Preliminary study of actuator fault detection for RUAVs using neuro-fuzzy system

• Main Authors: T., Thanaraj, Ng, Bing Feng, Low, Kin Huat
• Other Authors: School of Mechanical and Aerospace Engineering
• Format: Conference or Workshop Item
• Language: English
• Published: 2021
• Subjects: Engineering::Aeronautical engineering::Aviation; Unmanned Aircraft System; Actuator Fault Detection
• Online Access: https://hdl.handle.net/10356/147380
• Institution: Nanyang Technological University

With the ever-increasing use of Rotary Unmanned Aerial Vehicles (RUAVs) for various purposes, a fast and accurate fault detection system is key to detect unknown faults, to ensure fault tolerance and for safe operations. In this paper, a data-driven fault detection algorithm based on an Adaptive Neuro-Fuzzy Inference System (ANFIS) was used to detect single actuator faults of quadrotor flights through sensor readings from inertial measurement units. Sensor readings were collated and pre-processed to generate a training dataset to train the neuro-fuzzy model, and then the trained model was evaluated using four-fold cross-validation and a set of performance metrics. The results presented illustrate the generalisation performance ANFIS model and its effectiveness in detecting actuator faults.