Performance Driven Fault Detection for Quadrotor UAV Based on Transfer Learning

The fault detection for quadrotor unmanned aerial vehicle (UAV) is studied in this paper. Considering the UAV model is nonlinear and strongly coupled, a performance driven fault detection method is proposed based on neural network. However, the established fault detection system cannot be applied when the UAV enters a new gravitational field. To deal with solve this problem, a fault detection method is proposed based on transfer learning. By means of subspace transfer method and Bregman divergence measurement method, the source domain and target domain are aligned, and the parameter transfer and threshold setting of neural network are realized. Finally, we verify the effectiveness of the proposed method in a four rotor UAV system.