###### Abstract
However, the longitudinal dynamics of commercial vehicles exhibit strong nonlinear characteristics due to factors such as variations in vehicle mass, road gradients, and disturbances like headwinds [14; 15; 16; 17]. In addition, the physical limitations imposed by actuator dynamics further complicate the design of precise speed control systems [18; 19; 20]. According to [21], extensive research has been conducted to overcome these challenges by exploring robust control techniques suitable for commercial vehicle applications. Several different control methodologies have been proposed to address these limitations, with model reference adaptive control MRAC [22; 23; 24; 25; 26; 27; 28] considered a promising approach for enhancing the longitudinal control performance of HDVs. This is due to its ability to handle external disturbances and actuator limitations. For example, the author of [29] introduced a model reference adaptive velocity-control system for autonomous vehicles. This approach combines PID control with MRAC and enables robust vehicle control across various road conditions and environments. In [30], a conventional MRAC framework for an adaptive cruise control (ACC) system was proposed. This framework is based on a longitudinal dynamic model that considers uncertainties in the state and input matrices. The study demonstrated through simulations that MRAC outperforms linear state feedback controllers, even in uncertain environments. While these studies highlight the effectiveness of MRAC in longitudinal velocity control for autonomous vehicles, they do not explicitly address problems related to the high-frequency noise and input constraints inherent in MRAC. In dynamic environments such as those encountered by HDVs, limitations in control input can lead to degraded tracking performance. The author of [31] introduced a state predictor to address the trade-off between fast adaptation and smooth control signals in conventional MRAC systems. The proposed approach incorporates a predictor-error term into the adaptation law, which makes it possible to effectively suppress high-frequency noise in the control signal caused by the high learning rate of MRAC. As a result, smoother and more stable control signals can be achieved. However, because this approach does not consider the actuator characteristics of the vehicle, tracking performance can significantly deteriorate in the presence of input constraints. In addition, the author of [32] reported an MRAC control framework designed to handle scenarios where input constraints are present in the control system. This approach adaptively modifies the reference model to suppress actuator saturation caused by the control law, thereby enhancing the practicality and robustness of MRAC, even under input-constrained conditions. However, while this approach addresses problems when input constraints exist, it does not account for instabilities such as high-frequency noise in the control signals, which arise due to the high learning rate of the adaptive law. These limitations reveal that existing MRAC-based approaches do not simultaneously address the challenges of high-frequency oscillations and actuator saturation--both critical factors for achieving safe and practical speed control in HDVs. To address these issues, this study proposes three key contributions. First, an MRAC strategy is introduced to suppress external disturbances and enable robust real-time adaptation under dynamic conditions. Second, input constraint-aware model reference design is employed to reduce actuator saturation while maintaining desired performance. Third, a low-pass filter is incorporated into the adaptation law to minimize high-frequency control noise, thereby ensuring smoother control input and mitigating actuator load. These contributions are tailored specifically for HDVs, which are highly sensitive to both control effort and external disturbance due to their large mass and nonlinear dynamics.