Abstract: The control systems of mobile robots and unmanned vehicles are typical examples of time-delay systems. Time delays in these systems primarily stem from the signal transmission process. After a control signal is generated by the controller, it must travel through a communication bus to reach the actuator, where it is executed. This transmission process incurs delays due to several factors, including signal propagation time along the communication lines and the buffering and reading operations performed by the bus system. These apparently minor delays can have a significant impact on the performance and stability of control systems, particularly in high-precision applications such as path tracking. Path tracking control is a fundamental function of mobile robots and unmanned vehicles. It ensures that the controlled object follows a predefined path as accurately as possible. Recently, there has been growing interest in addressing the problem of signal time delay (STD) within path tracking control systems. However, existing research tends to focus on specific control strategies, and there is a notable dearth of comprehensive solutions with generalized applicability. Among various control strategies, nonlinear model predictive control (NMPC) has garnered considerable attention due to its ability to explicitly handle system constraints, perform multi-objective optimization, and utilize future reference trajectory information. These features render NMPC particularly well-suited to complex and dynamic control environments, such as those in which mobile robots are typically employed. Despite these advantages, research addressing the influence of STD on NMPC-based path tracking systems remains limited. This knowledge gap restricts the deployment of NMPC in real-world autonomous vehicle applications where time delays are unavoidable. To address these issues, this research proposes and validates a novel approach for mitigating the adverse effects of STD on NMPC-based path tracking control systems for car-like robots. First, we developed a path tracking control framework that can effectively isolate and analyze the influence of STD. Subsequently, the underlying mechanism through which STD affects NMPC control is examined. It was observed that STD causes a mismatch between the position of the robot used by the controller to generate control inputs and the actual position of the robot when these inputs are executed, thereby degrading control accuracy and system stability. As a solution, this study proposes an STD compensation method that extends the prediction horizon. Specifically, by keeping the iteration period constant and increasing the number of prediction steps, the predictive model can effectively accommodate the time delay introduced by STD. The number of additional prediction steps required is determined as the nearest integer to twice the ratio of STD to the control period. The proposed method is validated through both simulation and experimental studies. The results demonstrate that the presence of STD significantly affects the performance of NMPC-based path tracking systems. In particular, although NMPC without STD consideration performs well under ideal conditions, it fails to maintain accurate tracking when STD is present. In contrast, the proposed compensation method effectively reduces the impact of STD, maintaining a maximum displacement error of 0.1258 m and a maximum heading error of 0.0583 rad in systems subjected to STD of approximately 0.2 s. These findings confirm that the proposed approach enhances the robustness and reliability of NMPC path tracking control systems in realistic, delay-affected environments.