On one hand, we are aware of the fact that quadrotors have been becoming a part of our daily life day to day; on the other hand, their control is still a challenging task as, unlike from the ground vehicles, they do not have enough friction forces to stabilize their motion. What is more, quadrotor's six DOF motion (three translational and three rotational) is controlled by varying only the speeds of its four independent rotors, resulting in under-actuated, highly nonlinear and coupled dynamics. In this paper, conventional proportional-derivative (PD), Mamdani-type fuzzy and TSK-type fuzzy neural network-based controllers have been designed, and their performance have been compared based on both control accuracy and control effort. A realistic trajectory, which is feasible regarding the input constraints of the quadrotor, is generated to test the accuracy and efficiency of the proposed methods. Realistic uncertainties, such as wind and gust conditions, are also given to the system to demonstrate the robustness of the controllers in real-time operation. The adaptive fuzzy-neural controller gives the most accurate trajectory tracking results for a 4D trajectory reducing the error by a factor of 4 when compared to the conventional PD and fuzzy controller although the control effort increases only by 10%.