Contents I. Introduction
Unmanned Aerial Vehicle UAV research is a prominent topic of study. UAV research began in the mid-1800s, while drones small sized UAVs began in the early 1900s, becoming increasingly common during World War I. QuadCopter is the most popular UAV because of its compact size and good stability. It can take off and land vertically. It can quickly alter course to reach previously inaccessible locations [1] . Quadcopters are mostly utilized in military, civil, and agricultural applications, such as identifying unknown things, enemies, and border patrols. Quadcopters have recently been employed in the transportation of small products. To enable UAVs to fly autonomously, control rules that substitute the actions of a human pilot must be devised. For many years, linear control approaches such as PID were used to overcome this problem. Although the system is inherently nonlinear, input-output linearization must be used to construct the required controllers. Proportional, integral, and derivative (PID) and proportional and derivative (PD) controllers are used to model quadcopter pitch and roll moments. PID and PD responses to pitch and roll moments are examined in selecting the most suitable controller for pitch and roll moments. PID is utilized to stabilize the response of quadcopter dynamic motions, and the results are compared using PD and P controllers. Another well-known option is to employ backstepping techniques, which include seeing the control design model as a chain of integrators. Traditional PID controller approaches, such as Ziegler-Nichols (ZN) [2] [3] , do not ensure optimum control for a quadcopter. This strategy may result in system instability and high costs, and harm. As a result, a more efficient control approach for the quadcopter is required to make the system highly stable with little losses and without injuring the system.