基于元胞自动机的蜂群无人机故障影响模型
作者:
作者单位:

南京航空航天大学自动化学院

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中图分类号:

TP273

基金项目:

中央高校基础科研人工智能+专项(NZ2020003)


Fault influence model of swarm UAV Based on Cellular Automata
Author:
Affiliation:

College of Automation Engineering,Nanjing University of Aeronautics and Astronautics

Fund Project:

Artificial intelligence + special project of basic scientific research in Central University(NZ2020003)

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    摘要:

    基于固定翼无人机飞行特性以及蜂群无人机控制策略,针对无人机控制器遭受恶意攻击的情形,采用时序网络与元胞自动机理论分析蜂群无人机故障影响机理。首先通过时序网络分析蜂群无人机拓扑网络的变化情况,提出基于跳数的故障传播路径的确定方法;其次考虑蜂群无人机状态信息,建立符合蜂群无人机特征的元胞对象,同时基于局部信息交互原则,确定元胞自动机的状态演变规则,并依据近邻信息对无人机控制律的影响,提出矢量投影法来确定故障影响权值,辨识出各无人机故障影响程度的动态变化情况;最后建立仿真模型,利用预测与实际故障影响程度结果,基于DCG算法与模式距离验证了所建故障影响模型的有效性。

    Abstract:

    Based on the flight characteristics and the control strategy of UAV with fixed wings, temporal network and cellular automata theory are used to analyze the failure influence mechanism of swarm UAV in the case of malicious attack to controller. Firstly, the change of the topology network of swarm UAV is studied by temporal network, and a method based on hop number is then proposed to determine the fault propagation path. Secondly, utilizing the status information of swarm UAV, the cellular object that satisfy the characteristics of swarm UAV is established. Based on the principle of local information interaction, the state conversion rules is determined so that the dynamic change of the influence degree of UAV failure can be identified, by which the weights of failure influence are determined by the vector projection method according to the influence of the neighbour information on the control law of a UAV. Finally, after building a simulation model, the prediction results compared with the actual fault influence degree are obtained via DCG algorithm and model distance, which verifies the effectiveness of the proposed fault influence model.

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历史
  • 收稿日期:2021-05-23
  • 最后修改日期:2021-08-26
  • 录用日期:2021-09-10
  • 在线发布日期: 2021-10-01
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