Fuzzy game decision-making of unmanned aerial vehicles air-to-ground attack based on the particle swarm optimization integrating multiply strategies

In view of the complexity and uncertainty of cooperative air-to-ground attack for unmanned air vehicles (UAVs), a multi-stages and fuzzy multi-objective programming is proposed by jointing the mission of suppression of enemy air defense and the mission of ground target attack, introducing the concepts of survival factor, friction factor and state factor and combining the factors of survival probability, weapon consumption and fuzzy target threat. In order to better describe the antagonism and multi-strategic nature of the attack task, the game theory is used to transform the planning model into a synthetic aggregation model of fuzzy multi-objective bi-matrix game. Using the theory of necessity, the uncertainty objective in the aggregationmodel is clarified. And then using the entropy weight method, the aggregation model can be solved by transforming it into a single-target bi-matrix game model. A method of solving Nash equilibrium based on the particle swarm optimization algorithm integrating multiply strategies is proposed. By introducing adaptive inertia weight, dynamic inverse learning and local mutation strategy, the local precise search capability of particle swarms can be guaranteed while enhancing population diversity. The simulation results of the example verify the validity of the proposed model and method. © 2019, Editorial Department of Control Theory & Applications South China University of Technology. All right reserved.