Sensor fault diagnosis in quadrotors using nonlinear adaptive estimators

Abstract

Unmanned Aerial Vehicles (UAVs) have attracted significant attentions in recent years due to their potentials in various military and civilian applications. Small UAVs are often equipped with low-cost and lightweight micro-electro-mechanical systems (MEMS) inertial measurement units including 3-axis gyro, accelerometer and magnetometer. The measurements provided by gyros and accelerometers often suffer from bias and excessive noise as a result of temperature variations, vibration, etc. This paper presents a sensor fault diagnostic method for quadrotor UAVs. Specifically, we consider the faults in the gyro and accelerometer. A model-based sensor fault detection and isolation (FDI) estimation method is presented. The proposed FDI method adopts the idea that accelerometer and gyroscopic measurements coincide with the translational and rotational forces represented in the UAV dynamics. Thus, the faults in accelerometer and gyroscope can be represented as virtual actuator faults in the quadrotor state equations. Two diagnostic estimators are designed to provide structured FDI residuals allowing simultaneous detection and isolation of gyroscope and accelerometer sensor bias. In addition, nonlinear adaptive estimators are designed to provide an estimate of the unknown sensor bias. The parameter convergence property of the adaptive estimation scheme is analyzed. Simulation studies utilizing a nonlinear quadrotor UAV model are used to illustrate the effectiveness of the proposed method.