Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
Skip to main content
Springer Nature Link
Log in

Parameters identification of UCAV flight control system based on predator-prey particle swarm optimization

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

Abstract

With the improvement of the aircraft flight performance and development of computing science, uninhabited combat aerial vehicle (UCAV) could accomplish more complex tasks. But this also put forward stricter requirements for the flight control system, which are the crucial issues of the whole UCAV system design. This paper proposes a novel UCAV flight controller parameters identification method, which is based on predator-prey particle swarm optimization (PSO) algorithm. A series of comparative experimental results verify the feasibility and effectiveness of our proposed approach in this paper, and a predator-prey PSO-based software platform for UCAV controller design is also developed.

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. Jin G D, Gu L X. UAV simulator-based simulation of flight control system, In: Proceedings of the 2009 International Workshop on Intelligent Systems and Applications, 2009, Wuhan. 1–4

  2. Samuel B L, Antonios T, Brian A W, et al. Terrain based co-operative UAV mapping of complex obstacles using 2-D splinegon. Int J Intell Syst Comput Cybern, 2012, 5: 248–292

    Google Scholar 

  3. Duan H B, Shao S, Su B W, et al. New Development thoughts on the bio-inspired intelligence based control for unmanned combat aerial vehicle. Sci China Tech Sci, 2010, 53: 2025–2031

    Article  Google Scholar 

  4. Duan H B, Li P. Progress in control approaches for hypersonic vehicle. Sci China Tech Sci, 2012, 55: 2965–2970

    Article  Google Scholar 

  5. Li G W, Zhang W G, Li J, et al. Particle swarm optimization based on uniform design and its application in flight control systems. Comput Appl, 2004, 27: 727–729

    Google Scholar 

  6. Li C Y, Jing W X, Gao C S. Adaptive back-stepping-based flight control system using integral filters. Aerosp Sci Technol, 2009, 13: 105–113

    Article  Google Scholar 

  7. Juang J G, Chiou H K, Chien L H. Analysis and comparison of aircraft landing control using recurrent neural networks and genetic algorithms approaches. Neurocomputing, 2008, 71: 3224–3228

    Article  Google Scholar 

  8. Oosterom M, Babuška R. Design of a gain-scheduling mechanism for flight control laws by fuzzy clustering. Control Eng Practice, 2006, 14: 769–781

    Article  Google Scholar 

  9. Qi G P, Song P, Li K J. Multivariable PID neural network based flight control system for small-scale unmanned helicopter. In: Proceedings of the 2009 IEEE International Conference on Information and Automation, 2009, Macau. 1331–1335

  10. Duan H B, Liu S Q, Wang D B, et al. Design and realization of hybrid ACO-based PID and LuGre friction compensation controller for three degree-of-freedom high precision flight simulator. Simul Model Pract Theory, 2009, 17: 1160–1169

    Article  Google Scholar 

  11. Sun X, Zhang W G, Yin W, et al. Design of a large envelope wavelet neural network gain scheduling flight control law based on PSO algorithm. Meas Sci Technol, 2007, 26: 34–36

    MATH  Google Scholar 

  12. Liu F, Duan H B, Deng Y M. A chaotic quantum-behaved particle swarm optimization based on lateral inhibition for image matching. Optik, 2012, 123: 1955–1960

    Article  Google Scholar 

  13. Kennedy J, Eberhart R. Particle swarm optimization. In: Proceedings of the IEEE International Conference on Neural Networks, Perth, 1995. 1942–1948

  14. Duan H B, Ma G J, Luo D L. Optimal formation reconfiguration control of multiple UCAVs using improved particle swarm optimization. J Bionic Eng, 2008, 5: 213–225

    Article  Google Scholar 

  15. Jin S H, Kwang Y L, Raul G R. Multiobjective control of power plants using particle swarm optimization techniques. IEEE Trans Energy Convers, 2006, 21: 552–561

    Article  Google Scholar 

  16. Mitsuharu H, Atsushi I, Keiichiro Y. Particle swarm optimization considering the concept of predator-prey behavior. In: Proceedings of the IEEE Conference on Evolutionary Computation, 2006, Vancouver. 434–437

  17. Di Gesù V, Lenzitti B, Lo Bosco G, et al. Comparison of different cooperation strategies in the prey-predator problem. In: Proceedings of the International Workshop on Computer Architecture for Machine Perception and Sensing, 2006, Montreal. 108–112

  18. Zhang WY. Study of Intelligent Robust Design Approach of Large Flight Envelope Flight Control System. Dissertation for the Master Degree. Beijing: Beihang University, 2004.

    Google Scholar 

  19. Duan H B. Ant Colony Algorithms: Theory and Applications. Beijing: Science Press, 2005

    Google Scholar 

  20. Zhang M T, An J W. Application of intelligent computation to promote the automation of flight control design. In: Proceedings of the International Conference on Intelligent Computation Technology and Automation, 2008, Hunan, Vol. 1: 991–994

    Google Scholar 

  21. Li X D, Xian B, Diao C, et al. Output feedback control of hypersonic vehicles based on neural network and high gain observer. Sci China Inf Sci, 2011, 54: 429–447

    Article  MathSciNet  MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

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

    HaiBin Duan & YaXiang Yu

  2. Science and Technology on Electron-Optic Control Laboratory, Luoyang, 410079, China

    ZhenYu Zhao

Authors
  1. HaiBin Duan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. YaXiang Yu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. ZhenYu Zhao
    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

Duan, H., Yu, Y. & Zhao, Z. Parameters identification of UCAV flight control system based on predator-prey particle swarm optimization. Sci. China Inf. Sci. 56, 1–12 (2013). https://doi.org/10.1007/s11432-012-4754-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11432-012-4754-9

Keywords

Search

Navigation