基于智能控制的隧道照明系统优化研究

发布时间：2018-01-10 14:20

本文关键词：基于智能控制的隧道照明系统优化研究　出处：《中国地质大学（北京）》2014年硕士论文　论文类型：学位论文

【摘要】：在“7918”国家高速公路网规划和鼓励、支持中西部发展的政策机遇下，公路建设进入了一个高速发展的时期，公路隧道数量也保持着高速增加的趋势姿态。公路隧道特别是高速公路隧道有着独特的设计要求，与城市隧道等其他隧道相比，设计更复杂，建设注意点更多。通过公路隧道的车辆车速较快，视野内光亮度变化强烈，且车辆密度大，加之隧道内车辆排放尾气等问题影响，严重威胁着行车安全。提高隧道内照明亮度可以缓解车辆驾驶者进入隧道由于隧道内外亮度差造成的不适，而一味追求照明辉度，又会增加隧道运行成本，造成电能源的极大消耗。如何最大限度满足驾驶者的视觉反射弧，平衡隧道内照明亮度和公路隧道运行成本之间的关系，成为隧道照明控制研究的关键点。在论文的开始部分比较了国内、国外隧道照明控制研究现状，并分析该控制系统的各类控制方法，比较控制的优劣，总结出隧道照明控制将走向智能控制的大发展趋势。其次，研究了影响隧道照明的主要因素，并决定以隧道外亮度、通过隧道车辆行驶速度以及车流量三个参数来调节隧道照明亮度。接着，探讨智能控制领域应用广泛的模糊控制和神经网络在隧道照明系统中的应用问题，智能调光控制不仅仅满足隧道照明需求，节约电能源，而且能够创造一个舒适的通过环境，大大提高通过车辆的安全系数。分析比较模糊控制中各种控制算法，，以及适合应用于自动控制领域的各类神经网络推理算法，使用T-S模糊控制和RBF神经网络共同控制隧道照明即FNN系统，并采用其结构等价型的结合方式。利用仿真软件，根据《公路隧道通风照明设计规范》中的要求，最后选择隧道外亮度、车流量和车速与隧道照明的关系，进行仿真控制。最后，做全文工作总结：首先，提出本论文研究内容的不足；其后，阐述后期展望。本论文采用模糊逻辑调光法，提取少量特征点，通过RBF网络训练，获得泛化能力和逼近程度较优的隧道照明控制系统。
[Abstract]:In the "7918" national highway network planning and encouragement, supporting the development of the central and western policy opportunities, highway construction has entered a period of rapid development. Highway tunnels, especially highway tunnels, have unique design requirements, compared with other tunnels such as urban tunnels, the design is more complex. The speed of vehicles passing through the highway tunnel is faster, the light brightness in the field of vision changes strongly, and the vehicle density is large. In addition, the problem of vehicle exhaust emission in the tunnel seriously threatens the safety of driving. Improving the lighting brightness in the tunnel can alleviate the discomfort caused by the difference in brightness between the inside and outside of the tunnel, and the pursuit of illumination will increase the operating cost of the tunnel. How to meet the needs of the driver's visual reflex arc and balance the relationship between the lighting brightness and the operating cost of the highway tunnel are the key points in the study of tunnel lighting control. At the beginning of the paper, the domestic and foreign tunnel lighting control research status is compared, and the various control methods of the control system are analyzed, and the advantages and disadvantages of the control system are compared. Summed up the tunnel lighting control will be the trend of intelligent control. Secondly, the main factors affecting tunnel lighting are studied, and the outside brightness of tunnel is determined. The luminance of tunnel lighting is adjusted by three parameters: vehicle speed and traffic flow. Then, the application of fuzzy control and neural network in tunnel lighting system is discussed, which is widely used in intelligent control field. Intelligent dimming control can not only meet the tunnel lighting requirements, save electricity and energy, but also create a comfortable passing environment, and greatly improve the safety factor of passing vehicles. This paper analyzes and compares various control algorithms in fuzzy control, as well as various neural network reasoning algorithms suitable for application in the field of automatic control. The T-S fuzzy control and RBF neural network are used to control the tunnel illumination, that is, the FNN system, and its structurally equivalent combination is adopted. According to the requirements of the Design Code for ventilation and Lighting of Highway Tunnel, the simulation software is used to select the relationship between the luminance, the flow rate and the speed of the tunnel lighting, and to carry out the simulation control. At last, the thesis summarizes the work: first, it puts forward the deficiency of this thesis, and then, it expounds the future prospect. In this paper, the fuzzy logic dimming method is used to extract a small number of feature points, and through the training of RBF network, a tunnel lighting control system with better generalization ability and approximation degree is obtained.
【学位授予单位】：中国地质大学（北京）
【学位级别】：硕士
【学位授予年份】：2014
【分类号】：U453.7

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