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Distributed Model Predictive Control for Cooperative and Flexible Vehicle Platooning

机译:分布式模型预测控制,适用于合作与柔性车辆排

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This paper investigates the cooperative and flexible vehicle platooning problem in automated highway systems. We formulate the platoon into a dynamically decoupled system and address the flexible platooning problem using distributed model predictive control (DMPC) techniques. A two-step non-iterative DMPC strategy is proposed that sequentially solves local components of a constrained optimal control problem over two batches of vehicle clusters based on intervehicle communication. By introducing the clustering procedure, the proposed DMPC scheme only requires compatibility constraints of common neighbors of two adjacent vehicle clusters. With the two-step DMPC scheme, stability of the overall closed-loop system is guaranteed by a series of linear matrix inequalities that can be efficiently solved in practical applications. Collision-free properties are addressed using coupled state constraints together with terminal sets during a cooperation procedure. Simulations of flexible platooning are conducted for both joining and leaving events to show the effectiveness of the two-step DMPC scheme.
机译:本文研究了自动化公路系统中的合作和灵活的车辆排问题。我们将排载成动态解耦系统,并使用分布式模型预测控制(DMPC)技术来解决灵活的排态问题。提出了一种两步的非迭代DMPC策略,其在基于Intervehicle通信中顺序地解决了两批在两批车辆集群上的受限最佳控制问题的局部分量。通过引入聚类程序,所提出的DMPC方案仅需要两个相邻车辆集群的共同邻居的兼容性约束。通过两步DMPC方案,通过在实际应用中可以有效地解决的一系列线性矩阵不等式,保证了总闭环系统的稳定性。在协作过程中,使用耦合状态约束与终端集合一起寻址无碰撞属性。对加入和离开事件进行灵活排列的模拟,以显示两步DMPC方案的有效性。

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