本文關(guān)鍵詞：基于智能控制的隧道照明系統(tǒng)優(yōu)化研究　出處：《中國地質(zhì)大學(xué)（北京）》2014年碩士論文　論文類型：學(xué)位論文

  更多相關(guān)文章： 隧道照明 模糊控制 神經(jīng)網(wǎng)絡(luò)

【摘要】：在“7918”國家高速公路網(wǎng)規(guī)劃和鼓勵、支持中西部發(fā)展的政策機(jī)遇下，公路建設(shè)進(jìn)入了一個高速發(fā)展的時期，公路隧道數(shù)量也保持著高速增加的趨勢姿態(tài)。公路隧道特別是高速公路隧道有著獨特的設(shè)計要求，與城市隧道等其他隧道相比，設(shè)計更復(fù)雜，建設(shè)注意點更多。通過公路隧道的車輛車速較快，視野內(nèi)光亮度變化強(qiáng)烈，且車輛密度大，加之隧道內(nèi)車輛排放尾氣等問題影響，嚴(yán)重威脅著行車安全。 提高隧道內(nèi)照明亮度可以緩解車輛駕駛者進(jìn)入隧道由于隧道內(nèi)外亮度差造成的不適，而一味追求照明輝度，又會增加隧道運(yùn)行成本，造成電能源的極大消耗。如何最大限度滿足駕駛者的視覺反射弧，平衡隧道內(nèi)照明亮度和公路隧道運(yùn)行成本之間的關(guān)系，成為隧道照明控制研究的關(guān)鍵點。 在論文的開始部分比較了國內(nèi)、國外隧道照明控制研究現(xiàn)狀，并分析該控制系統(tǒng)的各類控制方法，比較控制的優(yōu)劣，總結(jié)出隧道照明控制將走向智能控制的大發(fā)展趨勢。其次，研究了影響隧道照明的主要因素，并決定以隧道外亮度、通過隧道車輛行駛速度以及車流量三個參數(shù)來調(diào)節(jié)隧道照明亮度。接著，探討智能控制領(lǐng)域應(yīng)用廣泛的模糊控制和神經(jīng)網(wǎng)絡(luò)在隧道照明系統(tǒng)中的應(yīng)用問題，智能調(diào)光控制不僅僅滿足隧道照明需求，節(jié)約電能源，而且能夠創(chuàng)造一個舒適的通過環(huán)境，大大提高通過車輛的安全系數(shù)。 分析比較模糊控制中各種控制算法，，以及適合應(yīng)用于自動控制領(lǐng)域的各類神經(jīng)網(wǎng)絡(luò)推理算法，使用T-S模糊控制和RBF神經(jīng)網(wǎng)絡(luò)共同控制隧道照明即FNN系統(tǒng)，并采用其結(jié)構(gòu)等價型的結(jié)合方式。 利用仿真軟件，根據(jù)《公路隧道通風(fēng)照明設(shè)計規(guī)范》中的要求，最后選擇隧道外亮度、車流量和車速與隧道照明的關(guān)系，進(jìn)行仿真控制。 最后，做全文工作總結(jié)：首先，提出本論文研究內(nèi)容的不足；其后，闡述后期展望。 本論文采用模糊邏輯調(diào)光法，提取少量特征點，通過RBF網(wǎng)絡(luò)訓(xùn)練，獲得泛化能力和逼近程度較優(yōu)的隧道照明控制系統(tǒng)。
[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.
【學(xué)位授予單位】：中國地質(zhì)大學(xué)（北京）
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：U453.7

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