Adaptive Neural Network Based Fault Detection Design for Unmanned Quadrotor under Faults and Cyber Attacks

Abstract

The occurrence of faults and failures in flight control systems of unmanned aerial vehicles (UAVs) can destabilize the system which could cause potential economic and life losses. Therefore, it's necessary to detect faults and attacks in real time and modify the control system based on the occurred fault. In this paper, a neural network-based fault detection (NNFD) approach is introduced to detect and estimate the faults and false data injection (FDI) attacks on the sensor systems of a quadrotor in real time. An unmanned quadrotor is selected as our case study to demonstrate the effectiveness of our proposed NFDD strategy. The simulation results show that the applied NNFD method can detect the faults and FDI attacks on an unmanned quadrotor sensors with sufficient accuracy.