城市隧道通风控制算法研究与实现

公路隧道通风系统主要用于降低隧道内污染物浓度.射流风机的控制方法是通风系统设计的核心.通过对不同通风控制方法的比较,根据城市隧道的特点,确定采用模糊控制对隧道进行通风控制.并依据CO浓度和烟雾浓度之间具有相互的独立性,提出了建立两个独立的模糊控制器,利用两个模糊控制器分别进行推理运算得到各自的输出.最后比较两个输出,采用最大运行台数控制原则得出射流风机的运行台数.     

模糊控制技术在隧道射流风机中的应用

不管是在铁路还是公路隧道中射流风机都属于隧道内的大功率运行设备,其能否合理、有效的运行直接影响着隧道内的环境状态和运营成本;文章以公路隧道为例,在分析现有隧道射流风机控制方法的基础上,提出运用模糊控制技术对射流风机进行控制的设计方案;首先用MATLAB模糊逻辑工具箱设计模糊控制器并创建了模糊控制规则表,然后对利用iFIX下的VBA开发环境在后台自动运行模糊控制程序完成仿真;根据CO的浓度变化以及在实验过程中风机利用的数量和时间证明该系统具有良好的控制性能,实时性强,隧道运行环境改善明显。     

基于PLC的模糊控制在隧道通风中的应用

本文根据公路隧道的特点，提出了一种基于DLC的模糊控制法并应用于隧道通风系统。这种新的控制方法将CO浓度和VI浓度以及车流量分别作为多个模糊控制器的输入量，输出的风机量由多个模糊控制的输出值取最大值。通过PLC离线计算和在线查表的软件推理方式来实现，有效地提高了通风系统控制的智能化。     

公路隧道射流风机通风效果优化研究

以公路隧道射流风机通风效果为研究对象,综合分析影响射流风机纵向通风效果的各项因素,利用计算流体动力学（CFD）理论,对射流风机的安装高度、风机轴线与隧道轴线之间的夹角进行优化仿真分析。以1120型风机为例通过隧道三维建模技术Solidworks进行效果优化仿真,得到最优安装高度及夹角,达到了公路隧道射流风机通风效果的优化。仿真结果表明射流风机的安装高度与射流角度对通风效果的影响趋势呈抛物线特征,存在一个最优的射流风机安装高度和风机轴线与隧道轴线的夹角,为公路隧道通风设计提供了科学依据。     

基于ZigBee的隧道通风自动控制系统

将ZigBee技术与PLC控制技术、变频技术相互融合,提出了基于ZigBee技术的隧道通风自动控制系统。该系统主要由隧道环境检测系统和隧道通风控制系统组成。环境检测系统以CC2430为核心,负责采集隧道内的环境数据,并将该数据通过无线传感网络传送给通风控制系统的控制器;通风控制系统以PLC为核心,可接收环境检测系统所采集的环境数据,并将其与预先设定值进行比较,同时输出信号来控制变频器的输出频率,从而控制风机转速,实现隧道自动通风的目的。测试结果表明:该系统整体工作稳定,操作界面友好,同时验证了无线传感网络数据传输的稳定性。     

迭福山隧道通风控制模式探讨

迭福山隧道是深圳市坪西一级公路的特殊路段，双洞四车道，左洞长1578．62米，右洞长1657.38米，各装有4台射流风机。通过数值仿真、实际测试和吸收二年多来隧道通风控制的经验，探讨了行之有效的隧道通风控制模式，对单向交通隧道纵向通风控制有一定的参考价值，取得了较好的经济效益和社会效益。     

模糊控制在城市公路隧道通风系统中的应用

介绍模糊控制系统的构成,以及模糊控制器的详细设计.将CO浓度值和能见度值(VI)结合考虑,作为模糊控制系统的输入参数,风机启动台数作为模糊控制系统的输出.运用模糊逻辑工具箱设计一个模糊控制器,并由此获得了风机台数输出控制曲面.     

基于神经网络的公路隧道通风控制

研究并设计了一种基于神经网络和专家系统的公路隧道通风智能控制系统。控制系统在原反馈信号——污染物浓度基础上,加入了车流量和车速前馈信号,并通过改进的BP神经网络预测污染物浓度,合理的完成了对通风设备的控制。结果表明,该系统优于传统的分级式反馈控制方法。     

基于和声搜索的环控通风调控策略优化

针对地铁车站环控通风系统风机定频运行,导致风机无法跟随站台PM2.5的浓度的变化而改变频率,造成能源浪费。根据环控通风系统的运行现状,可将其划分为只送不排模式、不送只排模式和即送又排模式,并考虑到环控通风系统应适应多扰动的影响,选取最小二乘法系统辨识一阶惯性时滞模型,用于描述变量动态变化响应传递函数,采用自抗扰控制器建立环控通风系统控制模型,利用和声搜索算法对环控通风模型PM2.5浓度设定值寻优,结果显示经过策略优化之后,只送不排模式、不送只排模式和即送又排模式站台PM2.5平均浓度分别降低了7%、8%和10%,风机能耗分别降低了19%、19%和30%。     

基于一种新模糊模型的非线性系统模糊辨识

提出一种基于新的模糊模型和加权递推最小二乘算法 (WRLSA)的非线性系统模糊辨识方法.新型的具有插值能力的模糊系统可以通过学习从输入输出采样数据中提取MISO系统模糊规则,它继承了Sugeno模型及其变化形式的许多优点.采用相应的模糊隶属函数,使得被辨识的模型可用若干局部线性模型来表示,然后利用WRLSA拟合这些线性模型.给出了详细的模糊辨识算法,为了验证该辨识方法的有效性,还给出了对熟知的Box-Jenkins数据的辨识结果.     

The control of highway tunnel ventilation using fuzzy logic

The purpose of tunnel ventilation control is to provide a safe and comfortable environment for users. The tunnel ventilation is optimized by controlling jet fans and dust collectors installed inside the tunnel. The jet fans blow polluted air from inside the tunnel toward air exit ports. The dust collectors remove soot and smoke so that pollutant concentration inside the tunnel can be better measured by CO (carbon monoxide) meters. Since this is a process involving many elements which are difficult to quantify exactly, the predictive fuzzy control is introduced to solve the problem. By means of this approach it was made possible to reduce electric power consumption greatly while keeping the degree of pollution within the allowable limit.     

Longitudinal tunnel ventilation control. Part 1: Modelling and dynamic feedforward control

Road tunnels exceeding a certain minimum length are equipped with a ventilation system. In case of a fire it is used to achieve a predefined air flow velocity in the tunnel by adequately controlling the installed jet fans in order to ensure sufficient visibility for persons to safely follow the escape routes. As the dynamics of the air flow in road tunnels strongly depend on the tunnel length, short tunnels with longitudinal ventilation systems pose a challenging control task. In this paper, non-linear dynamic feedforward control is proposed for longitudinal ventilation control in case of an emergency. For this purpose, an analytical non-linear zero-dimensional model of the air flow is feedback linearised. Due to its special properties, which are presented and analysed, two different versions of feedforward control are proposed: One is focused on performance, the other on robustness. Finally, the beneficial behaviour of the presented two-degrees-of-freedom control approach is demonstrated by its application to an Austrian motorway tunnel.     

基于模糊小波神经网络的交通标志识别方法研究

对交通标志进行实时、正确的识别,是车辆自动导航中一个重要方面.该文介绍了一种基于模糊小波神经网络的交通标志识别方法.该方法首先利用不变矩来提取图像特征,然后将特征向量输入模糊小波神经网络进行识别.该网络以小波函数作为模糊隶属函数,将模糊技术与神经网络相结合,利用神经网络实现模糊推理,并可对隶属函数的形状进行实时调整,从而使网络具有更强的学习和自适应能力.实验证明,该方法具有较高的识别精度和速度,在车辆自动导航中具有较高的应用价值.     

Adaptive fuzzy identification and predictive control for industrial processes

This paper proposes a method for adaptive identification and control for industrial applications. The learning of a T–S fuzzy model is performed from input/output data to approximate unknown nonlinear processes by a hierarchical genetic algorithm (HGA). The HGA approach is composed by five hierarchical levels where the following parameters of the T–S fuzzy system are learned: input variables and their respective time delays, antecedent fuzzy sets, consequent parameters, and fuzzy rules. In order to reduce the computational cost and increase the algorithm’s performance an initialization method is applied on HGA. To deal with nonlinear plants and time-varying processes, the T–S fuzzy model is adapted online to maintain the quality of the identification/control. The identification methodology is proposed for two application problems: (1) the design of data-driven soft sensors, and (2) the learning of a model for the Generalized predictive control (GPC) algorithm. The integration of the proposed adaptive identification method with the GPC results in an effective adaptive predictive fuzzy control methodology. To validate and demonstrate the performance and effectiveness of the proposed methodologies, they are applied on identification of a model for the estimation of the flour concentration in the effluent of a real-world wastewater treatment system; and on control of a simulated continuous stirred tank reactor (CSTR) and on a real experimental setup composed of two coupled DC motors. The results are presented, showing that the developed evolving T–S fuzzy model can identify the nonlinear systems satisfactorily and it can be used successfully as a prediction model of the process for the GPC controller.     

Modeling and control of carbon monoxide concentration using aneuro-fuzzy technique

Modeling and control of carbon monoxide (CO) concentration using a neuro-fuzzy technique are discussed. A self-organizing fuzzy identification algorithm (SOFIA) for identifying complex systems such as CO concentration is proposed. The main purpose of SOFIA is to reduce the computational requirement for identifying a fuzzy model. In particular, the authors simplify a procedure for finding the optimal structure of fuzzy partition. The δ rule, which is a basic learning method in neural networks, is used for parameter identification of a fuzzy model. SOFIA consists of four stages which effectively realize structure identification and parameter identification. The procedure of SOFIA is concretely demonstrated by a simple example which has been used in some modeling exercises. The identification result shows effectiveness of SOFIA. Next, the authors apply SOFIA to a prediction problem for CO concentration in the air at the busiest traffic intersection in a large city of Japan. Prediction results show that the fuzzy model is much better than a linear model. Furthermore, the authors simulate a control system for keeping CO concentration at a constant level by using the identified fuzzy model. A self-learning method for adaptively modifying controller parameters by δ rule is introduced because the dynamics of real CO concentration system changes gradually over a long period of time. Two self-learning controllers are designed in this simulation. One is a self-learning linear PI controller. The other is a self-learning fuzzy PI controller. The authors investigate robustness and adaptability of this control system for disturbance and parameter perturbation of the CO concentration model. Simulation results show that the self-learning fuzzy controller is more robust and adaptive     

隧道窑温度模糊控制算法的设计及应用

论文针对隧道窑燃烧过程中具有的非线性、大时滞、大惯性特点,基于专家知识,提出了利用自整定模糊PID控制、模糊辨识、Smith预估补偿控制以及Bang-Bang控制相结合的隧道窑温度控制算法。该算法在基于西门子S7-300 PLC控制系统的软硬件基础上实现并在山西某耐火材料企业实际投入使用,取得了显著的控制效果和良好的经济效益。     

一种简化的区间二型模糊系统辨识方法

KM降阶算法是目前区间二型模糊集合常用的降阶算法,针对其效率低、难以用于实时辨识与控制的缺点,提出了一种简化的区间二型模糊系统辨识方法。该方法采用二型T-S模糊模型,前件参数为区间二型模糊集合,后件参数为普通T-S模糊模型形式。二型T-S模糊模型的解模糊化采用简化的降阶算法,提高了模型的辨识效率,可用于实时辨识与控制。仿真实例表明,所提算法在不降低辨识精度的情况下能够有效提高辨识效率。