This paper includes attitude controller design ideas for a quadrotor platform which can be regarded as an exceptionally agile flying robot with highly non-linear and unstable features in flight dynamics. The quadrotor poses severe problems in characterizing the dynamics especially when performing high-speed manoeuvres. These facts cause the quadrotor not to lose its popularity as a compelling tool among avid researchers who endeavour to realize various controller ideas. The procedure in this paper is initiated with the construction of the system model and the verification of this phase relying on the characteristics of the test bed. With the aid of sensors on the off-the-shelf platform, the controllers are designed to enact tracking of the reference commands that contain the desired trajectories and attitudes. The controller methods highlighted in this research are non-linear dynamic inversion, model reference adaptive control and integral back-stepping technique. The trade-off between performance and robustness is investigated as well. The responses of the system to the impacts of the existence of uncertain parameters, unmatched uncertainties or disturbances are exceptional means to judge how robust the controller is. An overview of the cases with parametric uncertainty and the existence of noise, therefore, find its place as a section within this work. This sketch grades the controller options while putting forward the advantage of adaptation. Besides, by employing correction approaches, the advancement of the adaptive controller in terms of robustness is examined where dead zone implementation, parameter bounding, e-, and σ-modifications are exploited. The motivation behind this research is to produce persistent state controllers to lay the first stone for more complex algorithm structures such as autonomous flight phases, obstacle avoidance and way-point targeting. The future work of this study is the justification of the reliability of the methodologies used and the results attained from the simulations through experiments.