| 宏观交通流模型参数的迭代学习辨识方法 |
| |
| 引用本文： | 侯忠生,金尚泰,赵明.宏观交通流模型参数的迭代学习辨识方法[J].自动化学报,2008,34(1):64-71. |
| |
| 作者姓名： | 侯忠生 金尚泰 赵明 |
| |
| 作者单位： | 1.北京交通大学电子信息工程学院先进控制系统研究所 北京 100044 |
| |
| 基金项目： | 国家自然科学基金(60474038，60774022)，国家教育部博士点基金(20060004002)资助~~ |
| |
| 摘 要： | 利用宏观交通流行为的重复性特性, 将快速路宏观交通流模型转换为包含此模型的一般离散时间非线性系统模型, 然后针对此一般离散时间非线性系统模型设计了基于迭代学习的宏观交通流模型参数辨识算法. 严格的理论推导证明了这种参数辨识方案的收敛性和鲁棒性. 仿真结果验证了该算法的有效性.
|
| 关 键 词： | 宏观交通流模型 迭代学习控制 参数辨识 重复性 |
| 收稿时间： | 2006-08-21 |
| 修稿时间： | 2007-01-15 |
Iterative Learning Identification Method for the Macroscopic Traffic Flow Model |
| |
| HOU Zhong-Sheng,JIN Shang-Tai,ZHAO Ming.Iterative Learning Identification Method for the Macroscopic Traffic Flow Model[J].Acta Automatica Sinica,2008,34(1):64-71. |
| |
| Authors: | HOU Zhong-Sheng JIN Shang-Tai ZHAO Ming |
| |
| Affiliation: | 1.Advanced Control Systems Laboratory, School of Electronics and Information Engineering, Beijing Jiaotong University, Beijing 100044 |
| |
| Abstract: | By transforming the macroscopic traffic flow model into a more general discrete-time nonlinear system model,an iterative learning identification method is developed to estimate the parameters of the more general discrete- time nonlinear system,so the macroscopic traffic flow parameters as well,based on the repeatability of the macroscopic traffic flow behavior in a freeway.With rigorous analysis,it is shown that the proposed learning identification scheme can guarantee the convergence and robustness.A number of simulation results are provided to demonstrate the efficacy of the proposed approach. |
| |
| Keywords: | Macroscopic traffic flow model iterative learning parameter identification repeatability |
| 本文献已被 CNKI 维普 万方数据 等数据库收录！ |
| 点击此处可从《自动化学报》浏览原始摘要信息 |
|
点击此处可从《自动化学报》下载全文 |
