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

Thanaraj T; Kin Huat Low; Bing Feng Ng

doi:10.1016/j.isatra.2023.02.026

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

ISA Trans. 2023 Jul:138:168-185. doi: 10.1016/j.isatra.2023.02.026. Epub 2023 Feb 27.

Authors

Thanaraj T¹, Kin Huat Low², Bing Feng Ng³

Affiliations

¹ Air Traffic Management Research Institute, Nanyang Technological University, 637460, Singapore. Electronic address: tthanara001@e.ntu.edu.sg.
² School of Mechanical and Aerospace Engineering, Nanyang Technological University, 639798, Singapore. Electronic address: mkhlow@ntu.edu.sg.
³ School of Mechanical and Aerospace Engineering, Nanyang Technological University, 639798, Singapore. Electronic address: bingfeng@ntu.edu.sg.

PMID: 36906441
DOI: 10.1016/j.isatra.2023.02.026

Abstract

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.

Keywords: Actuator fault detection and isolation; Extreme-learning machines; Multi-rotor unmanned aerial vehicle; Neuro-fuzzy systems.