Extreme learning machine based actuator fault detection of a quadrotor helicopter

Abstract

Actuator fault might occur during quadrotor helicopter's flight. It is a demand to detect actuator fault in real-time. An actuator fault detection method based on extreme learning machine is proposed. An extreme learning machine network is used to approximate the dynamic of the actuator, which is trained by the actuator's inputs, outputs, and states. The dynamic of the actuator is mapped to the output matrix of the proposed extreme learning machine network. By monitoring the norm of the output matrix, the dynamic of the actuator is supervised, and system fault can be detected. The proposed extreme learning machine network is tested on a self-made experimental propeller system platform, and results show that the method is sensitive and effective on both major and minor fault circumstances.