文章基本信息

标题：Linear Data-Model Based Adaptive ILC for Freeway Ramp Metering without Identical Conditions on Initial States and Reference Trajectory
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作者：Hao Liang ; Na Lin ; Ronghu Chi 等
期刊名称：Public Policy And Administration
印刷版ISSN：2029-2872
出版年度：2019
卷号：48
期号：2
页码：268-277
DOI：10.5755/j01.itc.48.2.20406
出版社：Kaunas University of Technology
摘要：Although freeway traffic system is conducted with a repeatable pattern day-to-day, the initial volume/or speed and the desired density of the traffic flow may vary with days due to the external disturbances. In this paper, a new linear data-model based adaptive ILC (LDM-AILC) is proposed to address ramp metering in a macroscopic level freeway environment. A linear data-model is developed for the nonlinear macroscopic traffic flow model by introducing an equivalent dynamical linearization approach in the time domain. Then the LDM-AILC is designed with a feedback control law and a parameter updating law. The proposed scheme is data-driven intrinsically, where only the I/O data is required for the controller design and analysis. The convergence is shown by rigorous analysis without any identical conditions exposed on both the initial state and the reference trajectory. Extensive simulation results are provided to verify the effectiveness of the proposed LDM-AILC.
关键词：Adaptive ILC; Linear data-model; Traffic control; Ramp metering; Random initial conditions; Iteration-varying target trajectory.