Adaptive model predictive control for robust lateral motion tracking of semi-autonomous vehicles with dynamic parameter variation.

Yeneneh, K; Yoseph, B; Sufe, G · 2025 · PubMed Central

DOI: 10.1038/s41598-025-30352-3

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Summary

This study addresses the challenge of robust lateral motion tracking in semi-autonomous vehicles (SAVs), which operate under dynamically uncertain conditions and shared human-machine control. Traditional controllers often fail to maintain stability and accuracy due to nonlinear vehicle dynamics, time-varying parameters like mass and speed, and external disturbances. The authors propose a novel Adaptive Model Predictive Control (AMPC) framework designed to enhance safety and reliability in SAVs by continuously adapting to real-time variations in vehicle dynamics and accommodating delayed human intervention. The research employs a comprehensive simulation environment developed in MATLAB/Simulink. Vehicle lateral dynamics are modeled using both a kinematic bicycle model for trajectory generation and a nonlinear dynamic bicycle model for controller design, capturing lateral velocity and yaw rate. The AMPC integrates a recursive least squares (RLS) estimator to perform real-time parameter identification, updating internal model matrices for variables such as cornering stiffness and mass. This adaptive mechanism is embedded within a predictive optimization structure that minimizes a quadratic cost function penalizing tracking errors and control effort, while respecting physical constraints on steering angle and rate. The controller was benchmarked against conventional Model Predictive Control (MPC) and Linear Quadratic Regulator (LQR) methods across multiple scenarios, including aggressive lane changes, crosswind disturbances, and low-friction conditions (friction coefficient μ = 0.4). Additionally, human-in-the-loop simulations were conducted to evaluate performance under delayed driver intervention of 1–3 seconds. The results demonstrate that the AMPC significantly outperforms conventional controllers. It achieved a 43% reduction in lateral tracking error and a 37% improvement in yaw angle root mean square error (RMSE) compared to baseline methods. The system maintained peak yaw errors below 0.275 radians even under severe disturbances, with steering control signals remaining smooth and within actuator limits, exhibiting maximum steering rates under 0.48 rad/s. The human-in-the-loop simulations confirmed that the controller could handle delayed driver interventions without compromising vehicle stability or trajectory tracking. These findings validate the AMPC’s superior robustness, adaptability, and real-time performance in managing complex lateral control tasks. The framework provides a scalable solution for enhancing safety in shared-control driving environments by addressing both technical uncertainties and human-centric aspects of Advanced Driver Assistance Systems (ADAS). By improving lateral maneuverability and reducing vulnerability to dynamic disturbances, this work contributes to the development of more reliable semi-autonomous vehicles, with implications for infrastructure design, regulatory policy, and next-generation ADAS interfaces.

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• lane positioning
• steering pattern

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• Theoretical Contribution: computational model