Adaptive Longitudinal Speed Control for Heavy-Duty Vehicles Considering Actuator Constraints and Disturbances Using Simulation Validation - Junyoung Lee; Taeyoung Oh; Jinwoo Yoo (Applied Sciences, 2025)
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 μ-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 TruckMaker 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.