## 6 Conclusions  
This study proposed a design methodology for the model reference longitudinal speed control of HDVs, in which a predefined reference model guides the vehicle's behavior to achieve the desired dynamic performance. The proposed method explicitly considers uncertainties and dynamic characteristics encountered in complex driving environments. By integrating \(\upmu\)-modification adaptive control, the developed closed-loop control system effectively addresses the input limitations that conventional MRAC systems fail to handle. In addition, the system incorporates a state predictor to suppress high-frequency noise. This feature reduces the actuator load, which increases its suitability for HDVs. The stability of the proposed closed-loop control system was analytically verified using Lyapunov stability conditions. Robustness and performance were further validated through various experimental scenarios that simulated realistic, complex environmental and dynamic conditions. The primary contribution of this study was the identification and mitigation of the limitations associated with conventional control approaches, such as LQR and standard MRAC, especially in environments where HDV acceleration inputs are highly sensitive to external disturbances and vehicle conditions. Unlike fixed-gain control methods such as PID and LQR, which struggle to maintain performance under rapidly changing conditions, the proposed adaptive approach dynamically adjusts control parameters in real time. This adaptability is critical in HDV applications where system dynamics are heavily influenced by time-varying payloads and environmental disturbances. The proposed controller modifies the control input in real time, explicitly considering acceleration input limits arising from variations in vehicle mass and environmental conditions. This capability ensures accurate tracking of target speeds. Furthermore, the controller effectively suppresses sudden changes in control inputs and high-frequency noise. Consequently, the actuation commands remain smooth, which reduces actuator overload and wear. The effectiveness of the proposed control system was demonstrated in three practical scenarios: hill climbing, headwind conditions, and variations in vehicle mass. Across all scenarios, the proposed system consistently showed superior performance in terms of target speed tracking and control input stability. Most existing studies have primarily focused on improving tracking performance without explicitly addressing actuator limitations. However, actuator constraints significantly influence the stability and durability of HDV control systems in practical applications. Therefore, the development of adaptive control strategies that explicitly account for actuator dynamics and constraints is essential for ensuring both reliable speed-tracking and long-term system robustness of HDV control systems. Despite the demonstrated effectiveness of the proposed approach, several limitations remain. First, the current control framework focuses solely on longitudinal dynamics and does not consider integration with lateral control strategies, which would be essential for handling complex maneuvers, such as curved road tracking or lane changes. Second, although the simulation scenarios include realistic environmental disturbances--such as road slopes, variable payloads, and headwinds--they do not account for sensor-level uncertainties, such as measurement noise, parameter estimation errors, or data delays, which can significantly impact real-world implementations. Future research will address these aspects to further enhance the robustness and practical applicability of the proposed control system. In particular, we plan to implement and test the proposed control system on a real HDV to validate its performance under real-world conditions, including varying road gradients, payload levels, and wind disturbances.