conference, Hilton Head Island, South Carolina, 1992. 838–857

  7. Buzogany L E, Pachter M, D’Azzo J J. Automated control of aircraft in formation flight. In: AIAA Guidance, Navigation and Control Conference, Monterey, California, 1993. 1349–1370

  8. Pachter M, D’Azzo J J, Proud A W. Tight formation flight control. J Guid Control Dynam, 2001, 24: 246–254

    Article  Google Scholar 

  9. Wang J Y, Wei R X. Close Formation configuration control of cooperative UAV. Flight Dynam, 2008, 26: 34–37

    Google Scholar 

  10. Zong L B, Xie F, Qin S Y. Intelligent optimizing control of formation flight for UAVs based on MAS. Acta Aeronaut Astronaut Sin, 2008, 29: 1326–1333

    Google Scholar 

  11. Singh S N, Pachter M. Adaptive feedback linearization nonlinear close formation control of UAVs. In: Proceedings of the American Control Conference. Chicago, 2000. 854–858

  12. Li Y, Li B, Sun Z, et al. Fuzzy technique based close formation flight control. In: Proceedings of the 31st Annual Conference of IEEE Industrial Electronics Society, New York, 2005. 40–44

  13. Li B, Liao X H, Sun Z, et al. Robust autopilot for close Formation flight of multi-UAVs. In: Proceedings of the 38th Southeastern Symposium on System Theory, Cookeville, 2006. 294–298

  14. Keviczky T, Fregene K, Borrelli F, et al. Coordinated autonomous vehicle formations: Decentralization, control synthesis and optimization. In: Proceedings of the 2006 American Control Conference, Minneapolis, Minnesota, 2006. 2022–2027

  15. Hu X B, Chen W H, Paolo E D. Multiairport capacity management: genetic algorithm with receding horizon. IEEE Trans Intel Syst, 2007, 8: 254–263

    Article  Google Scholar 

  16. Bhattacharya R, Balas G J, Kaya A, et al. Nonlinear receding horizon control of F-16 aircraft. In: Proceedings of the American Control Conference, Arlington, 2001. 518–522

  17. Mayne D Q, Rawlings J B, Rao C V, et al. Constrained model predictive control: stability and optimality. Automatica, 2000, 36: 789–814

    Article  MATH  MathSciNet  Google Scholar 

  18. Storn R, Price K. Differential evolution—a simple and efficient adaptive scheme for global optimization over continuous spaces. Technical Report, International Computer Science Institute, Berkeley, 1995

    Google Scholar 

  19. Blake W, Multhopp D. Design, performance and modeling considerations for close formation flight. In: AIAA Guidance, Navigation, and Control Conference, Reston, Virginia, 1998. 476–486

  20. Ding B C. Predictive Control: Theory and Methods. Beijing: China Machine Press, 2008. 12–26

    Google Scholar 

  21. Zielinski K, Laur R. Stopping criteria for differential evolution in constrained single-objective optimization. In: Advances in Differential Evolution. Berlin/Heidelberg: Springer 2008, 143: 111–138

    Chapter  Google Scholar 

  22. Zielinski K, Weitkemper P, Laur R, et al. Parameter study for differential evolution using a power allocation problem including interference cancellation. In: Proceedings of the IEEE Congress on Evolutionary Computation, Vancouver, BC, 2006. 6748–6755

  23. Storn R, Price K. Differential evolution-a simple and efficient heuristic for global optimization over continuous spaces. J Global Optim, 1997, 11: 341–359

    Article  MATH  MathSciNet  Google Scholar 

  24. Zhang W X, Liang Y. Mathematical Foundation of Genetic Algorithms. 2nd ed. Xi’an: Xi’an Jiaotong University Press, 2003. 67–117

    Google Scholar 

  25. Yang J J, Liu M, Wu C. Genetic algorithm based nonlinear model predictive control method. Control Decision, 2003, 18: 141–144

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. National Key Laboratory of Science and Technology on Holistic Control, School of Automation Science and Electrical Engineering, Beihang University, Beijing, 100191, China

    XiangYin Zhang, HaiBin Duan & YaXiang Yu

Authors
  1. XiangYin Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. HaiBin Duan
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. YaXiang Yu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to HaiBin Duan.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Zhang, X., Duan, H. & Yu, Y. Receding horizon control for multi-UAVs close formation control based on differential evolution. Sci. China Inf. Sci. 53, 223–235 (2010). https://doi.org/10.1007/s11432-010-0036-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11432-010-0036-6

Keywords

Search

Navigation