Accurate and robust trajectory tracking under varying vehicle dynamics remains a major challenge for autonomous and semi-autonomous vehicles, where safety, comfort, and stability must be ensured despite nonlinearities and uncertain parameters such as mass variation, tire stiffness, and road friction. This study presents a Robust Model Predictive Control framework that explicitly accounts for parameter uncertainty and external disturbances while maintaining computational efficiency suitable for embedded automotive systems. The controller integrates multi-parametric predictive modeling and robust optimization with constraint tightening, enabling proactive adjustment of steering commands and sustained stability across diverse driving conditions. A detailed nonlinear vehicle model incorporating lateral, yaw, and load-transfer dynamics was developed, and the proposed controller's performance was benchmarked against proportional integral derivative and linear predictive control strategies. Simulation results demonstrate major improvements, including up to 78% reduction in lateral tracking error, 71% enhancement in yaw stability, 68% reduction in settling time, 61% smoother steering actuation, and 57% improvement in disturbance rejection compared to conventional controllers. Moreover, the proposed method maintains real-time feasibility with an average computation time of 6.3 milliseconds per control cycle, confirming its suitability for on-board electronic control units. Compared to classical robust strategies such as Sliding Mode Control, the proposed approach provides improved smoothness and explicit constraint handling while maintaining strong robustness to uncertainties.These results establish that the proposed robust predictive framework significantly enhances accuracy, stability, and computational efficiency under nonlinear and uncertain operating conditions. Overall, it provides a practical, scalable, and reliable foundation for next-generation intelligent vehicle control systems capable of safe and adaptive trajectory tracking in real-world environments.
Yeneneh et al. (Tue,) studied this question.