Robust path tracking control for autonomous vehicles under parameter uncertainties and network-induced delays

Accurate path tracking for autonomous vehicles is often challenged by parameter uncertainties and network induced delays, which can degrade control performance and threaten system stability. Traditional approaches frequently address these factors separately or rely on complex delay models, resulting in high computational demands and conservative control strategies. To this end, a robust path tracking control strategy is developed by characterizing network-induced delays as variations in the sampling period, enabling a unified and tractable representation of delay effects. Combined with time-varying vehicle dynamics, a polytopic model is constructed that encapsulates both parameter uncertainties and sampling period variations. The linear matrix inequality conditions are subsequently formulated by incorporating stability, performance optimization, and constraints. Compared to existing state-of-the-art techniques, the proposed method reduces lateral tracking error and heading error by 36.36% and 12.00%, respectively, and enhances vehicle stability by 10.00%.