کنفرانس بین المللی کنترل ، ابزار دقیق و اتوماسیون

صفحه اصلی / هشتمین کنفرانس بین المللی کنترل ، ابزار دقیق و اتوماسیون

Using Deep Learning Network for Fault Detection in UAV

نویسندگان :

Armin Mahdi Erfanian¹ Amin Ramezani²

1- دانشگاه تربیت مدرس 2- دانشگاه تربیت مدرس

کلمات کلیدی :

UAV, Quadrotor, Fault detection, BLSTM, Deep learning.

چکیده :

Unmanned aerial vehicles (UAV), especially quadrotors, have received much attention in recent years. The most important challenge in a drone system is fault detection. therefore, in this article, we used the Long short-term memory algorithm (LSTM) to detect faults. in other words we were able to detect the actuator fault of quadrotor using LSTM algorithm, used in the field of deep learning. In this research, we first simulated the quadrotor model using PID controller and then extracted the data and through this we were able to train the network and eventually detect the fault by collecting data from various state variables and inputs at different frequencies and amplitudes. The simulation results show this method can be effective in detecting faults in quadrotor and its accuracy reaches 99%.

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