Robust path tracking for autonomous vehicles under measurement uncertainties induced by signal deficiencies and varying dynamics

The accuracy and integrity of measurement signals are essential for path-tracking control (PTC) in autonomous vehicles. However, signal deficiencies, such as data packet dropouts, lead to sensor and control signal loss, while time-varying vehicle dynamics introduce additional uncertainties, significantly degrading PTC performance. To this end, a robust PTC strategy that accounts for measurement uncertainties induced by signal deficiency and varying dynamics is proposed. First, a comprehensive model is established to jointly characterize uncertainties arising from signal deficiencies and varying vehicle dynamics. Dynamic-induced uncertainties are represented as bounded matrices, while variations in signal deficiencies are modeled through a Markov chain. This unified framework enables robust controller design without requiring precise prior knowledge of uncertain parameters. Second, a real-time robust PTC strategy is proposed, where stability conditions are derived to constrain the effects of disturbances, dynamic variations, and signal deficiencies. The cone complementarity linearization (CCL) method is employed to ensure real-time implementability. Simulation and experimental results demonstrate that the proposed method effectively mitigates performance degradation, reducing lateral and heading errors by 29.92% and 14.26%, respectively, compared with existing methods.