###### Abstract  
Heavy-duty vehicles (HDVs), such as buses and commercial trucks, display unique dynamic characteristics due to their high mass and specific actuator properties. These factors make HDVs particularly sensitive to changes in vehicle load and road gradient, which significantly affect their longitudinal control performance. In other words, such variations present considerable challenges in maintaining stable and efficient longitudinal control of HDVs. To address these challenges, this study proposes a model reference adaptive control (MRAC) framework explicitly designed for HDVs. The control system utilizes a state predictor to mitigate actuator load problems caused by high-frequency components in the adaptive control input. In addition, when input constraints are present, the reference model is modified using the \(\upmu\)-modification technique. The system satisfies Lyapunov stability conditions and ensures stable longitudinal control performance across a range of driving conditions. The proposed closed-loop longitudinal control system was evaluated by implementing the controller using the vehicle dynamics simulation software IPG Truck-Maker 12.0.1 and integrated with MATLAB/Simulink R2022b. The test scenarios included repetitive speed change maneuvers, which accounted for uncertainties such as road gradients, headwinds, and vehicle load conditions. The simulation results show that the control system not only effectively suppresses disturbances but also enables stable longitudinal speed tracking by considering actuator load and constraints, outperforming conventional MRAC. These results suggest that the proposed closed-loop longitudinal control system can be effectively applied to HDVs. The findings suggest that the proposed closed-loop longitudinal control system can be effectively applied to HDVs, ensuring improved stability and performance under real-world driving conditions. heavy-duty vehicle; longitudinal control; adaptive control; model reference adaptive control (MRAC); payload variation; actuator constraints; vehicle dynamics 2020 ac  
traffic accidents involving large commercial vehicles [4; 5; 6]. To mitigate these risks, the development of Level 4 autonomous driving technology for commercial vehicles is receiving significant attention, as it can effectively reduce the need for continuous driver intervention and improve overall driving safety [7; 8]. Autonomous driving for commercial vehicles relies heavily on the fusion of multiple sensor inputs to accurately perceive the surrounding environment, identify drivable space, and generate and execute safe and efficient driving plans [9; 10]. One of the key requirements for the successful deployment of autonomous driving systems in commercial vehicles is precise longitudinal speed tracking, especially under varying road and traffic conditions. Accurate speed control is key not only to maintaining safe distances from surrounding vehicles but also to ensuring stable vehicle behavior during various driving scenarios. Examples include highway cruising, stop-and-go traffic, and uphill or downhill driving. Due to the inherent characteristics of commercial vehicles, including high mass, time-varying dynamic properties, and non-linear relationships between control inputs and vehicle responses, conventional fixed-parameter controllers often struggle to maintain consistent speed-tracking performance [11; 12; 13]. In order to address these challenges, this study proposes an adaptive control framework that can adapt in real time to external disturbances and variations in vehicle dynamics. This approach improves the robustness and tracking accuracy of longitudinal speed control for commercial vehicles. Cruise control systems demand high-performance speed-tracking capabilities to follow target speeds accurately, even when external disturbances are present.