基于非线性模型预测控制的城市路网交通流优化  

Urban road network traffic flow optimization based on nonlinear model predictive control

作　　者：胡小勇 张建军[2] 杨云晖 邓志刚 HU Xiaoyong;ZHANG Jianjun;YANG Yunhui;DENG Zhigang(Xinyu Highway Development Center,Xinyu 338099,China;Key Laboratory of Integrated Transportation Big Data Application Technology,Beijing Jiaotong University,Beijing 100044,China;Xinyu Highway Survey and Design Institute,Xinyu 338099,China)

机构地区：[1]新余市公路事业发展中心,江西新余338099 [2]北京交通大学综合交通运输大数据应用技术交通运输行业重点实验室,北京100044 [3]新余公路勘察设计院,江西新余338099

出　　处：《现代电子技术》2023年第20期163-166,共4页Modern Electronics Technique

基　　金：江西省交通运输厅科技项目(2021H0023)。

摘　　要：为提高城市路网设计效率,文中提出一种基于非线性模型预测控制的城市路网交通流优化方案。采用预测控制方法,利用模拟试验验证所提算法在道路网络中的应用,从而最大限度地提高路口的通行能力;同时,验证迭代式识别在车辆流量模型的参数识别中的正确性。研究结果表明:采用时变参数模式预测时,每个路口的队长度差异都是比较合理的,模型预测结果与仿真结果接近;采用该模型的预测控制算法可以对网络的实时状况进行实时修正,从而改善道路的运行性能,8:00时段道路阻塞状况的模拟结果证明该方法具有较好的性能。相比之下,当系统中产生随机扰动时,自适应迭代学习辨识算法可以实现对系统期望输出的快速跟踪,具有较快的收敛速度和良好的效果。In order to improve the efficiency of urban road network design,an urban road network traffic flow optimization based on nonlinear model predictive control is proposed.By means of predictive control method,the simulation test is conducted to verify the application of the proposed algorithm in road network,so as to maximize the crossing capacity of intersections.The correctness of iterative identification in the parameter identification of vehicle flow model is verified.The research results show that when using time⁃varying parameter mode for the prediction,the difference in queue length at each intersection is relatively reasonable,and the model prediction results are close to the simulation results.The predictive control algorithm of this model can be used to correct the real⁃time condition of the network,so as to improve the running performance of the road.The simulation results of road congestion during 8:00 slot show that this method has good performance.In contrast,when random disturbance is generated in the system,the adaptive iterative learning identification algorithm can realize the fast tracking of the expected output of the system,and has a fast convergence speed and effect.

关 键 词：城市路网 交通流优化 车辆流量模型 非线性 交通信号预测控制 自适应迭代学习辨识算法 

分 类 号：TN711-34[电子电信—电路与系统] TP13[自动化与计算机技术—控制理论与控制工程]