Neural Adaptive PID Control of a Quadrotor using EFK

In this paper, we present a novel trajectory tracking algorithm for a four-rotor air vehicle (quadrotor). The PID controller is developed following an adaptive neuronal technique, and the discrete theory of Lyapunov verifies its stability. Also, the neuronal identification of the UAV dynamic model is presented. Besides, an extended Kalman filter is used in order to filter the signals from the aerial vehicle that are contaminated by measurement noises, and that can affect the quality of the identification. Then, the output errors are re-propagated to adjust the PID gains to reduce the control errors. Finally, the experimental results are presented using a four-rotor aerial vehicle (quadrotor), by comparing the presented proposal with a classical fixed-gain PID.