UAV air combat maneuvering decision based on intuitionistic fuzzy game theory

被引:5
|
作者
Li S. [1 ]
Ding Y. [1 ]
Gao Z. [1 ]
机构
[1] College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing
关键词
Differential evolution algorithm; Intuitionistic fuzzy; Maneuvering decision; Nash equilibrium;
D O I
10.3969/j.issn.1001-506X.2019.05.19
中图分类号
学科分类号
摘要
To solve the problem of unmanned aerial vehicle (UAV) air combat maneuvering decision in uncertain environment, the game theory is combined with intuitionistic fuzzy set. First of all, the optional strategy of UAV is assessed by intuitionistic fuzzy multi-attribute to obtain the intuittionistic fuzzy payoff matrix. Then, Nash equilibrium condition under intuitionistic fuzzy total order relations is proposed, and a planning model for solving Nash equilibrium is established. Meanwhile, differential evolution algorithm, based on individual control parameters and genetic algebra adaptive strategy, is improved to get the optimal solution of the game model. Finally, the simulation validates the rationality and effectiveness of the model and the proposed algorithm, which is a new idea for solving the air combat decision-making problems in uncertain environment. © 2019, Editorial Office of Systems Engineering and Electronics. All right reserved.
引用
收藏
页码:1063 / 1070
页数:7
相关论文
共 30 条
  • [21] Zhong Y.W., Liu J.R., Yang L.Y., Et al., Maneuver library and integrated control system for autonomous close-in air combat, Acta Aeronautica et Astronautica Sinica, 29, pp. 114-121, (2008)
  • [22] Gu J.J., Liu W.H., Jiang W.Z., WVR air combat situation assessment mode based on weapon engagement zone and kill probability, Systems Engineering and Electronics, 37, 6, pp. 1306-1312, (2015)
  • [23] Chen T.Y., The inclusion-based TOPSIS method with interval-valued intuitionistic fuzzy sets for multiple criteria group decision making, Applied Soft Computing, 26, 1, pp. 57-73, (2015)
  • [24] Xu Z.S., Ronald R.Y., Some geometric aggregation operators based on intuitionistic fuzzy sets, International Journal of General Systems, 35, 4, pp. 417-433, (2006)
  • [25] Li N.N., He Z.Y., Power quality comprehensive evaluation combing subjective weight with objective weight, Power System Technology, 33, 6, pp. 55-61, (2009)
  • [26] La Q.D., Yong H.C., Soong B.H., An introduction to game theory, Potential Game Theory, pp. 841-846, (2016)
  • [27] Mallipeddi R., Lee M., An evolving surrogate model-based differential evolution algorithm, Applied Soft Computing, 34, C, pp. 770-787, (2015)
  • [28] Fan Q., Yan X., Self-adaptive differential evolution algorithm with discrete mutation control parameters, Expert Systems with Applications, 42, 3, pp. 1551-1572, (2015)
  • [29] Guo Z., Liu G., Li D., Et al., Self-adaptive differential evolution with global neighborhood search, Soft Computing, 21, 13, pp. 3759-3768, (2017)
  • [30] Sun T.Y., Tsai S.J., Lee Y.N., Et al., The study on intelligent advanced fighter air combat decision support system, Proc. of the IEEE International Conference on Information Reuse and Integration, pp. 39-44, (2006)