These approaches focus on enhancing system performance and robustness through sophisticated methods. While such methods provide significant improvements, they often come with increased computational complexity. In contrast, the present study validates the application of a standard PID controller, offering a simpler and more practical solution for scenarios where extreme precision is not required but robustness and simplicity are prioritized. Furthermore, [PERSON]. [10] analyzed the impact of sampling effects on the absolute stability of discrete-time bilateral teleoperation systems, highlighting the importance of appropriate sampling periods in maintaining system stability. Additionally, Ref. [11] proposed a position and force tracking control based on force estimation, utilizing a time-delay state observer to estimate system state variables affected by delays. The research in [12] introduced an adaptive PD control method using deep reinforcement learning to compensate for stochastic time delays in local-remote teleoperation systems. Similarly, in [13], an adaptive observer-based scaled four-channel control approach, applying damping injection for nonlinear teleoperation systems, is presented. This approach unifies the study of robotic dynamic uncertainties, operator/environment force acquisitions, and asymmetric time-varying delays. In [14], a bounded force feedback control law is introduced to improve transparency in nonlinear bilateral teleoperation systems in the presence of input saturation, asymmetric time-varying communication delays, and parametric uncertainties. The proposed controller is a nonlinear proportional plus nonlinear damping controller with the addition of an adaptive term. Another widely employed technique is Neural Networks (NNs). For instance, in [15], an NN-based four-channel wave-based time domain passivity approach (TDPA) is proposed for a teleoperation system with time-varying delays. The applied NN is used to eliminate the system's dynamic uncertainties. Under the linearity assumption on the human and environment, the stability analysis is carried out by using the Lyapunov method. In [16], a control scheme is developed for a teleoperation system, combining radial basis function neural networks (RBFNNs) and wave variable techniques to simultaneously compensate for the effects caused by communication delays and dynamical uncertainties. To overcome the potential instability problem in the presence of delayed communication channels, wave variables and their corrections are effectively embedded in the control system, and a Lyapunov-based analysis is performed to theoretically establish the closed-loop stability. In [17], an adaptive control architecture based on velocity feedback filters and RBFNNs is proposed to achieve stable and effective position synchronization control for bilateral systems with time-varying delays. In [18], an adaptive neural learning fixed-time control scheme incorporating an integral barrier Lyapunov function is proposed, while NNs are utilized to reconstruct environmental forces and to approximate the total uncertainty introduced by robots and the environment. In [19], the problem of position synchronization for master-slave robots is solved by using an RBFNN together with adaptive control methods to deal with the system uncertainties. Furthermore, the design of virtual variables can effectively compensate for the effect of time-varying delays. In [20], a finite-time control method is designed on the basis of the traditional proportional damping injection control by introducing a non-integer power to the position error, velocity, and the combined error with position error and velocity. The controller also incorporates an RBFNN and an adaptive approach to compensate for unknown dynamics and external forces, thus avoiding the measurement of force signals. Another methodology frequently used is Fuzzy Logic (FL).