Application of LSTM neural network based UAV attitude control in attitude estimation

From now on, Unmanned Aerial Vehicle (UAV) technologies have become more mature, and UAV has been skillfully used in various regions under this mature technology. However, during UAV flight, they often encounter interference from external factors such as airflow, wind and temperature changes, which pose challenges to their stability and flight accuracy. This paper proposes an algorithm what is based on LSTM (Long, Short-Term Memory network), aiming that the flight attitude is affected when it is subject to external interference, while still ensuring the robustness of its flight performance in this state. In this paper, the optimization method of anti-jamming adaptive adjustment based on LSTM. In the meantime, a UAV dynamic model and neural network control based on dynamic modeling are established. Then article established the LSTM rule and designed the PID controller of LSTM. And to prove the effectiveness of the designed PID controller by pilot experiments with Matlab / simulated link simulation and flight experiments. Finally, step size response, autonomous tracking, robustness and anti-interference were tested by experimental simulations.