Neuronal Slidding Mode Output Control Application in a Attitude for Quadrotor

The primary goal of this research report is to investigate the challenging topic of flight dynamics control-regulation over a quadrotor UAV's orientation (attitude) in the presence of wind disturbances and unmodeled dynamics. To do this, we first create a viable output feedback controller-regulator that incorporates Filters High Gain Observers (FHGO) of ultimate generation, feedback and Radial Basis Function Neural Network (RBF) compensation of estimated disturbances. Furthermore, the usage of reliable sliding mode controllers. Then, despite wind dynamics, noise in sensor outputs, velocity estimates, and common modeling mistakes, a quadrotor UAV is maintained at a given orientation utilizing such a controller. Finally, the effectiveness of the suggested controller is investigated in a simulation framework using a realistic physical model of a quadrotor UAV.