Data-Driven Robust Predictive Control for Mixed Vehicle Platoons Using Noisy Measurement

This paper investigates cooperative adaptive cruise control (CACC) for mixed platoons consisting of both human-driven vehicles (HVs) and automated (AVs). research is critical because the penetration rate AVs in transportation system will remain unsaturated a long time. Uncertainties randomness are prevalent human driving behaviours highly affect platoon safety stability, which need to be considered CACC design. A further challenge difficulty know exact models HVs powertrain parameters HVs. To address these challenges, this proposes data-driven model predictive (MPC) that does not or parameters. The MPC design adopts technique reachability predict future trajectory within given horizon based on noisy vehicle measurements. Compared classic (ACC) existing dynamic programming (ADP), proposed ensures satisfaction constraints such as acceleration limit safe inter-vehicular gap. With salient feature, has provably guarantee establishing robustly stable despite velocity changes leading vehicle. efficacy advantage verified through comparison with ACC ADP methods small large platoons.