## 1 Introduction
In their research, instead of using Euler angles to model the quadrotor's attitude system, they used a unit quaternion formulation to avoid the singularity problem presence in Euler formulism. They tested their controller performance in two situations, one without the external disturbance and the second with the external disturbance. The simulation result showed that the controller successfully stabilized the attitude of the quadrotor in both situations. However, a slight overshoot occurred in the attitude rate response when the external disturbance was introduced but could still guarantee the quadrotor's stabilization. [PERSON] and [PERSON] [26] conducted a comparative study of SMC with the conventional PID controller to control the quadrotor's altitude and attitude angle. The dynamic model of the quadrotor was formulated using the Newton-Euler approach. A boundary layer around the sliding surface was introduced to ensure the chattering problem was eliminated. In the paper, a sigmoid function was used for the altitude controller, and a saturation function was used in the attitude controller. The finding showed that SMC gave faster response time and less oscillation when tracking the desired altitude and attitude angle compared to the PID controller. [PERSON] and [PERSON] [27] presented a study of a decentralized adaptive controller based on an improved Lyapunov-based model reference adaptive control (MRAC) method for altitude and attitude control of the quadrotor in the presence of parametric and non-parametric uncertainties. In this paper, simulation and implementation of the real quadrotor system were conducted. The finding shows that the proposed controller can stabilize the quadrotor. The performance index, overshoot, and settling time of the proposed controller was better than the PID controller. [PERSON], [PERSON] [28] presented a study of altitude and attitude control of the quadrotor under internal and external disturbances using active disturbance rejection control (ADRC). In this paper, a dynamic surface control algorithm was used to design the controller based on the estimated states by extended state observer (ESO). The dynamic surface control could ensure the robustness and adaptability of the system with uncertainties and external disturbance. The simulation result showed that the proposed controller successfully tracked the disturbance in each subsystem effectively. Even with a sudden change in disturbance, ESO could estimate new states in a short time. The controller could track the desired path rapidly with good tracking performance under disturbance. A comparative study between traditional ADRC and ADRC-SMC shows that the proposed controller has better stability and robustness under internal and external disturbance than others with better tracking performance and smaller overshoot. [PERSON] and [PERSON] [29] presented a fuzzy logic controller (FLC) for controlling the quadrotor's position and attitude angle under disturbance conditions. In this paper, the fuzzy logic controller was implemented using two inference engines: the Mamdani and Takagi-Sugeno-Kang fuzzy (TSK) models. A comparative study between those two-inference models was then done to evaluate the controller's performance. Three simulation tests were conducted; (1) without disturbance, (2) the controller subjected to sensor noise, and (3) the controller subjected to sensor noise and a medium wind gust of 10 m/s. The finding showed that the fuzzy logic controller with both inference engine give satisfactory performance even in sensor noise and wind disturbance with +4: \(-3^{\circ}\) error in attitude angle. However, the fuzzy logic controller based on the Mamdani inference engine gave a faster response time than the TSK model with a significant drift in the yaw angle under disturbance. Real implementation showed that the attitude angle error fluctuated around +7: \(-8^{\circ}\) for pitch and +12: \(-6^{\circ}\) for the roll.