Advertisement

Journal of Bionic Engineering

, Volume 5, Issue 4, pp 340–347 | Cite as

Optimal Formation Reconfiguration Control of Multiple UCAVs Using Improved Particle Swarm Optimization

  • Hai-bin DuanEmail author
  • Guan-jun Ma
  • De-lin Luo
Article

Abstract

Optimal formation reconfiguration control of multiple Uninhabited Combat Air Vehicles (UCAVs) is a complicated global optimum problem. Particle Swarm Optimization (PSO) is a population based stochastic optimization technique inspired by social behaviour of bird flocking or fish schooling. PSO can achieve better results in a faster, cheaper way compared with other bio-inspired computational methods, and there are few parameters to adjust in PSO. In this paper, we propose an improved PSO model for solving the optimal formation reconfiguration control problem for multiple UCAVs. Firstly, the Control Parameterization and Time Discretization (CPTD) method is designed in detail. Then, the mutation strategy and a special mutation-escape operator are adopted in the improved PSO model to make particles explore the search space more efficiently. The proposed strategy can produce a large speed value dynamically according to the variation of the speed, which makes the algorithm explore the local and global minima thoroughly at the same time. Series experimental results demonstrate the feasibility and effectiveness of the proposed method in solving the optimal formation reconfiguration control problem for multiple UCAVs.

Keywords

uninhabited combat air vehicles particle swarm optimization control parameterization and time discretization optimal formation reconfiguration 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. [1]
    Zheng C W, Li L, Xu F J. Evolutionary route planner for unmanned air vehicles. IEEE Transactions on Robotics and Automation, 2005, 21, 609–620.CrossRefGoogle Scholar
  2. [2]
    Blake W, Multhopp D. Design, performance and modeling considerations for close formation flight. Proceedings of the 1998 AIAA Guidance, Navigation, and Control Conference, Reston, USA, 1998, 476–486.Google Scholar
  3. [3]
    Dargan J, Pachter M, Dazzo J. Automatic formation flight control. Proceedings of the 1992 AIAA Guidance, Navigation, and Control Conference, Washington DC, USA, 1992, 838–857.Google Scholar
  4. [4]
    Fierro R, Belta C, Desai J, Kumar V. On controlling aircraft formations. Proceedings of the 40th IEEE Conference on Decision and Control, Arlington, USA, 2001, 2, 1065–1070.Google Scholar
  5. [5]
    Koo T, Shahruz S. Formation of a group of unmanned aerial vehicles (UAVs). Proceedings of the American Control Conference, Arlington, USA, 2001, 69–74.Google Scholar
  6. [6]
    Giuletti F, Pollini L, Innocenti M. Autonomous formation flight. IEEE Control Systems Magazine, 2000, 20, 34–44.CrossRefGoogle Scholar
  7. [7]
    Pollini L, Giuletti F, Innocenti M. Robustness to communication failures within formation flight. Proceedings of the American Control Conference, Arlington, USA, 2002, 2860–2866.Google Scholar
  8. [8]
    Chichka D F, Speyer J, Park C. Peak-seeking control with application to formation flight. Proceedings of the 38th IEEE Conference on Decision and Control, Arlington, USA, 1999, 3, 2463–2470.Google Scholar
  9. [9]
    Shannon Z, John K T, Shankar S. Hybrid system design for formations of autonomous vehicles. Proceedings of the 42nd IEEE Conference on Decision and Control, Maui, Hawaii, USA, 2003, 1–6.Google Scholar
  10. [10]
    Teo K L. A Unified Computational Approach to Optimal Control Problems, Longman Scientific and Technical, New York, 1991.zbMATHGoogle Scholar
  11. [11]
    Lee Y D, Lee B H, Kim H G. An evolutionary approach for time optimal trajectory planning of a robotic manipulator. Information Sciences, 1999, 113, 245–260.CrossRefGoogle Scholar
  12. [12]
    Furukawa T. Time-subminimal trajectory planning for discrete non-linear systems. Engineering Optimization, 2002, 34, 219–243.CrossRefGoogle Scholar
  13. [13]
    Lee K Y, Dissanayake G. Numerical solution for a near minimum time trajectory for two coordinated manipulators. Engineering Optimization, 1998, 30, 227–247.CrossRefGoogle Scholar
  14. [14]
    Furukawa T, Durrant-Whyte H F, Bourgault F, Dissanayake G. Time-optimal coordinated control of the relative formation of multiple vehicles. Proceedings the 2003 IEEE International Symposium on Computational Intelligence in Robotics and Automation, Kobe, Japan, 2003, 259–264.Google Scholar
  15. [15]
    Simeon T, Leroy S, Lauumond J P. Path coordination for multiple mobile robots: A resolution-complete algorithm. IEEE Transactions on Robotics and Automation, 2002, 18, 42–49.CrossRefGoogle Scholar
  16. [16]
    Saber R F, Dunbar W B, Murray R M. Cooperative control of multi-vehicle systems using cost graphs and optimization. Proceedings of the 2003 American Control Conference, Denver, USA, 2003, 2217–2222.Google Scholar
  17. [17]
    Kennedy J, Eberhart R. Particle swarm optimization. Proceedings of the 1995 IEEE International Conference on Neural Networks, Perth, Australia, 1995, 1942–1948.Google Scholar
  18. [18]
    Eberhart R, Kennedy J. A new optimizer using particle swarm theory. Proceedings of the 6th International Symposium on Micro-Machine and Human Science, Nagoya, Japan, 1995, 39–43.Google Scholar
  19. [19]
    Fu G J, Wang S M, Liu S Y. An improved velocity mutation particle swarm optimizer. Computer Engineering and Application, 2006, 13, 48–51.Google Scholar
  20. [20]
    Hao R, Wang Y J, Wang Q. An improved particle swarm optimization based on self-adaptive escape velocity. Journal of Software, 2005, 16, 2036–2044.CrossRefGoogle Scholar
  21. [21]
    Carlos C A, Gregprop T P, Maximino S L. Handling multiple objectives with particle swarm optimization. IEEE Transactions on Evolutionary Computation, 2004, 8, 256–279.CrossRefGoogle Scholar
  22. [22]
    Chen Y M, Chang S H. An agent-based simulation for multi-UAVs coordinative sensing. International Journal of Intelligent Computing and Cybernetics, 2008, 1, 269–284.MathSciNetCrossRefGoogle Scholar
  23. [23]
    Xiong W, Chen Z J, Zhou R. Optimization for multiple flight vehicles formation reconfiguration using hybrid genetic algorithm. Proceeding of the first Chinese Guidance, Navigation and Control Conference, Beijing, China, 2007, 501–506.Google Scholar

Copyright information

© Jilin University 2008

Authors and Affiliations

  1. 1.School of Automation Science and Electrical EngineeringBeihang UniversityBeijingP. R. China
  2. 2.Provincial Key Laboratory for Information Processing TechnologySuzhou UniversitySuzhouP. R. China
  3. 3.Center for Systems and ControlXiamen UniversityXiamenP. R. China

Personalised recommendations