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首页> 外文期刊>IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences >Model Predictive Control of Traffic Flow Based on Hybrid System Modeling
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Model Predictive Control of Traffic Flow Based on Hybrid System Modeling

机译:基于混合系统建模的交通流模型预测控制

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This paper presents a new framework for traffic flow control based on an integrated model description by means of Hybrid Dynamical System (HDS). The geometrical information on the traffic network is characterized by Hybrid Petri Net (HPN). Then, the algebraic behavior of traffic flow is transformed into Mixed Logical Dynamical Systems (MLDS) form in order to introduce an optimization technique. These expressions involve both continuous evolution of traffic flow and event driven behavior of traffic signal. HPN allows us to easily formulate the problem for complicated and large-scale traffic network due to its graphical understanding. MLDS enables us to optimize the control policy for traffic signal by means of its algebraic manipulability and use of model predictive control framework. Since the behavior represented by HPN can be directly transformed into corresponding MLDS form, the seamless incorporation of two different modeling schemes provide a systematic design scenario for traffic flow control.
机译:本文提出了一种基于混合动力系统(HDS)的集成模型描述的交通流控制新框架。交通网络上的几何信息以混合Petri网(HPN)为特征。然后,将交通流的代数行为转换为混合逻辑动力系统(MLDS)形式,以引入一种优化技术。这些表达既涉及交通流的不断发展,又涉及交通信号的事件驱动行为。 HPN凭借其图形化的理解,使我们能够轻松地为复杂的大规模交通网络提出问题。 MLDS使我们能够通过其代数可操作性和模型预测控制框架的使用来优化交通信号的控制策略。由于HPN表示的行为可以直接转换为相应的MLDS形式,因此两种不同建模方案的无缝结合为交通流控制提供了系统的设计方案。

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