Quadrotor Sensor Fault Diagnosis with Experimental Results

Abstract

This paper presents a fault detection, isolation, and estimation scheme for sensor bias faults in accelerometer and gyroscope measurements of quadrotor unmanned air vehicles (UAVs). Based on sliding-mode observer techniques, a robust estimation of the quadrotor roll and pitch angles is obtained by using only accelerometer measurements. Then, a diagnostic scheme is developed for detecting, isolating, and estimating sensor bias faults in the gyroscope and accelerometer measurements. Structured residuals are generated, allowing the detection and isolation of multiple simultaneous sensor faults under consideration. After the faults are detected and isolated, two nonlinear estimators are employed to provide an estimate of the unknown fault magnitude. The stability and estimation performance properties of the nonlinear estimators are established. The sensor fault diagnosis algorithm is implemented and evaluated through experimental results using a real-time indoor quadrotor test environment.