【作者】 徐丽君；

【导师】 徐进学； 王向东；

【作者基本信息】 沈阳工业大学 ， 控制理论与控制工程， 2007， 硕士

【摘要】 近年来，随着车辆数目的急剧增加，高速公路上交通堵塞的现象屡见不鲜，这使得如何采用合理的控制方法，预防或缓解高速公路上的拥挤状态显得尤为重要。入口匝道控制方法是控制高速公路交通状态最直接有效的方法之一。本文针对高速公路交通系统的特点，对入口匝道智能控制方法展开研究，主要的研究内容和结论如下：针对高速公路交通模型难确定的问题以及高速公路交通状态具有一定的可重复性的特点，本文采用迭代学习控制(ILC)方法对高速公路的交通密度进行控制。在一阶开环ILC匝道控制算法的基础之上，设计了一阶开闭环ILC匝道控制算法。严格的理论分析证明了此一阶开闭环ILC匝道控制算法的收敛性，仿真分析验证了此算法的有效性与优越性。此一阶开闭环ILC匝道控制算法结构简单，且不依赖于精确交通模型，能够有效避免由交通模型难确定而带来的控制结果难精确问题。与一阶开环ILC匝道控制方法相比，此一阶开闭环方法使系统具有更快的收敛速度以及更好的暂态性能。在一阶开闭环ILC匝道控制算法的基础上，设计了高阶开闭环ILC匝道控制算法，此算法不仅具有一阶开闭环ILC匝道控制算法的优点，而且因为其利用了更多的状态信息进行控制，系统的稳定性更好。严格的理论分析证明了此方法的收敛性，仿真分析验证了它的有效性以及优越性。与经典的ALINEA方法相比，此方法使系统拥有更精确的控制结果以及更好的稳定性。针对高速公路交通模型难确定，控制量难自适应交通变化以及难实现多路段协调控制三个问题，本文设计了一种基于模糊神经网络方法的匝道控制算法。此算法中，模糊规则既实现了相邻路段间的协调控制，又调整了高速公路干道交通状态与匝道排队长度的大小；神经网络的自学习功能使此方法对外界交通变化具有自适应能力。该算法能够在将高速干道交通密度维持在理想密度附近的同时，保持入口匝道排队长度尽可能的短。在抑制交通密度波动和排队长度增长方面比经典的反馈控制方法ALINEA取得了更好的效果。值得提出的是此方法具有一定的协调控制能力，比单匝道控制方法对道路容量的利用更加充分，这在仿真数据中也有明确体现。更多还原

【Abstract】 In rescent years the phenomena of traffic congestion on urban freeways is becoming more and more familiar due to the dramatic expansion of car-ownership, which makes it urgent and significant to apply efficient control approaches to keep the traffic condition in an ideal way. On-ramp metering, when properly applied, is considered as an efficient traffic management tool for freeways. A research on intelligent freeway on-ramp metering approaches was conducted according to some characteristics of the freeway traffic state. The main contents and results are as follows:According to the repeatability of the traffic pattern for freeway, iterative learning approach (ILC, in short) method was applied to regulate the congestion state. A one order open-closed loop ILC scheme was designed to control the traffic density. By means of rigorous theoretic analysis, its validity and superiority are adequately proved. Simulation results validate its efficiency. When compared to the conventional open loop ILC approach, the strategy proposed in this thesis has faster convergence speed and better transient performance than the open loop ILC law.Based on the one order open-closed ILC approach, a high order open-closed loop ILC scheme was further analyzed and designed. This approach does not only have the advantages as the first order one, but also makes the entire freeway system more stable as it collects more system information to design the metering rate. Simulation results confirmed its efficiency. When compared to the well-known ALINEA approach, this high order open-closed loop ILC approach has faster convergence speed and better robust performance.According to the problems that are encountered in current ramp control research: uncertainty of the traffic model、difficulty in designing an adaptive ramp controller and the necessary of the coordinated algorithms, the neuro-fuzzy network method was applied to resolve the problems above and an adaptive and coordinated on-ramp strategy was designed. This approach considers both the traffic density and the on-ramp queue length together and aims at controlling both of them simultaneously. When compared to the well-known ALINEA strategy, this approach has better performance in restraining traffic flow fluctuation and queue increase. It is worthy to point out that the approach is coordinated, which can utilize the road capacity better than the single on-ramp methods.更多还原