Cooperative adaptive cruise control (CACC) is one of the promising intelligent transportation technologies. Considering that connected and automated vehicles (CAVs) and human-driven vehicles (HDVs) will coexist for a long period, the design of CACC methods in mixed traffic flow becomes critical. Theoretically, the maneuvers of CAVs can be controlled based on the prediction results of HDVs. However, considering the stochastic nature of human drivers, the prediction uncertainty cannot be avoided and could accumulate significantly. To handle the uncertainty, one method utilizes feedback control, which cannot explicitly satisfy constraints and usually requires wireless connectivity of HDVs. The other method utilizes time-triggered feedforward control (e.g., Model Predictive Control, MPC), which suffers from the huge burden of inter-vehicular communication and computation. To address these limitations, this paper proposes a robust cooperative platoon control as follows. First, the dynamics of the prediction uncertainty are formulated and analyzed. It is found that the prediction uncertainty increases significantly with the time step (e.g., quadratically for position uncertainty), which leads to the accumulation of the tracking error disturbance. Second, to mitigate and bound the accumulation, time-triggered feedback control is designed. It is proved that the accumulated disturbance is bounded if the new prediction uncertainty of each control interval is bounded, i.e., robust. Third, event-triggered feedforward control is designed to handle the disturbance outside the bound. The tight constraints are satisfied by the feedforward control. Based on tube methods, the overall method can guarantee the constraints, e.g., safety, stability, and string stability.

Robust cooperative platoon control in mixed traffic flow considering the uncertainty of human-driven vehicles.

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Robust cooperative platoon control in mixed traffic flow considering the uncertainty of human-driven vehicles.