多交叉口的无模型自适应控制
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摘要
论文研究了多交叉口的信号配时问题。首先综述了多交叉口信号配时的传统理论和方法,然后分析其存在的问题。指出了定时控制由于信号周期时长和绿信比均为固定值,不能很好的处理交通流的变化；感应控制主要是根据本交叉口车辆到达情况来判断绿灯时间是否做出调整,这虽然能够比较充分的利用车辆到达的信息,但未考虑多交叉口的整体控制效果；目前对于多交叉口的信号配时存在的难点主要有以下两个：一是固定配时方案不能够适应交通流的变化情况,二是对多交叉口的建模较为困难。
本文在处理多交叉口信号配时问题时的主要工作如下：
第一,论文结合排队长度均衡的思想将无模型自适应控制(Model-Free Adaptive Control)算法应用到多交叉口的信号控制中。该信号控制算法能够动态调整绿灯时间以适应交通流变化,并且其控制律设计仅需被控系统的I/O数据,不依赖于受控对象的数学模型,能有效处理上述难点。在多交叉口的具体处理上,采用的是分散估计分散控制型MFAC算法,通过将交叉口之间的影响项看作是对单个交叉口的可测干扰,将对复杂多交叉口的控制转化为独立单交叉口的控制,较大程度的简化了问题复杂度。
第二,利用微观交通仿真软件Paramics,采用动态链接库技术实现对多交叉口配时系统的控制算法接入以及仿真,并在干道型和网格型多交叉口路网结构中将MFAC控制与绿波控制以及定时控制的仿真结果进行对比分析。
This paper solves the signal timing problem of multiple intersections. First of all, it illuminates traditional timing theories and methods of multiple intersections, and then analyses their existing problems. Due to the fixed cycle time and the fixed split, the expected effect of fixed timing control couldn't be achieved when the traffic changed rapidly. Then actuated control method is raised in timing process with exposed disadvantages of fixed timing control. Although actuated control can use vehicle arriving information, the performance of the whole intersection is ignored in the timing process. There are mainly two issues existing in signal timing for multiple intersections:fixed timing plan can't adjust with the changing traffic flow, the other is modeling difficulty for multiple intersections. The main works are as follows:
Firstly, Model-Free Adaptive Control method and the idea of equilibrium queue length are applied in the signal timing solution of multiple intersections. MFAC algorithm can adjust green time with traffic fluctuation, and the mathematical model is not necessary for the controller design. The actual method used in signal timing for multiple intersections is decentralized estimating control method based on MFAC, which considers the influence between intersections to be a measurable disturbance. It can largely simplify signal timing problem of multiple intersections through making complicated multiple interactions issue transform to the controller design of the single interaction separately.
Secondly, we use the microcosmic traffic simulation software Paramics to show different effects of the proposed algorithm. The contrast of simulation results will be given about fixed timing method, green wave control and decentralized estimating control algorithm based on MFAC. Additionally, dynamic link library technology in visual C++is essential for the algorithms accessed to Paramics environment.
本文在处理多交叉口信号配时问题时的主要工作如下：
第一,论文结合排队长度均衡的思想将无模型自适应控制(Model-Free Adaptive Control)算法应用到多交叉口的信号控制中。该信号控制算法能够动态调整绿灯时间以适应交通流变化,并且其控制律设计仅需被控系统的I/O数据,不依赖于受控对象的数学模型,能有效处理上述难点。在多交叉口的具体处理上,采用的是分散估计分散控制型MFAC算法,通过将交叉口之间的影响项看作是对单个交叉口的可测干扰,将对复杂多交叉口的控制转化为独立单交叉口的控制,较大程度的简化了问题复杂度。
第二,利用微观交通仿真软件Paramics,采用动态链接库技术实现对多交叉口配时系统的控制算法接入以及仿真,并在干道型和网格型多交叉口路网结构中将MFAC控制与绿波控制以及定时控制的仿真结果进行对比分析。
This paper solves the signal timing problem of multiple intersections. First of all, it illuminates traditional timing theories and methods of multiple intersections, and then analyses their existing problems. Due to the fixed cycle time and the fixed split, the expected effect of fixed timing control couldn't be achieved when the traffic changed rapidly. Then actuated control method is raised in timing process with exposed disadvantages of fixed timing control. Although actuated control can use vehicle arriving information, the performance of the whole intersection is ignored in the timing process. There are mainly two issues existing in signal timing for multiple intersections:fixed timing plan can't adjust with the changing traffic flow, the other is modeling difficulty for multiple intersections. The main works are as follows:
Firstly, Model-Free Adaptive Control method and the idea of equilibrium queue length are applied in the signal timing solution of multiple intersections. MFAC algorithm can adjust green time with traffic fluctuation, and the mathematical model is not necessary for the controller design. The actual method used in signal timing for multiple intersections is decentralized estimating control method based on MFAC, which considers the influence between intersections to be a measurable disturbance. It can largely simplify signal timing problem of multiple intersections through making complicated multiple interactions issue transform to the controller design of the single interaction separately.
Secondly, we use the microcosmic traffic simulation software Paramics to show different effects of the proposed algorithm. The contrast of simulation results will be given about fixed timing method, green wave control and decentralized estimating control algorithm based on MFAC. Additionally, dynamic link library technology in visual C++is essential for the algorithms accessed to Paramics environment.
引文
[1]翟润平,周彤梅.道路交通控制原理及应用[M].北京：中国人民公安大学出版社.2002.13-45
[2]李立源,曹大铸.道路交通流最优预测与交叉口最优控制[J].控制理论与应用.1993.10(1).67-72
[3]Kotsialos A. Coordinated and Integrated Control of Motorway Networks via Non-linear Optimal Control[J]. Transportation research Part C.2002.10(3).65-84
[4]李江,傅晓光,李作敏.现代道路交通管理[M].北京：人民交通出版社.2000.191-194
[5]Kim J. A Fuzzy Logic Control Simulator for Adaptive Traffic Management. Proceedings of the Sixth IEEE International Conference on Fuzzy Systems. Barcelona. Spain.1997
[6]刘智勇.智能交通控制理论及应用[M].北京：科学出版社.2003.10-35
[7]田海,薛艳春,赵宇明,等.基于模糊控制的十字路口交通灯控制系统[J].自动化与信息工程.2008.29(4).8-10
[8]曾静,薛定宇,袁德成.非线性系统的多模型预测控制方法[J].东北大学学报(自然科学版).2009.30(1).26-29
[9]Roozemond D A. Stability of Intelligent Agent Systems in Urban Traffic Control[J]. Application of Artificial Intelligence in Engineering.1998.26 (1).15-18
[10]陈波.神经网络PID控制及其仿真[J].电脑知识与技术.2008.4(34).1691-1692
[11]林妹,贾磊,孔庆杰.主干线协调控制的改进算法研究[J].控制工程.2005.12(5).432-435
[12]周力,陈跃东,江明.基于PC-PLC网络的智能交通线控系统研究[J].自动化与仪器仪表.2007.15(2).58-61
[13]Xi Y G, Wang F, Wu G H. Nonlinear Multi-model Predictive Control. Proceedings of 13th World Congress. San Francisco. CA. USA.1997
[14]May A D. Traffic Flow Theory-the Traffic Engineer's Challenge. Institute of Transportation Engineers Annual meeting.1965.290-303
[15]Park B B, Rouphail N M, Sacks J. Assessment of Stochastic Signal Optimization Method Using Microsimulation[J]. Transportation Research Record:Journal of the Transportation Research Board.2007.1748 (2001).40-45
[16]张昌禄,翟润平.交通干线信号协调控制方法综述[J].中国人民公安大学学报.2007.13(1).87-90
[17]Sandell N J, Varaiya P, Athans M. Survey of Decentralized Control Methods for Large Scale Systems[J]. IEEE Transactions on Automatic Control.1978.23 (2).108-128
[18]杨治平.自校正与自适应组合结构的动态跟踪控制[J].控制工程.2003.10(5).456-459
[19]席裕庚,李德伟.预测控制定性综合理论的基本思路和研究现状[J].自动化学报.2008.34(10).1225-1234
[20]侯忠生.非线性系统参数辩识、自适应控制及无模型学习自适应控制[D].沈阳.东北大学.1994.46-53
[21]侯忠生.无模型自适应控制的现状与展望[J].控制理论与应用.2006.23(4).586-592
[22]金尚泰.无模型学习自适应控制的若干问题研究及其应用[D].北京.北京交通大学.2009. 15-19
[23]侯忠生,朱远明,李永强.复杂系统的数据驱动MFAC和基于MFAC的模块化设计.中国自动化大会暨两化融合高峰会议.杭州.中国自动化学会.2009.815-819
[24]Ioannou P. Decentralized Adaptive Control of Interconnected Systems[J]. IEEE Transactions on Automatic Control.1986.31 (4).291-298
[25]Chandra R S, Langbort C, Andrea R D. Distributed Control Design with Robustness to Small Time Delays [J]. System & Control Letters.2009.58 (4).296-303
[26]Tan K K, Lee T H, Huang S N. Adaptive Predictive Control of a Class of SISO Nonlinear System[J]. Dynamic and Control.2001.11 (2).151-174
[27]侯忠生.非参数模型及其自适应控制理论[M].北京：科学出版社.1999.102-152
[28]Milutinovic D, Lima P. Modelling and Optimal Centralized Control of a Large-Size Robotic Population[J]. IEEE Transaction on Robotics.2006.22 (6).1280-1285
[29]Labinaz G, Bayoumi M M, Rudie K. Viable Cascade Control and Application to a Batch Polymerization Process[J]. IEEE Transactions on Control Systems Technology.2000.8 (3). 396-407
[30]赵欢.单信号交叉口绿灯时间无模型自适应控制[D].北京.北京交通大学.2009.20-22
[31]王伟智,刘秉瀚.车辆排队长度自动检测新方法[J].中国体视学与图像分析.2006.11(3).216-221
[32]贺晓锋,杨玉珍,陈阳舟.基于视频图像处理的车辆排队长度检测[J].交通与计算机.2006.24(5).43-46
[33]邢丽,王晓原,吴芳.微观交通仿真模型的验证方法[J].计算机工程与应用.2010.46(9).204-207
[34]Benekohal R F. Procedure for Validation of Microscopic Traffic Flow Simulation Model[J]. Transportation Research Part F.1990.6 (3).190-202
[35]魏丽,孙俊,商蕾.微观交通仿真模型建模及应用[J].武汉理工大学学报(交通科学与工程版).2010.34(4).793-796
[36]商蕾,高孝洪,蒋汉平.城市微观交通模型建模及仿真实现[J].武汉理工大学学报(交通科学与工程版).2003.27(4).499-502
[37]陈德望,蔡伯根,吴建平.综合集成TSIS、VC和Matlab构建交通控制仿真平台[J].计算机仿真.2004.21(8).191-193
[38]Bloomberg L, Dale J. A Comparison of the VISSIM and CORSIM Traffic Simulation Models[C]. Institute of Transportation Engineers Annual Meeting.2000.3-16
[2]李立源,曹大铸.道路交通流最优预测与交叉口最优控制[J].控制理论与应用.1993.10(1).67-72
[3]Kotsialos A. Coordinated and Integrated Control of Motorway Networks via Non-linear Optimal Control[J]. Transportation research Part C.2002.10(3).65-84
[4]李江,傅晓光,李作敏.现代道路交通管理[M].北京：人民交通出版社.2000.191-194
[5]Kim J. A Fuzzy Logic Control Simulator for Adaptive Traffic Management. Proceedings of the Sixth IEEE International Conference on Fuzzy Systems. Barcelona. Spain.1997
[6]刘智勇.智能交通控制理论及应用[M].北京：科学出版社.2003.10-35
[7]田海,薛艳春,赵宇明,等.基于模糊控制的十字路口交通灯控制系统[J].自动化与信息工程.2008.29(4).8-10
[8]曾静,薛定宇,袁德成.非线性系统的多模型预测控制方法[J].东北大学学报(自然科学版).2009.30(1).26-29
[9]Roozemond D A. Stability of Intelligent Agent Systems in Urban Traffic Control[J]. Application of Artificial Intelligence in Engineering.1998.26 (1).15-18
[10]陈波.神经网络PID控制及其仿真[J].电脑知识与技术.2008.4(34).1691-1692
[11]林妹,贾磊,孔庆杰.主干线协调控制的改进算法研究[J].控制工程.2005.12(5).432-435
[12]周力,陈跃东,江明.基于PC-PLC网络的智能交通线控系统研究[J].自动化与仪器仪表.2007.15(2).58-61
[13]Xi Y G, Wang F, Wu G H. Nonlinear Multi-model Predictive Control. Proceedings of 13th World Congress. San Francisco. CA. USA.1997
[14]May A D. Traffic Flow Theory-the Traffic Engineer's Challenge. Institute of Transportation Engineers Annual meeting.1965.290-303
[15]Park B B, Rouphail N M, Sacks J. Assessment of Stochastic Signal Optimization Method Using Microsimulation[J]. Transportation Research Record:Journal of the Transportation Research Board.2007.1748 (2001).40-45
[16]张昌禄,翟润平.交通干线信号协调控制方法综述[J].中国人民公安大学学报.2007.13(1).87-90
[17]Sandell N J, Varaiya P, Athans M. Survey of Decentralized Control Methods for Large Scale Systems[J]. IEEE Transactions on Automatic Control.1978.23 (2).108-128
[18]杨治平.自校正与自适应组合结构的动态跟踪控制[J].控制工程.2003.10(5).456-459
[19]席裕庚,李德伟.预测控制定性综合理论的基本思路和研究现状[J].自动化学报.2008.34(10).1225-1234
[20]侯忠生.非线性系统参数辩识、自适应控制及无模型学习自适应控制[D].沈阳.东北大学.1994.46-53
[21]侯忠生.无模型自适应控制的现状与展望[J].控制理论与应用.2006.23(4).586-592
[22]金尚泰.无模型学习自适应控制的若干问题研究及其应用[D].北京.北京交通大学.2009. 15-19
[23]侯忠生,朱远明,李永强.复杂系统的数据驱动MFAC和基于MFAC的模块化设计.中国自动化大会暨两化融合高峰会议.杭州.中国自动化学会.2009.815-819
[24]Ioannou P. Decentralized Adaptive Control of Interconnected Systems[J]. IEEE Transactions on Automatic Control.1986.31 (4).291-298
[25]Chandra R S, Langbort C, Andrea R D. Distributed Control Design with Robustness to Small Time Delays [J]. System & Control Letters.2009.58 (4).296-303
[26]Tan K K, Lee T H, Huang S N. Adaptive Predictive Control of a Class of SISO Nonlinear System[J]. Dynamic and Control.2001.11 (2).151-174
[27]侯忠生.非参数模型及其自适应控制理论[M].北京：科学出版社.1999.102-152
[28]Milutinovic D, Lima P. Modelling and Optimal Centralized Control of a Large-Size Robotic Population[J]. IEEE Transaction on Robotics.2006.22 (6).1280-1285
[29]Labinaz G, Bayoumi M M, Rudie K. Viable Cascade Control and Application to a Batch Polymerization Process[J]. IEEE Transactions on Control Systems Technology.2000.8 (3). 396-407
[30]赵欢.单信号交叉口绿灯时间无模型自适应控制[D].北京.北京交通大学.2009.20-22
[31]王伟智,刘秉瀚.车辆排队长度自动检测新方法[J].中国体视学与图像分析.2006.11(3).216-221
[32]贺晓锋,杨玉珍,陈阳舟.基于视频图像处理的车辆排队长度检测[J].交通与计算机.2006.24(5).43-46
[33]邢丽,王晓原,吴芳.微观交通仿真模型的验证方法[J].计算机工程与应用.2010.46(9).204-207
[34]Benekohal R F. Procedure for Validation of Microscopic Traffic Flow Simulation Model[J]. Transportation Research Part F.1990.6 (3).190-202
[35]魏丽,孙俊,商蕾.微观交通仿真模型建模及应用[J].武汉理工大学学报(交通科学与工程版).2010.34(4).793-796
[36]商蕾,高孝洪,蒋汉平.城市微观交通模型建模及仿真实现[J].武汉理工大学学报(交通科学与工程版).2003.27(4).499-502
[37]陈德望,蔡伯根,吴建平.综合集成TSIS、VC和Matlab构建交通控制仿真平台[J].计算机仿真.2004.21(8).191-193
[38]Bloomberg L, Dale J. A Comparison of the VISSIM and CORSIM Traffic Simulation Models[C]. Institute of Transportation Engineers Annual Meeting.2000.3-16
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