## 1 Introduction
The algorithm was experimentally tested in a real-time experiment and was compared with the conventional PID and sliding mode controllers. The experiment was done in the presence of a disturbance in one of the actuators. The author concluded that the non-linear PID type controller they proposed could give a better performance than conventional PID and sliding mode controller since it can provide a better tracking accuracy. [PERSON], [PERSON] [22] proposed an optimal LQR controller combined with a Kalman filter to control the quadrotor's attitude angle. They conducted the simulation for noise-free and under noise conditions to appreciate Kalman Filter's performance in rejecting the noise. The performance of the LQR controller was then compared with the classical PID controller. The simulation result showed that both controllers can meet the system's requirement under the noise-free condition with no steady-state error. However, the LQR controller did not produce an overshoot and smoother response, while PID had a slight overshoot up to 0.2\({}^{\circ}\). Under the noisy condition, the LQR controller with Kalman Filter can maintain its performance quality than the LQR controller without a Kalman filter. The attitude response error can be reduced from \(\pm\)0.6\({}^{\circ}\) to +0.2: \(-\)0.3\({}^{\circ}\) when the Kalman filter is added. [PERSON] and [PERSON] [23] conducted a study based on the modified quadrotor dynamics. The traditional quadrotor with four control inputs limits its mobility and trajectory capabilities. Thus, an over-actuated quadrotor system is designed by adding a tilting mechanism to the propellers in their respective axes. A derived dynamics model based on Newton-Euler was linearized using feedback linearization to accommodate the H\(\infty\) controller for position and orientation control. The controller efficacy in eliminating the disturbance, sensor noise, and parametric uncertainty was tested and validated. In the first test, the simulation result shows that the controller managed to stabilize the quadrotor in the initial altitude position in less than 2.5 s after eliminating the external disturbance. A 100 Hz frequency with a 0.02 peak of white Gaussian noise was introduced in the sensor noise test. The simulation shows that the controller managed to reject the noise with only 0.003 m and 0.002\({}^{\circ}\) of error in the position and orientation angle. The same result is obtained with the model parameter uncertainty test, where the controller effectively eliminated those errors. Lastly, they also performed a trajectory tracking to test the effectiveness of the controller. As expected, the quadrotor successfully tracked the desired path with only 2 cm drift from the actual path while the orientation angle was approximately 0\({}^{\circ}\). Overall, the controller provided good robustness against external disturbance, sensor noise, and parametric uncertainties. [PERSON] and [PERSON] [24] presented a feedback linearization controller for a trajectory tracking control of a quadrotor. The dynamics of the quadrotor system were separated into two subsystems which are translational and rotational. They used the feedback linearization law to linearize both subsystems of the quadrotor. In their study, two numerical simulations were conducted under time-varying reference trajectory and constant reference trajectory to illustrate the closed-loop system's behavior. The result showed that both simulations produced a similar behavior. The position and the yaw angle of the quadrotor converged toward the desired reference trajectory within \(\approx\)5 s. They concluded that this technique gives the control designer more freedom to increase the controller's performance at the control effort cost. [PERSON], [PERSON] [25] presented a study on dynamics modeling and attitude stabilization control of a quadrotor. A backstepping control technique was design based on the compensation for Coriolis and gyroscope torque control law.