Preview–repetitive control method for vehicle platoon under constant-speed cruise based on information fusion

To address the vulnerability of vehicle platoons to complex external disturbances during cooperative lane-changing, this paper proposes an information-fusion-based preview-repetitive control (PRC) method. First, a two-degree-of-freedom lateral dynamic model and a discrete state-space representation with communication topology are constructed. On this basis, preview control and repetitive control are synergistically integrated. By mathematically fusing the control increment, system co-state estimation, and multi-source preview data, the framework simultaneously exploits future trajectory information and historical disturbance learning to eliminate periodic residual errors without requiring numerical optimisation. Finally, simulation results demonstrate that under sinusoidal and noisy disturbances, PRC reduces the peak-to-peak lateral error to 4.5% and the error standard deviation by 89.9% compared to Model Predictive Control (MPC), while accelerating convergence by 54.5%. The proposed PRC offers a highly scalable, high-frequency execution mechanism with superior robustness for automated platoons.