## 1 Introduction  
Significant and rapid advances in unmanned aerial vehicle (UAV) technology have made them economically viable and easily maintainable. They have been used in research and in civil, military, and commercial applications, such as military UAVs, search and rescue, and agriculture [1; 2; 3; 4; 5]. Even though they can complete tasks, they still face many challenging problems because of the sensor accuracy and the coupled highly nonlinear system dynamics. Considering the controller of the UAV, which is one of the challenging problems, encouraged many researchers to take on such challenges and propose several control designs, such as proportional-integral-derivative (PID) [6; 7], fuzzy PID control (FPID or T1-FPID) [8; 9; 10], linear quadratic regulator (LQR) [11; 12], linear quadratic Gaussian and loop transfer recovery (LQG/LTR) [13], model predictive control (MPC) [12; 14; 15] and Hoo [16; 17] control algorithms.  
The controller algorithms and the calculation of their parameters are directly related to the physical dynamics of the system. However, the physical dynamics of the UAV represent a challenge for many of the control algorithms implemented on these UAVs, mainly when the control parameters are determined by using limited known dynamic model parameters. Moreover, knowing all parameters of the physical dynamics is almost impossible due to uncertainties or inaccessible parameters. Therefore, the stable controller parameter might cross to the unstable region in the presence of uncertainty or inaccessible parameters. To explain and illustrate the problem, it is scaled and tested using a mini UAV. For indoor experimental research, nano or mini UAVs have proven their efficiency concerning easy access, small sizes and weights [18]. As a result, researchers have used several commercial UAVs, such as Bitcraze Crazyflie 2.0 [8; 19; 20; 21; 22], Parrot Mambo Minidrone [9; 10; 12] and DJI Tello [23; 24; 25].  
In many studies, the connection with the payload represents a further research challenge [23; 26; 27; 28]; the control problem under uncertain payloads is solved using reinforcement learning. The UAV has a flexible cable, payloads, and magnets. When using the magnets, the payloads can connect to UAV. Hence, the weights cannot be known, adding additional uncertain parameters. Thanks to model-based meta-reinforcement learning, the system's inputs and outputs are trained and created model-based data using a human-based remote controller. After designing a model-based meta-reinforcement learning dynamic model, a model predictive control algorithm is implemented to cope with such effects [23].  
UAVs face other challenges not only due to the payload but also due to wind disturbances. In [27], a path following a control algorithm for a UAV with cable-connected payload is designed under wind disturbances. In addition, near hovering and cruising states are included in the control, as well as an employed position-based tracking control algorithm for the outer structure. A whole system model is developed with payload and disturbance effects, meaning that they are not unknown parameters in [27]. Considering unknown parameters, such as the payload mass, an excellent example of associated research work can be attributed to [26] on adaptive UAV control for a cable-suspended unknown payload. The system is tested with a nonlinear and adaptive PD controller. The coefficients/parameters of the UAV controllers are tuned via many different optimisation methods [29], and each of them has its advantages and disadvantages. In [30], a comparative study of different controllers for UAVs is investigated in terms of limitations and strength parts. In this study, we focus on fuzzy-based PID controllers since they have better accuracy than PID controllers and are computationally efficient, which makes them applicable to UAVs in real time [7].