A journal of IEEE and CAA , publishes high-quality papers in English on original theoretical/experimental research and development in all areas of automation
Volume 2 Issue 1
Jan.  2015

IEEE/CAA Journal of Automatica Sinica

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Article Contents
Haibin Duan, Pei Li and Yaxiang Yu, "A Predator-prey Particle Swarm Optimization Approach to Multiple UCAV Air Combat Modeled by Dynamic Game Theory," IEEE/CAA J. of Autom. Sinica, vol. 2, no. 1, pp. 11-18, 2015.
Citation: Haibin Duan, Pei Li and Yaxiang Yu, "A Predator-prey Particle Swarm Optimization Approach to Multiple UCAV Air Combat Modeled by Dynamic Game Theory," IEEE/CAA J. of Autom. Sinica, vol. 2, no. 1, pp. 11-18, 2015.

A Predator-prey Particle Swarm Optimization Approach to Multiple UCAV Air Combat Modeled by Dynamic Game Theory

Funds:

This work was supported by National Natural Science Foundation of China (61425008, 61333004, 61273054), Top-Notch Young Talents Program of China, and Aeronautical Foundation of China (2013585104).

  • Dynamic game theory has received considerable attention as a promising technique for formulating control actions for agents in an extended complex enterprise that involves an adversary. At each decision making step, each side seeks the best scheme with the purpose of maximizing its own objective function. In this paper, a game theoretic approach based on predatorprey particle swarm optimization (PP-PSO) is presented, and the dynamic task assignment problem for multiple unmanned combat aerial vehicles (UCAVs) in military operation is decomposed and modeled as a two-player game at each decision stage. The optimal assignment scheme of each stage is regarded as a mixed Nash equilibrium, which can be solved by using the PP-PSO. The effectiveness of our proposed methodology is verified by a typical example of an air military operation that involves two opposing forces: the attacking force Red and the defense force Blue.

     

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