基于结构冗余传感器配置与灰狼优化算法的无人机可诊断性优化设计

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中图分类号：V240.2 文献标志码：A DOI：10.12305/j.issn.1001-506X.2025.07.22

Abstract：To improve the diagnosability of unmanned aerial vehicles （UAVs），this paper proposes an optimal design strategy for diagnosability based on structural redundant sensor configurations and the grey wolf optimization algorithm.Firstly，to makeup forthe defect of structural analysis in measuring the dificultyof fault diagnosis，a quantitative evaluation method of diagnosability based on Wasserstein distance is proposed. Secondly， a structural redundant sensor configuration algorithm is designed to maximize the system’s diagnosability with the lowest sensor configuration cost. Finally，a diagnosability optimization design strategy based on the gray wolf optimization algorithm is proposed to minimize the design cost of the diagnosability system while mting the qualitative and quantitative diagnosability requirements. Based on the fixed UAV structural model，using the proposed algorithm，the system detectability and isolation rate reaches 100% with the minimum cost of optimal sensor configuration. The optimization strategy based on qualitative evaluation makes the diagnostic cost shrink by 83% ，which is a saving of 2% to 15% compared with the other algorithms. The optimization strategy based on quantitative evaluation makes the diagnostic cost shrink by 90% ，which is a saving of 0% to 25% （204号 compared with the other algorithms.

Keywords ： structural analysis;diagnosability；sensor configuration; grey wolf optimization algorithm （GWO）

0 引言

随着新型材料、电子集成、机器学习、人工智能等技术的不断发展，无人机朝着仿生化、智能化、功能多样化、集群协同化的方向发展1，呈现出较强的自主决策和任务规划能力，并在军用、民用3以及农业领域4得到不同程度的应用。（剩余30433字）