Distributed model predictive control of multi-vehicle systems with switching communication topologies

• Published in: Transportation research. Part C, Emerging technologies Vol. 118; p. 102717
• Main Authors: Li, Keqiang, Bian, Yougang, Li, Shengbo Eben, Xu, Biao, Wang, Jianqiang
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.09.2020
• Subjects: Connected vehicles; Distributed control; Model predictive control; Switching communication topology; Distributed control; Model predictive control; Connected vehicles; Switching communication topology
• ISSN: 0968-090X; 1879-2359
• DOI: 10.1016/j.trc.2020.102717

•A DMPC platoon controller is proposed to address switching communication topology.•The convergence of predicted terminal states is strictly proved.•A sufficient asymptotic stability condition on weight matrices is derived. Vehicle-to-vehicle (V2V) communication-enabled cooperation of multiple connected vehicles improves the safety and efficiency of our transportation systems. However, the joining and leaving of vehicles and unreliability of wireless communication channels will cause the switching of communication topology among vehicles, thus affecting the performance of multi-vehicle systems. To address this issue, a distributed model predictive control (DMPC) method is proposed for multi-vehicle system control under switching communication topologies. First, an open-loop optimization problem is formulated, within which neighbor-deviation and self-deviation penalties and constraints are incorporated to ensure stability. Then, a DMPC algorithm is designed for multi-vehicle systems subject to switching communication topologies. For the closed-loop system, the convergence of predicted terminal states is proved based on the neighbor-deviation constraint. After that, closed-loop system stability is analysed based on a common Lyapunov function (CLF) defined using a joint neighbor set. It is proved that asymptotic stability of the closed-loop system can be achieved through a sufficient condition on the weight matrices of the open-loop optimization problem. Numerical simulations are conducted to demonstrate the effectiveness of the proposed DMPC controller.