• DocumentCode
    3349584
  • Title

    Research on fuzzy guidance law based on self-adaptive Genetic Annealing Algorithm

  • Author

    Jin-yong, Yu ; Ru-chuan, Zhang ; Hong-chao, Zhao

  • Author_Institution
    Number Three Dept., Naval Aeronaut. & Astronaut. Univ., Yantai
  • fYear
    2008
  • fDate
    21-24 Sept. 2008
  • Firstpage
    1036
  • Lastpage
    1041
  • Abstract
    A new approach about design of guidance law (GL) for integrated self-adaptive genetic annealing algorithm (SGAA) and fuzzy logic (SGAA-FGL) was proposed in this study. Firstly, Based on traditional fuzzy logic control, the nonlinear variable region function was introduced, thus dynamic change of the fuzzy variable region can be realized. Next the self-adaptive simulated annealing genetic algorithm was employed to optimize the fuzzy rule, which was designed by selecting adaptively the cross probability and mutation probability of the proposed algorithm and improved the stability and convergence of system. Finally, the simulation results were presented to show the validity of the proposed method.
  • Keywords
    adaptive control; fuzzy control; genetic algorithms; nonlinear control systems; simulated annealing; cross probability; fuzzy guidance law; fuzzy logic control; fuzzy rule; fuzzy variable region; mutation probability; nonlinear variable region function; self-adaptive simulated annealing genetic algorithm; Algorithm design and analysis; Design optimization; Fuzzy control; Fuzzy logic; Fuzzy systems; Genetic algorithms; Genetic mutations; Nonlinear dynamical systems; Simulated annealing; Stability; fuzzy control; guidance law; self-adaptive genetic annealing algorithm; variable region;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Cybernetics and Intelligent Systems, 2008 IEEE Conference on
  • Conference_Location
    Chengdu
  • Print_ISBN
    978-1-4244-1673-8
  • Electronic_ISBN
    978-1-4244-1674-5
  • Type

    conf

  • DOI
    10.1109/ICCIS.2008.4670760
  • Filename
    4670760