基于交通流宏微观模式的反馈控制方法研究

发布时间：2018-03-14 08:32

  本文选题：交通流　切入点：优化速度模型　出处：《广西大学》2017年硕士论文　论文类型：学位论文

【摘要】：随着国家经济的快速发展,城市化进程加快,城市道路车辆骤增而产生的拥堵问题,为此需要结合交通流理论研究提出合理的方案来解决道路交通的拥堵问题。对于道路交通,不仅需要提高道路的交通流量,同时需要能够有效提高道路交通的运行效能。现代社会已经朝智能化方向发展,智能交通系统(ITS)是道路交通智能化的重要组成部分。本文基于交通流宏微观模式,考虑智能交通系统的发展,探讨道路交通控制方法,为道路交通控制推广理论依据,本文的主要研究工作如下:(1)基于交通流优化速度模型和反馈控制理论,提出平均场的反馈控制和延时反馈控制模型。文中对这两个控制模型进行线性稳定性,得出稳定性条件其中对平均场反馈控制模型进行非线性分析,导出描述交通拥堵的扭结-反扭结密度波的mKdV方程,并求出该mKdV方程的解。当交通流处于不稳定状态时,交通流呈现时停时走交通,施加平均场反馈控制和延时反馈控制分别进行控制,通过采集每个时刻的每一辆车的速度和位置的变化趋势,以此判断抑制交通拥堵的控制效果,并且通过控制效果的对比确定平均场延迟反馈作用下的交通流控制模型对于抑制交通拥堵更为有效。(2)以宏观交通流Nagatani的格子流体力学(LH)模型为基础,考虑驾驶员反应的延迟效应,并以下游与当前车流量差作为反馈控制策略,构建格子流体力学(LH)反馈控制模型。通过对该模型的拉普拉斯变换得出交通流控制系统的传递函数,在传递函数的H∞范数小于1时,求解得到系统的稳定性条件。通过理论分析和数值模拟,验证了车辆驾驶员的延迟反应是引起交通系统不稳定的重要因素,在反馈控制作用下,交通系统从不稳定状态恢复稳定状态,拥堵的系统得到了有效抑制,实现了交通的控制。(3)基于优化速度的全速度模型,提出了有延迟效应的速度差模型。通过对该模型的全局稳定性和局部稳定性分析,并通过数值模拟,研究结果表明系统在有延迟效应的情况下将更容易有效抑制交通拥堵。(4)文中应用宏观连续性方程,同时以延迟效应的速度差模型作为基础,结合微宏观转换关系,导出具有各向异性的交通流流体力学模型。对该宏观交通流动力学方程进行线性稳定性分析和非线性分析,并导出交通密度波的KdV-burgers方程。在周期边界条件下。应用该宏观流体力学模型来进行数值模拟,交通流在高低密度稳定性增强,不稳定区域缩小。最后,本文对智能交通系统的控制理论研究进行总结和展望。
[Abstract]:With the rapid development of the national economy and the acceleration of the urbanization process, the congestion problem caused by the sudden increase of urban road vehicles should be combined with the study of traffic flow theory to put forward a reasonable plan to solve the congestion problem of road traffic. It is necessary not only to increase the traffic flow on the road, but also to be able to effectively improve the operational efficiency of the road traffic. Intelligent Transportation system (ITS) is an important part of road traffic intelligence. Based on traffic flow macro and micro mode and considering the development of intelligent traffic system, this paper discusses the method of road traffic control, which is the theoretical basis for the popularization of road traffic control. The main work of this paper is as follows: (1) based on the traffic flow optimization speed model and the feedback control theory, the feedback control model and the delay feedback control model of the mean field are proposed. The stability conditions are obtained, in which the nonlinear analysis of the mean field feedback control model is carried out, and the mKdV equation describing the kink and inverse kink density wave of traffic congestion is derived, and the solution of the mKdV equation is obtained. When the traffic flow is in an unstable state, When the traffic flow is stopped, the traffic is controlled by the mean field feedback control and the delay feedback control respectively. By collecting the changing trend of the speed and position of each vehicle at each moment, the control effect of restraining the traffic congestion is judged. And through the comparison of control effects, it is determined that the traffic flow control model under the action of average field delay feedback is more effective in reducing traffic congestion. It is based on the lattice fluid dynamics (LH) model of macroscopic traffic flow Nagatani. Considering the delay effect of driver response and taking downstream and current traffic flow difference as feedback control strategy, the LH) feedback control model of lattice fluid dynamics is constructed. The transfer function of traffic flow control system is obtained by Laplace transformation of the model. When the H 鈭，

本文编号：1610418