This paper addresses the solution of a trajectory tracking a quadrotor considering plant parameter variations, proposing a novel model. First a novel model of the quadrotor is developed, considering different phenomena such as: blade flapping and drag force. By means of a discrete-time Recurrent High-Order Neural Network trained with an extended Kalman filter algorithm, neural model is identified for the quadrotor. Then, it is transformed using the block control technique in order to design a sliding surface, which depends on the tracking errors. Finally, a non-switching discrete-time sliding mode control is applied to ensure the aforementioned sliding surface be attractive. The effectiveness of the proposed based on simulations.