This manuscript presents a control law based on the kinematic control concept and the input–output linearization approach. More specifically, the given approach has the structure of a two-loop controller. A rigorous closed-loop system analysis is presented by using known theory on perturbed systems. By assuming that the desired velocity in the body frame is persistently exciting, the uniform bound of the tracking error in earth coordinates is ensured. A simulation study using practical mobile robot parameters shows the viability of the introduced approach. In addition, two known trajectory tracking controllers are simulated in order to compare the performance of the proposed technique. Better tracking accuracy is obtained with the proposed control approach, even if uncertainties in the knowledge of the friction coefficients are presented.

Bilateral teleoperation systems face significant challenges in achieving accurate master-slave position convergence, especially under the influence of time-varying communication delays. This document presents an adaptive neural network compensation strategy within a proportional-derivative-like (P+d) control framework. The proposed controller addresses uncertainties associated with time delays and system dynamics using online adaptation of neural networks. Furthermore, a rigorous theoretical analysis of the closed-loop teleoperation system is presented and validated through numerical simulations on two-degree-of-freedom manipulators. Finally, the results demonstrate that adaptive neural networks can significantly improve the operational reliability of bilateral teleoperation systems in dynamic environments.

Bilateral teleoperation of skid-steering mobile robots with time-varying delays presents significant challenges in ensuring accurate leader–follower coupling. This article presents a novel controller for a bilateral teleoperation system composed of a robot manipulator and a skid-steering mobile robot. The proposed controller leverages neural networks to compensate for ground–robot interactions, uncertain dynamics, and communication delays. The control strategy integrates a shared scheme between damping injection and two neural networks, enhancing the robustness and adaptability of the delayed system. A rigorous theoretical analysis of the closed-loop teleoperation system is provided, establishing conditions of control parameters to ensure stability and convergence of the coordination errors. The proposed method is validated through numerical testing, demonstrating strong agreement between theoretical outcomes and simulation results.