Design and Implementation of PD-NN Controller optimized Neural Networks for a quad-rotor

In this paper a full approach of modeling and control of a four rotor unmanned air vehicle (UAV) known as quad-rotor aircraft is presented. In fact, a PD and a PD optimized Neural Networks Approaches (PD-NN) are developed to be applied to control a quad-rotor. The goal of this work is to concept a smart Self-Tuning PD controller based on neural networks able to supervise the quad-rotor for an optimized behavior while tracking a desired trajectory. Many challenges could arise if the quad-rotor is navigating in hostile environments presenting irregular disturbances in the form of wind added to the model on each axis. Thus, the quad-rotor is subject to three dimensional unknown static/varying wind disturbances. The quad-rotor has to quickly perform tasks while ensuring stability and accuracy and must behave rapidly with regards to decision making facing disturbances. This technique offers some advantages over conventional control methods such as PD controller. Simulation results are obtained with the use of Matlab/Simulink environment and are founded on a comparative study between PD and PD-NN controllers based on wind disturbances. These later are applied with several degrees of strength to test the quad-rotor behavior. These simulation results are satisfactory and have demonstrated the effectiveness of the proposed PD-NN approach. In fact, this controller has relatively smaller errors than the PD controller and has a better capability to reject disturbances. In addition, it has proven to be highly robust and efficient facing turbulences in the form of wind disturbances.