| 引用本文: | 梁波,牛佳安,李硕,杨彦斌,肖靖航,张晓坚.考虑能见度影响的公路隧道照明动态优化与智能控制[J].控制理论与应用,2023,40(10):1783~1792.[点击复制] |
| LIANG Bo,NIU Jia-an,LI Shuo,YANG Yan-bin,XIAO Jing-hang,ZHANG Xiao-jian.Dynamic optimization and intelligent control of highway tunnel lighting considering visibility effects[J].Control Theory and Technology,2023,40(10):1783~1792.[点击复制] |
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| 考虑能见度影响的公路隧道照明动态优化与智能控制 |
| Dynamic optimization and intelligent control of highway tunnel lighting considering visibility effects |
| 摘要点击 596 全文点击 225 投稿时间：2022-01-14 修订日期：2023-09-24 |
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| DOI编号 10.7641/CTA.2022.20042 |
| 2023,40(10):1783-1792 |
| 中文关键词 照明优化控制 模糊径向基神经网络 能见度 照明环境改善 仿真模拟 |
| 英文关键词 lighting optimization control fuzzy radial basis function neural networks visibility lighting environment improvement simulation |
| 基金项目 国家自然科学基金项目(51308409), 上海市浦江人才计划项目(15PJC075) |
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| 中文摘要 |
| 为解决不同能见度影响下公路隧道实际路面亮度变化过大以及由此引起的行车安全与能源虚耗问题, 本文提出了一种能够改善公路隧道照明环境的动态优化与智能控制方法. 首先, 通过对不同时空条件下的公路隧道进行现场试验和数据分析, 得到了隧道内能见度的变化规律; 其次, 在公路隧道传统照明设计的基础上考虑能见度对照明环境的影响, 建立了基于隧道内能见度、交通量、车速、路面亮度和照明亮度的按需照明与动态优化模型;随后, 以不同地区公路隧道的实测数据为样本, 结合划分出的公路隧道典型照明场景和模糊径向基神经网络算法构建了公路隧道照明智能控制模型, 最后, 通过仿真实验验证了所构建模型的有效性, 其结果表明, 本文所提出的优化控制方法能够在保证隧道照明安全性的前提下兼顾节能性. |
| 英文摘要 |
| To solve the problem of excessive changes in the road surface luminance under different visibility levels and the consequent traffic safety and energy wastage, a dynamic optimization and intelligent control method that can improve the lighting environment of highway tunnel is proposed in this paper. Firstly, through field tests and data analysis of highway tunnels under different space-time conditions, the variation of visibility in tunnels is obtained. Secondly, the influence of visibility on the lighting environment is considered on the basis of the traditional lighting design of highway tunnels. And an on-demand lighting and dynamic optimization model based on visibility, traffic volume, vehicle speed, road surface luminance and lighting luminance is established. Subsequently, the measured data of highway tunnels in different regions are used as samples. Combining the typical lighting scenes of highway tunnels and fuzzy radial basis neural network algorithm to build a highway tunnel lighting intelligent control model. Finally, the effectiveness of the model is shown by simulation experiments. The results show that the proposed optimal control method can take into account energy saving on the premise of ensuring the safety of tunnel lighting. |
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