Distributed Model Predictive Longitudinal Control for a Connected Autonomous Vehicle Platoon with Dynamic Information Flow Topology

Abstract

The development of vehicle-to-vehicle (V2V) improves the cooperation efficiency of the connected autonomous vehicle (CAV) platoon. However, the failure of the network communication occurs occasionally in the realistic environment, where the ideal fixed information flow topology (FIFT) cannot be adapted. To address this issue, this paper proposes a dynamic information flow topology (DIFT) utilizing a distributed model predictive control (DMPC) algorithm for CAV platoons. When the communication link is broken, the platoon control system will switch to the corresponding collaborative control mode instead of the degeneration to adaptive cruise control (ACC). First, the duty-vehicle dynamic model is constructed. In addition, the constraints with vehicle physical limitations and rear-end collision are considered. The acceleration information of the pedal actuator from the leading vehicle and the states of the predecessor including position, velocity and acceleration are transmitted to the following vehicle with a switch Indicator under DIFT. The cost function with the consideration of DIFT and fuel consumption is formulated for the optimization problem. Comparing with the FIFT, the proposed method is evaluated in the co-simulation of Matlab-TruckSim. The results demonstrate that the proposed DIFT strategy shows the satisfactory performance of the platoon under the communication issues by measuring inter-vehicle space, position and velocity tracking, and acceleration change with high tracking accuracy of position within 1.2 m and velocity within 0.04 m/s.