UAV Quadrotor Fault Detection and Isolation Using Artificial Neural Network and Hammerstein-Wiener Model

Abstract

In this paper, a sensor fault diagnosis system is proposed for an aerial vehicle (UAV) quadrotor. Given the nonlinearity of this system and the inaccuracies of modeling, adapted tools have been adopted to ensure control and diagnosis. After synthesizing control laws required for quadrotor control using the Sliding Mode Control method, a Hammerstein-Wiener model has been developed. The goal is to estimate the states of the quadrotor system and build a set of residuals to detect sensor faults. The advantage of this solution is that it does not require prior knowledge of the model and can be easily generalized to other types of vehicles. Then, to ensure decision making for fault isolation, the neural network has been combined with the diagnostic system. With judicious choice of configuration, it can efficiently classify defects from residuals. Finally, typical sensor failures have been injected during simulations. The results of the diagnosis have been very satisfactory. The model has been validated by the test data.