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This paper conducts an in-depth study on the reliability and safety of leader-follower type Unmanned Aerial Vehicle (UAV) formation systems. Firstly, the dynamic model of leader-follower UAV formation systems is established, and a systematic analysis of typical faults and their failure mechanisms within UAV formations is conducted, including flight control system failures, airframe damage, collisions, communication failures, and environmental interference. Subsequently, a design method for distributed fault monitors based on intermediate observers is proposed to achieve fault detection and diagnosis for leader-follower UAV formations addressing disturbances from wind interference and actuator faults, considering both centralized and distributed output estimation errors. For UAV formations with directional topology connections, the observer gain matrix is computed by solving low-order Linear Matrix Inequalities (LMI) based on Schur matrix decomposition theory. Finally, through simulation studies, the effectiveness of the proposed method is validated, demonstrating that the intermediate observer can accurately estimate the flight state of UAV formations and perform relatively accurate fault diagnosis for UAV formations under both no-wind disturbance and wind disturbance conditions.

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Fault Diagnosis of Clustered UAVs Based on Intermediate Observer

  • 会议论文

  • 发表日期：

    2025年

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  • Ziwang Zhang,
  • Jian Shen,
  • Qingyu Zhu,
  • Qichen Yan,
  • Xiaoguang Wang,
  • Benkang Zhang,
  • Enyou Wang

摘要

This paper conducts an in-depth study on the reliability and safety of leader-follower type Unmanned Aerial Vehicle (UAV) formation systems. Firstly, the dynamic model of leader-follower UAV formation systems is established, and a systematic analysis of typical faults and their failure mechanisms within UAV formations is conducted, including flight control system failures, airframe damage, collisions, communication failures, and environmental interference. Subsequently, a design method for distributed fault monitors based on intermediate observers is proposed to achieve fault detection and diagnosis for leader-follower UAV formations addressing disturbances from wind interference and actuator faults, considering both centralized and distributed output estimation errors. For UAV formations with directional topology connections, the observer gain matrix is computed by solving low-order Linear Matrix Inequalities (LMI) based on Schur matrix decomposition theory. Finally, through simulation studies, the effectiveness of the proposed method is validated, demonstrating that the intermediate observer can accurately estimate the flight state of UAV formations and perform relatively accurate fault diagnosis for UAV formations under both no-wind disturbance and wind disturbance conditions.