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Model Free Adaptive Predictive Perimeter Control for an Urban Traffic Network

机译:城市交通网络的无模型自适应预测周界控制

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Most exiting macroscopic fundamental diagram (MFD) based perimeter control methods are regarded as model-based feedback control methods, whose performance is hard to improve in practice due to the fact that traffic flow model is complex and has uncertainties. In this paper, a model free adaptive predictive perimeter control strategy is proposed for an urban traffic network. The control performance is improved by virtue of the prediction data model derived by dynamic linearization technique. The effectiveness of the proposed perimeter control algorithm is verified by comparing with the traditional PID controller in the simulation section.
机译:大多数基于宏基本原理图(MFD)的外围控制方法都被视为基于模型的反馈控制方法,由于交通流模型复杂且具有不确定性,因此其性能在实践中难以提高。本文提出了一种针对城市交通网络的无模型自适应预测周界控制策略。通过动态线性化技术导出的预测数据模型可以提高控制性能。通过与仿真部分中的传统PID控制器进行比较,验证了所提出的周边控制算法的有效性。

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