Actuator fault detection and isolation on multi-rotor UAV using extreme learning neuro-fuzzy systems

• Main Authors: Thanaraj, T., Low, Kin Huat, Ng, Bing Feng
• 其他作者: School of Mechanical and Aerospace Engineering
• 格式: Article
• 語言: English
• 出版: 2023
• 主題: Engineering::Computer science and engineering::Mathematics of computing::Numerical analysis; Engineering::Aeronautical engineering::Aircraft motors and engines; Actuator Fault Detection and Isolation; Multi-Rotor Unmanned Aerial Vehicle; Extreme-Learning Machines; Neuro-Fuzzy Systems
• 在線閱讀: https://hdl.handle.net/10356/167384

Undetected partial actuator faults on multi-rotor UAVs can lead to system failures and uncontrolled crashes, necessitating the development of accurate and efficient fault detection and isolation (FDI) strategy. This paper proposes a hybrid FDI model for a quadrotor UAV that integrates an extreme learning neuro-fuzzy algorithm with a model-based extended Kalman filter (EKF). Three FDI models using Fuzzy-ELM, R-EL-ANFIS, and EL-ANFIS are compared based on training, validation performances, and sensitivity to weaker and shorter actuator faults. They are also tested online for linear and nonlinear incipient faults by measuring their isolation time delays and accuracies. The results show that the Fuzzy-ELM FDI model exhibits greater efficiency and sensitivity, while Fuzzy-ELM and R-EL-ANFIS FDI models demonstrate better performance than a conventional neuro-fuzzy algorithm, ANFIS.