###### Abstract
On the one hand, flight control essentially alludes to the ability to carry out highly time-sensitive sensory data acquirement, processing, and the computation of forces for application to aircraft actuators. On the other hand, to name a few, it is advisable that UAV flight controllers are able to withstand faults, adapt to changes in payload and/or the environment, and improve the flight path. UAV flight control systems are mostly implemented using Proportional-Integral-Derivative (PID) control systems. PID has shown exceptional performance in many conditions, including in the context of drone racing, where accuracy and lightness are key factors. In contrast, it has many handicaps: (1) at times, it provides a high control signal due to wind, thus overshooting and continuing to increase as the accumulated error is unwound; and (2) the differentiator leads to the amplification of noise. The PID controller system is the preferred control method [8]. The PID controller has only three parameters, giving it an easy adjustment space to obtain a better control point [9]. It is evident that fast response speed and high controllability can be achieved by implementing proper PID parameters [10]. Therefore, the method of tuning PID parameters has become one of the hot research topics in the engineering field. In stable environments, a PID controller displays close to the best possible performance. When bared to unknown dynamics, such as variable payloads, wind and voltage drops, however, a PID controller can be far from optimal [11]. On the other hand, because quadcopters are dynamically unstable, under-actuated and nonlinear systems, it is difficult to design a quadcopter's PID controller to track a moving target, particularly under the variable speed of the moving target and under the influence of the environment. This is because any error that occurs in any of them can cause serious damage to the quadcopter. The PID controller successfully tracks a reference altitude, despite undergoing a large overshoot on takeoff. This may be related to the ground impact, which produces an unexpectedly strong lift before the quadcopter surveys the ground. Understanding how to balance the different aspects of flight to make a quadcopter respond optimally to a particular flight style is not a simple matter that can happen overnight. Practical knowledge of PID tuning helps to achieve this easily, and the more familiar the operator is with the PID settings, the easier it is to set the codecs to fly in any way. Conventional methods, such as Ziegler-Nichols (ZN) [12], for the PID controller does not guarantee optimal control for a quadcopter. This method might cause system instability as well as major losses and damages to the system. Therefore, a more efficient control method is needed for the quadcopter that can make the system very stable with minimal losses and without damaging the system. A secondary goal is to achieve reliable and proper movements and directions for the quadcopter. For next-generation flight control systems to be intelligent, there is a need to devise a way to integrate adaptability to the changeable environment and dynamics. Accordingly, the evolution of intelligent flight control systems remains an active research domain [13], concretely through the use of artificial neural networks, which are an appealing choice that have been agreed on as global approximators, and noise-resistance [14]. Online learning methods [15] have the merit of learning the dynamics of an aircraft in real time. The key restriction with these methods is that the flight control system is only aware of its past experiences. It follows that its performance level is constrained when exposed to an unprecedented event. Offline training models using supervised learning are problematic, as data acquisition is prohibitively expensive and derived from imprecise representations of rudimentary aircraft dynamics, which can result in suboptimal control policies [16].