# Advanced Servo Control and AI Integration in 3-DoF Platforms for Enhanced Simulation Interactivity
## 1 Introduction  
Simulation training aims to replicate real-world sensory stimuli through human-machine interfaces, possessing three main characteristics: immersion, interactivity, and imagination [1]. Immersion makes individuals feel as if they are in a real-life scenario, interactivity creates continuous interactive states through pre-designed actions, and imagination stimulates human creativity through graphic design, motion decomposition, scene planning, light distribution, shadow configuration, and 3D imagery. Consequently, virtual reality (VR) technology is divided into the establishment of three-dimensional models, visual input devices, auditory input devices, tactile input devices, and mind input devices [2]. VR technology is widely utilized, with well-developed peripheral hardware devices such as Google's Google Cardboard [3], Meta's Oculus [4], HTC's VIVE series [5], Samsung's Gear VR [6], and Sony's PlayStation VR [7]. VR applications are increasingly prevalent for creating virtual prototypes, 3D models, and visualizations. For example, one study proposed a comprehensive framework to achieve immersive services in the augmented remote operation of unmanned aerial vehicles (UAVs), analyzing key performance indicators and addressing VR-based remote and immersive control issues [8]. Another study introduced Vehicle VR, which accurately simulates real environments and specific operating interfaces and equipment, primarily to reduce training costs and enhance fidelity in training scenarios [9]. VR has also been applied in military training, such as the Brazilian Army's TAT VR simulator for pistol shooting, which emphasizes convenience, accessibility, and flexibility [10]. Considering the widespread use of motion platforms and their significant applications in the transportation industry, virtual environments enable training personnel to experience operations more realistically [11]. For example, a study on a four-degree-of-freedom motion chair demonstrated its effectiveness in enhancing flight simulation training, providing an immersive experience through modular design and kinematic control, achieving closed-loop control [12]. In general, multi-degree-of-freedom control systems require sophisticated electromechanical design to achieve accurate trajectory tracking [13]. These systems typically use servo motors paired with reducers to meet the platform's load capacity. However, the mechanical structure of multi-axis motion platforms is highly complex, with several control uncertainties, including inertia, efficiency, and friction coefficients within reducers, as well as nonlinear characteristics caused by gear backlash. Additionally, the weight of the mechanical structure and the combined weight of the users further complicate system modeling. Moreover, motor control modes must adapt to different operating conditions, affecting the precision of three-axis motion control systems. Various studies have investigated load estimation for unknown loads to address these challenges. For instance, [PERSON] et al. applied neural networks and adaptive theory to embedded permanent magnet motors, enabling the online calculation of controller weight values to maintain precise speed control despite parameter variations and additional loads [14]. [PERSON] et al. designed intelligent optimal controllers, integrating them with fuzzy neural networks for controlling permanent magnet motors [15]. [PERSON] et al. used fuzzy control to manage systems with uncertain inertia, ensuring better steady-state responses for permanent magnet motors [16]. Additionally, servo systems performing reciprocating motions often face periodic nonlinear friction. To tackle these challenges, this paper adopts iterative learning control (ILC) to adjust for parameter uncertainties, improving transient performance and eliminating periodic nonlinear disturbances without needing detailed knowledge of the system's complex parameters. This approach is particularly suitable for nonlinear, time-varying, and complexly modeled systems.