Cloud-based DMPC structure for velocity consensus and lane following of heterogeneous automated vehicles

This paper investigates the challenges associated with the velocity consensus and the lane following in heterogeneous automated vehicles considered as agents of a multi agent system. A cloud-based distributed model predictive control structure is proposed to address the distributed intrinsic of such systems. Moreover, for situations that the cloud-based control signal is not reachable, backup controllers based on explicit distributed model predictive control are developed. The proposed cloud-based controller is implemented in the Amazon cloud servers to show the practicality of the proposed approach. Simulation results are presented to demonstrate the effectiveness of our methodology in terms of handling practical limitations of connected and automated vehicles.