Distributed Adaptive Platooning Control of Connected Vehicles With Markov Switching Topologies

This paper addresses the challenge of distributed adaptive platooning control of connected vehicles with randomly switching topologies. A linearized longitudinal vehicle platoon dynamic model is investigated, which simultaneously considers the bounded external disturbances and leader control input. By representing the switching topologies model in terms of a continuous-time Markov process, a distributed adaptive control scheme is developed such that follower vehicles can synchronize their velocities and accelerations with the leader while preserving the intended distance. The outstanding feature of the proposed distributed adaptive control scheme is that it avoids acquiring global information associated with communication topology. Besides, the design procedure is scalable since the control gain can be derived by only solving a linear matrix inequality offline related to the vehicle dynamics parameters, which means that the method remains applicable when the platoon size changes. Finally, the efficacy of theoretical findings is substantiated through the utilization of simulations.