Affiliation:
1. College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China
2. School of Electrical Engineering, Anhui Polytechnic University, Wuhu 241000, China
Abstract
The heterogeneous unmanned system, which is composed of unmanned aerial vehicles (UAV) and unmanned ground vehicles (UGV), has been broadly applied in many domains. Collaborative fault diagnosis (CFD) among UAVs and UGVs has become a key technology in these unmanned systems. However, collaborative fault diagnosis in unmanned systems faces the challenges of the dynamic environment and limited communication bandwidth. This paper proposes an event-triggered collaborative fault diagnosis framework for the UAV–UGV system. The framework aims to achieve autonomous fault monitoring and cooperative diagnosis among unmanned systems, thus enhancing system security and reliability. Firstly, we propose a fault trigger mechanism based on broad learning systems (BLS), which utilizes sensor data to accurately detect and identify faults. Then, under the dynamic event triggering mechanism, the network communication topology between the UAV–UGV system and BLS is used to achieve cooperative fault diagnosis. To validate the effectiveness of our proposed scheme, we conduct experiments on a software-in-the-loop (SIL) simulation platform. The experimental results demonstrate that our method achieves high diagnosis accuracy for the UAV–UGV system.
Funder
National Natural Science Foundation of China
Outstanding Youth Foundation of Jiangsu Province of China
High Level Talent Research Start-up Fund of Anhui Polytechnic University
Open fund of the National Key Laboratory of Helicopter Aeromechanics
Natural Science Research Project of Anhui Province Universities
Outstanding Doctoral Dissertation in NUAA
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