Impact of Quantisation on Consistency of DMPC in Street Traffic with Dynamic Priority Rules

Abstract

AbstractDistributed systems typically require communication to steer the overall system to a reference or target state and keep it there. We consider an intersection scenario as a spacial set with vehicles as multi‐agents with individual initial and target conditions. Utilising a Distributed Model Predictive Control scheme (DMPC), in every time step each vehicle solves an finite horizon optimal control problem. The resulting optimal state trajectory is projected onto a quantisation of the spatial set and the quantised trajectories are broadcasted to the other vehicles to ensure collision avoidance. The quantisation is mainly motivated to reduce the necessary communication effort among the agents. Here, we introduce prediction coherence as difference of two predictions in two successive time steps. We numerically explore the idea to utilise prediction coherence to derive a lower bound for the communication requirements among the vehicles. (© 2017 Wiley‐VCH Verlag GmbH & Co. KGaA, Weinheim)