论文标题
分布式数据驱动的预测控制，用于合作平滑混合流量
Distributed data-driven predictive control for cooperatively smoothing mixed traffic flow
论文作者
论文摘要
连接和自动化车辆（CAVS）的合作控制承诺交通流畅。在混合流量中，具有未知动力学的人类驱动的车辆共存，数据驱动的预测控制技术可以通过可测量的流量数据进行CAV安全，最佳的控制。但是，在大多数现有策略中的集中控制设置限制了其对大规模混合交通流的可扩展性。为了解决这个问题，本文提出了一种合作的Deep-LCC（支持数据的预测领导巡航控制）及其分布式实现算法。在合作的深-LCC中，通过一个单个CAV自然地将交通系统自然地分配为多个子系统，该系统根据Willems的基本引理收集了子系统行为预测的局部轨迹数据。同时，交叉系统的相互作用是作为耦合约束的。然后，我们采用乘数（ADMM）的交替方向方法来设计分布式的深度LCC算法。该算法通过并行计算实现了计算和通信效率以及轨迹数据隐私。我们对不同交通尺度的模拟验证了分布式深-LCC的实时波动潜在潜力，这可以在只有5％-20％CAVS的100辆汽车的大规模交通系统中降低燃油消耗超过31.84％。
Cooperative control of connected and automated vehicles (CAVs) promises smoother traffic flow. In mixed traffic, where human-driven vehicles with unknown dynamics coexist, data-driven predictive control techniques allow for CAV safe and optimal control with measurable traffic data. However, the centralized control setting in most existing strategies limits their scalability for large-scale mixed traffic flow. To address this problem, this paper proposes a cooperative DeeP-LCC (Data-EnablEd Predictive Leading Cruise Control) formulation and its distributed implementation algorithm. In cooperative DeeP-LCC, the traffic system is naturally partitioned into multiple subsystems with one single CAV, which collects local trajectory data for subsystem behavior predictions based on the Willems' fundamental lemma. Meanwhile, the cross-subsystem interaction is formulated as a coupling constraint. Then, we employ the Alternating Direction Method of Multipliers (ADMM) to design the distributed DeeP-LCC algorithm. This algorithm achieves both computation and communication efficiency, as well as trajectory data privacy, through parallel calculation. Our simulations on different traffic scales verify the real-time wave-dampening potential of distributed DeeP-LCC, which can reduce fuel consumption by over 31.84% in a large-scale traffic system of 100 vehicles with only 5%-20% CAVs.
