## 1 Introduction
When the PID parameters for the control object are set to their ideal values, the system outputs a response that is fairly close to the target value. Typically, the method of determining the PID parameters' ideal values is called tuning, and it is often undertaken through trial and error, simulation, or systematic methods such as Ziegler-Nichols, ITAE, and robust PID tuning [17; 18]. However, a significant shortcoming of these approaches is that PID parameters need to be manually adjusted anytime the system's characteristics change. Changes in load, environment, or operating conditions make it difficult to fully automate the tuning procedure. On the other hand, manual tuning can be time consuming and may not always produce the best performance, especially in complicated dynamic systems [19]. To overcome this difficulty, there has been an increase in interest in creating automated tuning techniques and adaptive control schemes that can automatically modify the PID parameters in real time based on altering the system conditions. Although adaptive control techniques and automated tuning methods can substantially simplify the tuning process, they can still be time consuming and need significant computations [20; 21]. Fuzzy logic control (FLC) is one of the techniques that can be used for the autotuning of PID controllers. In a fuzzy PID controller, human knowledge and expertise are used to design the fuzzy rules that determine the control action based on the system's behavior and feedback [22]. There are several difficulties associated with fuzzy logic control, including high dependence on human knowledge and skill; fuzzy rules need to be updated over time, and there is no set method for creating fuzzy controllers [19]. On the other hand, robust nonlinear control techniques such as H-infinity loop shaping, sliding mode control, and so on [23; 24; 25; 26] can be applied. Recently, soft computing techniques such as neural networks have been successfully used in control systems. The use of artificial intelligence (AI) to replace the traditional PID controller can significantly simplify the tuning process and improve the overall performance and robustness of the control system [27; 28; 29]. [PERSON] et al. [30] replaced a PID controller with a neural network in order to control a new low-cost semi-active vehicle suspension system, and it proved to have good accuracy in terms of vibration reduction and response time. [PERSON] et al. [31] presented a 3-DOF delta robot trajectory-tracking control using an online neural network self-tuned inverse dynamic controller. [PERSON] et al. [32] proposed an enhanced PID controller with an artificial neural network (ANN) to increase the position control efficiency of a 2-DOF robotic arm. The controller has shown robust and flexible results. Although control approaches have been proposed for stabilizing UAV platform mechanisms, most existing works rely on classical control techniques. Furthermore, previous studies lack a comparative analysis of the performance of different stabilizing mechanisms, which limits insights into the impact of robot architecture on stabilized motion. This paper aims to address these gaps by proposing a parallel mechanism for UAV platform stabilization, utilizing modern control techniques for mechanism control. A comparative study is conducted on three different parallel manipulators. Initially, the 3-SRR/SRU PM is investigated, and a control technique based on both kinematical and dynamical models is developed, extending previous works. A new control strategy is proposed, which significantly improves the output values. The proposed control strategy is then applied to the 3-RRS PM, and the resulting motion of the moving plate is examined and compared with the 3-SRR/SRU PM. Based on further investigation of the results, a 3-SRR PM is proposed, which better addresses the stabilization problem of the moving platform.