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Abstract
Multi-agent cooperative operation plays an important role in the cyberspace war, and the main application lies in the field of multiple Unmanned Aerial Vehicles (UAVs)/multi-missile collaborative cluster. Sharing collaborative information and consistency are the foundation and prerequisite for the multi-agent to complete collaborative tasks such as coordination, formation, flocking and synchronization. A consensus information model is established based on the neighbor system and the cluster potential, and the bias of the cooperative information is mapped to the cluster potential energy. By using the minimization of parallel energy to solve the maximum a posteriori probability of the Markov random field, cooperative information reaches a consensus with distributed and non center condition. Different from the traditional consensus algorithm, the algorithm proposed introduces the concept of virtual reference. A virtual reference is established by cooperative information interaction among the neighbors by using the mean field theory with no external reference input. When the pilot node or the virtual pilot node exists, the state and its derivative of the pilot node cooperative information are used as the virtual reference. Simulation results show that the proposed algorithm has the advantages of insensitivity to network scale, fast convergence and high robustness. The algorithm can be also used in the presence/absence of reference input, meaning that the algorithm has great adaptability. © 2017, Press of Chinese Journal of Aeronautics. All right reserved.
In this paper, the cooperative consistency control algorithm of unmanned aerial vehicle is studied. The formation of unmanned aerial vehicles has the characteristics of controlling uncertainty. This makes the controller have certain uncertainty which may cause the multi-agent system to be difficult to agree. In view of this feature, the intelligent algorithm for collaborative consistency control of unmanned aerial vehicle formation is designed with the properties of the Laplace matrix of the multi-agent network which is composed of a series of intelligent bodies with autonomous capability and the interaction between them. The simulation results verify the feasibility of the intelligent algorithm.
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