移动闭塞条件下地铁列车的运行优化
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摘要
城市轨道交通(地铁)具有快速、便捷、大运量的特点,已经成为缓解城市交通矛盾的主要运输方式,而信号系统是确保地铁列车安全运行及提高运营效率的关键设备。随着计算机及通讯技术的发展,移动自动闭塞信号系统已经成为一种基于现代无线通信技术、计算机技术和控制技术的智能化的列车间隔控制系统,对移动自动闭塞系统的研究和开发代表了21世纪铁路信号发展的方向。近年来,中国铁路组织对移动自动闭塞系统的研究正在逐步深入,如何评估移动闭塞条件下城市轨道交通系统的线路通过能力,以及在此基础上如何优化行车组织,以便充分发挥移动闭塞区间通过能力高、调度指挥灵活大的优势是一个很有研究价值的课题。本文以城市轨道交通一地铁为研究背景,主要研究移动闭塞条件下列车追踪间隔时间的确立、单列列车的运行控制、车组间的控制,以及列车群在受到各种随机扰动而出现偏离计划运行线时,在考虑列车对乘客吸纳水平的基础上,如何对列车群进行运行调整。
     1.首先在大量阅读国内外有关文献资料的基础上,对移动自动闭塞信号系统的发展历程及国内外研究技术进行了综述,指出在移动自动闭塞信号系统下城市轨道交通主要面临如下技术问题:列车追踪间隔的确定、列车运行组织方式及主要技术参数的确定、系统的抗干扰能力、列车运行图自动调整理论与方法、综合调度管理系统。
     2.地铁列车运行控制系统的总体目标是保障系统运输能力。本文从线路通过能力和线路输送能力两方面对地铁的运输能力问题进行了讨论分析。指出线路通过能力是影响地铁运输能力的关键因素,而降低列车的追踪间隔是提高线路通过能力的主要方法。在较短的列车追踪间隔时间下,为确保列车的安全运行,必须要对列车进行运行控制,本文对地铁列车运行自动控制系统的组成和结构以及其三个子系统:列车自动监控系统、列车自动保护系统、列车自动运行系统的功能和分类进行了详细分析和比较,指出移动自动闭塞系统是列车自动控制系统的发展目标,对列车自动控制系统进行国产化研究开发很有必要。
     3.本文详细讨论了固定自动闭塞系统、准移动闭塞系统和移动自动闭塞系统的数字轨道电路的原理、列控方式等内容,在分析列车区间追踪间隔时间、车站追踪间隔时间的基本概念以及影响因素的基础上,建立了城市轨道交通在不同信号系统下的列车区间追踪间隔和列车车站追踪间隔模型。对三种闭塞信号系统下的最小列车追踪间隔时间进行了理论计算和分析,应用MATLAB进行了仿真。仿真结果表明在移动闭塞条件下,区间通过能力较固定闭塞条件、准移动闭塞条件下有很大地提高,但却不是固定的,也不与列车的运行速度成正比,而是在一定的速度上达到最大值。
     4.研究了移动闭塞条件下地铁列车的运行规律,建立了单列地铁列车的动力学模型,将基于事件的控制技术应用到地铁列车的控制中。通过引入运动参考变量,求出在以站间最小运行时间为目标的单列列车控制中,列车运行速度、加速度关于列车走行距离的表达式,从而可以根据列车走行距离实时调整规划列车的运行。基于事件的控制技术在机器人智能控制领域已经得到了广泛的应用,将这种技术用于地铁列车的运行控制中,是本文的一个创新。仿真结果亦表明,在采用基于事件的控制技术来对列车进行区间运行控制时,相对于其它两种常用的方法:PD控制和神经网络控制,无论在列车实际运行时间上,还是乘客乘坐的舒适性上都有很大程度的改进。
     5.城市轨道交通系统是一个大的混杂系统,具有“移动”性,每一列列车都是一个自治的智能体。智能体之间的协调、协作是评价智能系统性能的重要指标。本文研究了移动闭塞条件下地铁列车车组间的控制,针对在时间域求解过程中,当列车运行出现干扰时,列车间易出现串联影响的现象,采用基于事件的控制技术和编队思想对列车组进行控制。仿真结果表明,借助于移动自动闭塞信号系统的车—地之间的双向通讯方式、分布式控制结构和基于事件的控制思想,能够方便地实现系统的重新配置以及各子系统间的协调协作,同时还能够有效地解决时间域求解过程中出现的串行不稳定性,降低列车间运行间隔,在保证不撞车以及尽量减少站外停车的前提下,提高系统的运载能力。本论文的这一研究工作具有一定的原创性,并已得到专家的认可。
     6.正常运行情况下,列车严格按照列车计划运行图运行。但由于存在许多随机因素的干扰,列车运行又难免偏离计划运行图,尤其在以列车间隔时间短为显著特点的城市轨道交通系统上,一列列车的晚点往往会影响其它列车正常运行,有时甚至会影响整个城市轨道交通的有序运营。目前,现行的地铁列车晚点运行调整的性能指标多是尽量降低列车群的晚点时间,尚未涉及晚点列车对于车站“客流吸纳”水平的研究。而对于车站客流的吸纳情况,往往是评价轨道交通系统运营效率的一个重要的指标。本文基于移动自动闭塞信号系统下地铁列车的运行特点,首次研究了由于某些干扰造成某列列车出站晚点时,在考虑相邻列车对客流的吸纳水平均衡的前提下,以降低列车群晚点时间和提高对车站客流的吸纳水平为综合性能指标的晚点列车群追踪运行过程中的运行调整算法设计,并进行了仿真,具有一定的创新性。
     最后,总结了本论文的主要研究工作,并提出了进一步的研究方向。
Urban Mass Transit (Subway) has the advantages of flexibility, fastness, and high capacity. It has become the effective way to alleviate the modern heavy traffic. Signaling system is the key equipment to ensure its safety and high efficiency. Moving Block System (MBS) is a new type of intelligent train control system based on modern communication, computer technology and control techniques. The research and development of MBS represent the development trend of railway signaling in the 21st century. In recent years, the research of MBS has got in-depth development in Chinese railway. How to evaluate the carrying capacity under MBS and how to optimize train organization in order to make full use of the advantage of high block carrying capacity and the flexibility on schedule are an important field. We focus our research on the determination of the tracking interval time of the train, the running control of a train, the control between the successive trains and the operation adjustment of the trains after the deflection from the timetable for some stochastic disturbance considering the effective absorption of the random traveler flow. The research is based on subway system.
     1. Firstly, based on the development history and the research of the MBS, we point out there are five technical problems in the Urban Mass Transit: the determination of the train's tracking interval, the operation organization and the technical parameters, the performance of disturbance rejection, the theory and method of automatic timetable adjustment and the synthetical schedule management system.
     2. The object of the subway is to ensure the transportation capacity. It is discussed under the throughout capacity and the carrying capacity of the line. The main factor which affect the transportation capacity is the throughout capacity and the way of improving the throughout capacity is to reduce the tracking interval time. It is necessary to control the running of the train to ensure the safety of the train under short tracking interval time. The function of Automatic Train Controlling (ATC), including Automatic Train Supervision (ATS), Automatic Train Protection (ATP) and Automatic Train Operation (ATO) are analyzed. MBS will be the further development direction of ATC, so it is important to develop our national ATC.
     3. After the discussion of the principle of the track, circuit and the control way under different signaling systems: fixed block system, quasi-moving block system and moving block system, and the analysis of the concept of section interval and station interval, the models of the section interval and the station interval of the train in the different railway signaling systems are constructed, the calculation and analyses of running interval are conducted and simulated through MATLAB. Simulation result shows that the throughout capacity has been greatly improved under moving block system compared with other two signaling systems. However, it is not fixed, and is not proportional with the velocity of the train, it reaches the maximum value at certain velocity of the train.
     4. The running performance of the subway train under moving block system is studied, the dynamics mathematical model of subway train is constructed, and the event-based control technology is adopted to the control problems of subway. The velocity and acceleration function with respect to the distance is deduced by introducing the action reference variable in the control problem, which aims to minimize the running time. The event-based theory and technology has been widely used in the intelligent system, while it has not been used to the control of subway. The simulation result shows compared with other method:neural network control and proportion-differential control,not only the running time but also the passenger comfort can be improved.
     5. Urban Mass Transit is a hybrid system with the character of motion, and each train is an autonomous vehicle. The cooperation and coordination among the agents is an important index to evaluate the intelligent system. Aimed at the series effect among the trains when the disturbance appear in the time field, the the event-based technology and formation theory are adopted to the control among multi trains. The simulation shows that this method can reconstruct the system easily and increase the cooperation among subsystems with the bi-direction communication and the event-based technology, it also can decrease the series instability in time field effectively, the method can decrease the headway and increase the carrying capacity, ensure the safety and avoid the stop out of station. The work presented in this paper is original and appreciated by the specialists concerned.
     6. Generally, trains' running obeys arranged timetable strictly, but for some unpredictable reason, it is hard to avoid deflection from the timetable. On urban rail transport system which has character of the short headway, a train's delay may affect other trains; sometimes, it even affects the whole system's normal operation. In the current research of operation adjustment, the object of the train's adjustment is to minimize the delay time of the trains. The problem of the effective absorption of the random traveler flow has not been studied. But the effective absorption of the random traveler flow is very important to the evaluation of the urban mass transit. After the analysis of movement of subway under moving block system, when a train delays out from a station for some uncertain disturbance, a new algorithm is proposed and simulated for the first time, which takes the reducing of train's delay time and the increasing of effective absorption to the random traveler flow as the synthetical object.
     Finally, the main work of this thesis is summarized, and the future research directions are proposed.
引文
[1] 黄学跃.关于移动自动闭塞列车间隔控制的研究[D].北京:北方交通大学,1985.
    [2] 汪希时.高速铁路行车安全控制系统概论[J].世界铁路报道,1997,2:33-37.
    [3] 谢肇桐.移动闭塞系统[J].铁道通信信号,1996,32(2):35-37.
    [4] 汪希时,丁正庭,宁滨,卜长坤.京沪线开发应用移动闭塞制度可行性研究—总体与建议[R].北京:北方交通大学,1991.
    [5] 汪希时,丁正庭,宁滨,卜长坤.京沪线开发应用移动闭塞制度可行性研究—总体设计部分[R].北京:北方交通大学,1991.
    [6] 赵明.移动自动闭塞系统基本理论研究[D].北京:北方交通大学,1996.
    [7] 汪希时.铁路区间行车方法的自动调整[D].北京铁道学院论文,1963.
    [8] The advanced train control systems[R]. Transportation research record, No.1314,1991.
    [9] 先进列车控制系统[R].铁道部科学技术情报研究所,1992.
    [10] 王惠生.列车运行控制系统(CARAT)间隔控制的组成和传输方式[J].铁道通信信号,1996,32(4):34-36.
    [11] 吴汶麒.德国无线列车速度控制系统[J].铁道通信信号,1997,33(4):34-36.
    [12] R J Hill, L J Bond. Modelling moving-block railway signalling systems using discrete-event simulation[A].Proceedings of IEEE/ASME Joint Railroad Conference[C], 1995:105-111.
    [13] Z Guo, M C Zhou, L Zakrevski. Optimal tracking interval for predictive tracking in wireless sensor network[A].IEEE Communications Letters[C], 2005, 9(9):805-807.
    [14] H Takeuchi,C J Goodman, S Sone. Moving block signalling dynamics: performance measures and re-starting queued electric trains[A].IEE Proceedings on Electric Power Applications [C],2003,150 (4):483-492.
    [15] M J Lockyear. The application of a transmission based moving block automatic train control system on docklands light railway[A]. International Conference on Developments in Mass Transit Systems[C], 1998:51-61.
    [16] 汪希时,丁正庭.论提高区间通过能力的最优化闭塞—移动自动闭塞系统[J].北方交通大学学报,1991,15(1):73-78.
    [17] 黄建华.由AGT系统理论探讨移动移动闭塞的发展[D].北京:北方交通大学,1986.
    [18] 陈浩然.用于城市轨道交通的小编组、高密度列车运行方案[J].中国铁路,2001(5):44-46.
    [19] 罗丽云,吴汶麒.城市轨道交通移动闭塞列车安全间隔时间分析[J].中国铁道科学,2005,26(1):119-123.
    [20] 石先明.对我国客运专线列车追踪间隔时分的研究[J].中国铁路,2005,515(5):32-35.
    [21] 刘海东,毛保华,何天健等.不同闭塞方式下城轨列车追踪运行过程及其仿真系统的研究[J].铁道学报,2005,27(2):120-125.
    [22] 刘剑锋,丁勇,刘海冬等.城市轨道交通多列车运行模拟系统研究[J].交通运输系统工程与信息,2005,5(1):79-82.
    [23] 吴汶麒.轨道交通运行控制与管理[M].上海:同济大学出版社,2004.
    [24] 杨肇夏,纪加伦.京沪线开发应用移动闭塞制度可行性研究—行车组织方式与区间通过能力[R].北京:北方交通大学,1991.
    [25] 刘英,汪希时.移动自动闭塞条件下列车区间运行延误影响分析[J].北方交通大学学报,1998,22(5):7-12.
    [26] M Kanayama, M Miyoshi, Y Seki, Y Fujiwara, K Sobu.Development of train control simulator[A]. International Conference on Developments in Mass Transit Systems[C], 1998:196-201.
    [27] 刘云,张振江.MAS下区间列车追踪的研究和仿真[J].系统仿真学报,1999,11(1):29-54.
    [28] 张勇,赵明,汪希时.基于移动自动闭塞条件的列车运行仿真研究[J].系统仿真学报,1999,11(3):198-204.
    [29] 宋瑞,何世伟,朱松年.铁路系统线路通过能力分析模拟模型的研究[J].铁道学报,1999,21(2):2-7.
    [30] 郑时德,吴汉琳.铁路行车组织[M].北京:中国铁道出版社,1997.
    [31] 毛保华,何天健,袁振洲,刘海东,赵立宁,陈志英.通用列车运行模拟软件系统研究[J].铁道学报,2000,22(1):1-6.
    [32] T K Ho,B H Mao,Z Z Yuan,H D Liu,Y F Fung. Computer simulation and modelling in railway applications[J].Computer Physics Communications, 2002,143(1):1-10.
    [33] B H MAO, S CHEN, H D LIU, T K HO. A simulation-based study for higher speed trains on busy railway mainlines [A].Applications of advanced technologies in transportation , Proceedings of the seventh international conference [C],ASCE, 2002:305-312.
    [34] 赵春雷.双线自动闭塞区段提高通过能力和旅客列车速度的研究[J].铁道学报,1997,19(1):13-19.
    [35] 刘澜,杜文.多信息自动闭塞列车速度一间隔控制模型及算法[J].铁道学报,2000,22(6):8-12.
    [36] 赵明,汪希时.移动自动闭塞条件下列车追踪运行控制研究[J].铁道学 报,1997,19(3):61-68.
    [37] Y J Hirao, Y Hasegawa. Development of a universal train simulator (UTRAS) and evaluation of signaling systems[J]. RTRR,1995, 36(4): 180-185.
    [38] D Gill, C J Goodman. Computer-based optimization techniques for mass transit railway signalling design [C].IEE Proceedings, 1992,139(3): 261-275.
    [39] T K HO,B H MAO,Z X YANG,Z Z YUAN.A general purpose simulator for train operations[A]. Proceedings of ICTTS'98[C], ASCE, 1998: 830-839.
    [40] 刘云.列车运行仿真系统的建模与实现[J].铁道学报,1995,17(专辑):20-26.
    [41] 何鸿云,朱金陵.列车牵引计算及操纵示意图计算机软件的开发[J]. 西南交通大学学报,2000,35(5):514-516.
    [42] 郭佑民,王志伟,武福等.列车操纵与运行仿真系统[J].兰州铁道学院学报,2002,21(6):125-127.
    [43] 张波,马大伟.中高速列车共线运行的仿真研究[J].中国铁道科学,2003,24(3):119-124.
    [44] 袁磊.基于Agent的城市轨道交通列车运行调整算法研究[D].北京:北方交通大学,2004.
    [45] 周学松,朱钰,胡思继.基于列车运行状态推导图的列车运行调整算法[J].铁道学报,1999,21(6):1-5.
    [46] 周磊山.计算机编制列车运行调整计划的理论与方法研究[D].北京:北方交通大学,1994.
    [47] 金福才,胡思继.列车调整问题的无延迟调度算法研究[J].铁道学报,2003,25(2):10-14.
    [48] 曹家明.单线列车运行调整问题优化模型及算法[J].铁道学报,1994,16(3):72-78.
    [49] 查伟雄,陈治亚,李夏苗.复线列车运行调整理论与方法的研究[J].铁道学报,2000,22(1):12-16.
    [50] 李鹏,张一军.内部协同式列车运行调整专家系统的研究[J].中国铁道科学,1998,19(3):1-9.
    [51] 周磊山,秦作睿.列车运行计划与调整的通用算法及其计算机实现[J].铁道学报,1994,16(3):56-65.
    [52] 程宇.列车运行调整专家系统的研究[D].北方交通大学,1991.
    [53] 陈彦如,彭其渊,蒋阳升.复线列车运行调整满意优化模型研究[J].铁道学报,2003,25(3):8-12.
    [54] C W Tsang, T K Ho. A prioritized fuzzy constraint satisfaction approach to model agent negotiation for railway scheduling [A]. Proceedings of 2004 International Conference on Machine Learning and Cybernetics[C], 2004:1795-1801.
    [55] M T Isaai, M G Singh.An object-oriented, constraint-based heuristic for a class of passenger-train scheduling problems[J]. IEEE Transactions on Systems, Man and Cybernetics, Part C, 2000,30(1):12-21.
    [56] H Cheng, C C Lin. An interactive train scheduling workbench based on artificial intelligence[A]. Sixth International Conference on Tools with Artificial Intelligence[C], 1994:42-48.
    [57] M T Isaai, N P Cassaigne. Predictive and reactive approaches to the train-scheduling problem: a knowledge management perspective[J]. IEEE Transactions on Systems, Man and Cybernetics, PartC, 2001, 31 (4):476-484.
    [58] J F Chen, R L Lin, Y C Liu. Optimization of an MRT train schedule: reducing maximum traction power by using genetic algorithms[J].IEEE Transactions on Power Systems, 2005,20(3): 1366-1372.
    [59] L A Snider, E Lo, T M Lai. Harmonic simulation of DC traction system with multi-train operation[A].International Conference on Advances in Power System Control, Operation and Management[C], 2000,1 (30): 105-109.
    [60] M Sandidzadeh,李鹏.一种用于地铁轻轨的智能行车控制方法[J].中国铁道科学,2000,21(2):111-118.
    [61] 彭其渊,杨明伦,聂勋煌.单线区段实用货物列车运行图的优化模型及算法[J].铁道学报,1995,17(3):15-20.
    [62] 彭其渊,杨明伦.单线实用货物列车运行图计算机编制系统[J].西南交通大学学报,1995,30(5):537-542.
    [63] 彭其渊,王宝杰,周党瑞.基于实用的一种网络列车运行图计算方法[J].西南交通大学学报,1999,34(5):588-593.
    [64] 彭其渊.网络列车运行图模型算法研究及系统设计[D].成都:西南交通大学,1998.
    [65] 彭其渊,杨明伦.单线区段货物列车始发方案的优化模型及求解方法[J].西南交通大学学报,1995,30(2):177-181.
    [66] K Komaya. A knowledge-based approach railway scheduling[A]. Proceedings of IEEE Conference on Artifical Intelligence for applicationns [C], 1991,1(2):404-411.
    [67] T W Chiang, H Y Hau. Knowledge-based system for railway scheduleing[J]. Data & Knowledge Engineering,1998,27:289-312.
    [68] 贾利民.模糊控制与决策及其在铁路自动化中的应用[D].北京:铁道部科学研究院,1991.
    [69] 严余松.单线铁路平行运行图区间通过能力的整数规划法[J].西南交通大学学报,1994,29(1):97-101.
    [70] 王进勇.分局调度系统列车运行调整优化模型与算法[M].成都:西南交通大学出版社,2003.
    [71] P Hellstom, C Sehwier. An evaluation of algorithms and systems for computer aided train dispatching[J].Computer in Railway,1998,5: 585-595.
    [72] 周伟.基于DEDS模型预测的高速列车群运行调整新方法研究[D].西安:西安交通大学,1997.
    [73] 刘皓伟.行车指挥系统的Petri网建模与列车运行调整的遗传算法研究[D].北京:铁道部科学研究院,2000.
    [74] T W Chiang, H Y Hau. Cycle detection in repair-base railway scheduling system[A]. Proceedings of IEEE Conference on Robotics and Automation Minneapolis[C], 1996,2:517-522.
    [75] M Zweban, E Daris, B Davn, M J Deale. Scheduling and rescheduling with iterative repair[J]. IEEE Transactions on Systems, Man and Cybernetics, 1993,23(6):588-596.
    [76] T W Chaing, H Y Hau. Railway scheduling system using repair-based approach[A]. Proceedings of IEEE Conference on Tools with Artificial Intelligence[C], 1995:71-78.
    [77] 陈彦如,彭其渊,蒋阳升.复线列车运行调整满意优化模型研究[J].铁道学报,2003,25(3):8-12.
    [78] 陈彦如.ITS指挥系统的满意优化理论及列车运行调整研究[D].成都:西南交通大学,2003.
    [79] 金炜东.满意优化问题与列车操纵优化方法研究[D].成都:西南交通大学,1998.
    [80] 李平.面向对象遗传算法及其在铁路行车指挥中的应用[D].北京:铁道部科学研究院,2001.
    [81] 章优仕,金炜东.基于遗传算法的单线列车运行调整体系[J].西南交通大学学报,2005,40(2):147-152.
    [82] 史峰,黎新华,秦进等.单线列车运行调整的最早冲突优化方法[J].中国铁道科学,2005,26(1):106-113.
    [83] T W Chiang,H Y Hau,H M Chiang. Knowledge-based system for railway scheduling[J].Knowledge Engineering, 1998,27:289-312.
    [84] I Sahin. Railway traffic control and train scheduling based on inter-train conflict management[R].Transportation Research: PartB, 1999,33: 522-534.
    [85] A Fay. A fuzzy knowledge-based system for railway traffic control[J].Artificial Intelligence,2000,13:719-729.
    [86] T Huisman, R J Boucherie. Running times on railway sections with heterogeneous train traffic[R]. Transportation Research: PartB, 2001, 35:271-292.
    [87] 贾利民.基于模糊决策的分布式智能化行车指挥方法[J].中国铁道科学,1993,14(3):79-90.
    [88] 李夏苗,查伟雄,李轶平.以客运为主繁忙干线区段列车运行调整计划的优化[J].铁道学报,1999,21(6):10-14.
    [89] 张国宝.城市轨道交通运输组织[M].北京:中国铁道出版社,2000.
    [90] 季令等.城市轨道交通运营管理[M].北京:中国铁道出版社,1998.
    [91] 吴汶麒.城市轨道交通信号与通信系统[M].北京:中国铁道出版社,2001.
    [92] 张济民,吴汶麒.准移动闭塞列车安全间隔时间的计算[J].铁道学报,1997,21(3):6-10.
    [93] 韩立春,吴汶麒.数字轨道电路列车最小安全间隔时间计算[J].城市轨道交通研究,2001,2:36-40.
    [94] P G Howlett, P J Pudney. Energy efficient train control[M].Springer, 1995.
    [95] 金炜东,王自力等.列车节能操纵优化方法研究[J].铁道学报,1997,19(6):58-62.
    [96] 于建国,苗彦英.地铁电动车组运行模型的研究[J].大连铁道学院学报,2001,22(2):43-45.
    [97] 毛明平,陶生桂,王曰凡.上海地铁2号线牵引仿真计算研究[J].城市轨道交通研究,2001,14(2):22-27.
    [98] 钮泽全.牵引计算学[M].北京:中国铁道出版社,1984:30-38,52-55.
    [99] 陈喜红,李敏玲,柳晓峰.地铁列车运行时间仿真[J].电力机车与城轨车辆,2003,26(4):17-19.
    [100] N Xi, T J Tarn, A K Bejczy. Intelligent planning and control for multi-robot coordination: An Event-based approach[J]. IEEE Transactions on robotics and automation, 1996,12: 439-452.
    [101] W Kang, N Xi, Andy Sparks. Theory and application of formation control in a perceptive referenced frame[A]. Proceedings of 39th IEEE Conference on Decision and control[C], 2000,1:352-357.
    [102] M M Song, T J Tarn N Xi. Intelligent control: Analytical integration of hybrid system[A]. Proceedings of 37th IEEE Conference on Decision and control[C], 1998,3:2621-2626.
    [103] N Xi. Event-based motion planning and control for robotic system[D] . Washington: Washington University, 1993.
    [104] W Kang, N Xi. Formation control of multiple autonomous vehicles[A].Proceedings of IEEE International Conference on Control Applications[C], 1999,2: 1027-1032.
    [105] M M Song, T J Tarn , N Xi. Integration of task scheduling, action planning and control in robotic manufacturing systems[A]. Proceedings of the IEEE[C], 2000,88(3): 1097-1107.
    [106] G Antonelli, N Sarkar, S Chiaverini. External force control for underwater vehicle manipulator systems[A].Proceedings of the 38th IEEE Conference of Decision and Control[C], 1999: 2795-2980.
    [107] J H Chung, S A Velinsky. Robust interaction control of mobile manipulator-dynamic model based coordination[J].Intelligent and Robotic Systems, 1999,26:47-63.
    [108] J H Chung, S A Velinsky, R A Hess. Interaction control of redundant mobile manipulator[J].The International Journal of Robotics Research, 1998,17(12): 1302-1309.
    [109] J K Lee, H S Cho. Mobile manipulator motion planning for multiple tasks using global optimization approach[J]. Intelligent and Robotic Systems, 1997,18:169-190.
    [110] H Seraji. A unified approach to motion control of mobile manipulators[J].The International Journal of Robotics Research, 1998,17(2):107-118.
    [111] Y Yamamoto, X P Yun. Effect of dynamic interaction on coordinated control of mobile manipulators[J]. IEEE Transactions on Robotics and Automations, 1996,12(5):816-824.
    [112] B K Ghosh, T J Tarn, N Xi ,Z Y Yu, D Xiao.Robotic motion planning and manipulation in an uncalibrated environment[J]. IEEE Transactions on Robotics and Automations,1998,5(4): 50-57.
    [113] A Bemporad, T J Tarn, N Xi. Predictive path parameterization for constrained robot control[J]. IEEE Transactions on Control Systems Technology, 1999, 7(6): 648-656.
    [114] W H Sheng, H P Chen, N Xi, Y F Chen. Tool path planning for compound surfaces in spray forming processes[J]. IEEE Transactions on Robotics and Automations,2005,2(3): 240-249.
    [115] A K Ramadorai, T J Tarn, A K Bejczy, N Xi. Task-driven control of multi-arm systems[J].IEEE Transactions on Control Systems Technology, 1994,2(3):198-206.
    [116] H P Chen, W H Sheng, N Xi, M M Song, Y F Chen. Automated robot trajectory planning for spray painting of free-form surfaces in automotive manufacturing[A].Proceedings of IEEE International Conf- erence on Robotics and Automation[C], 2002,1: 450-455.
    [117] T J Tarn, M M Song, N Xi. Intelligent planning and control for hybrid system[A].Proceedings of IEEE International Conference on Intelligent Robots and Systems[C], 1998,2:972-977.
    [118] T J Tarn, M M Song, N Xi, B K Ghosh. Multi-sensor fusion scheme for calibration-free stereo vision in a manufacturing workcell[A]. Proceedings of IEEE International Conference on Multisensor Fusion and Integration for Intelli gent Systems, 1996,8: 416-423.
    [119] W H Sheng, N Xi, M M Song, Y F Chen. Graph-based surface merging in CAD-guided dimensional inspection of automotive parts[A]. Proceedings of IEEE International Conference on Robotics and Automation[C], 2002,3:3127-3132.
    [120] G N Saridis. Knowledge implementation:structure of intelligent control system[A].Proceedings of IEEE International Symp.on Intelligent Control[C], 1987:9-19.
    [121] J S Albus. Hierarchical interaction between sensory processing and world modeling in intelligent systems[A].Proceedings of 5th IEEE Sympo.on Intelligent Control[C], 1990,1:53-59.
    [122] B P Zeigler. Object-oriented simulation with hierarchial, modular models:intelligent agents and endomorphic system[M].San Diego, CA, Academic Press, 1990.
    [123] A E Bryson, J Y C Ho. Applied optimal control: optimization, estimation and control [M]. National Defence Industry Press.
    [124] T Gustavi, X M Hu. Formation control for mobile robots with limited sensor information[A].Proceedings of the 2005 IEEE International Conference on Robotics and Automation[C],2005:1791-1796.
    [125] D Gennaro, M C Iannelli, L Vasca, F. Formation control and collision avoidance in mobile agent system[A]. Proceedings of the 2005 IEEE International Symposium on Intelligent Control[C], 2005:796-801.
    [126] X H Li, J Z Xiao, J D Tan. Modeling and controller design for multiple mobile robots formation control[A].IEEE International Conference on Robotics and Biomimetics[C], 2004:838-843.
    [127] W Ren, R Beard. Trajectory Tracking for Unmanned Air Vehicles With Velocity and Heading Rate Constraints[J].IEEE Transactions on Control System Technology, 2004,12(5):706-716.
    [128] N E Leonard, E Fiorelli. Virtual Leaders, Artificial Potentials and Coordinated Control of Groups[A].Proceedings of the 40th IEEE Conference of Decision and Control[C], 2001,3:2968-2973.
    [129] P Seiler, A Pant, K Hedrick. Analysis of bird formations[A]. Proceedings of the 41st IEEE Conference of Decision and Contro 1 [C], 2002,1:118-123.
    [130] T Balch, R Arkin. Behavior-Based Formation Control for Multirobot Teams[J]. IEEE Transactions on Robotics and Automation, 1998, 14(6):926-939.
    [131] H G Tanner, A Jadbabaie, G J Pappas. Stable Flocking of Mobile Agents Part Ⅰ: Fixed Topology[A].Proceedings of the 42nd IEEE Conference of Decision and Control[C], 2003,2:2010-2015.
    [132] H G Tanner, A Jadbabaie, G J Pappas. Stable Flocking of Mobile Agents Part Ⅱ: Dynamic Topology[A].Proceedings of the 42nd IEEE Conference of Decision and Control[C], 2003,2:2016-2021.
    [133] A Jadbabaie, J Lin, A S Morse.Coordination of Groups of Mobile Autonomous Agents Using Nearest Neighbor Rules[J].IEEE Transactions on Automatic Control, 2003, 48(6):988-1001.
    [134] K Song, C Tang. Learning for Cooperation in Multirobot Team Competitions[A]. Proceedings of IEEE International Symposium on Computational Intelligence in Robotics and Automation[C],2001,29: 302-307.
    [135] 赵映莲.京沪高速铁路客车开行方案的研究[R].北京:铁道部科学研究院研究报告,1998.
    [136] 吴洋,王月明,曾理.晚点情况下地铁列车间隔的实时调整方法[J].电力 机车与城轨车辆,2003,26(5):21-23.
    [137] 吴洋,罗霞.一种晚点地铁列车实时调整策略及其动态速控模式[J].中国铁道科学,2005,26(6):113-118.
    [138] S Russell, P Norvig. Artificial intelligence: A modern approach[M]. Prentice-Hall,1995.
    [139] M Woolbridge, N R Jennings. Agents theories, architectures and languages: a Survey [M]. Berlin: Springer-Verlag ,1995.
    [140] 张云勇.移动Agent及其应用[M].北京:清华大学出版社,2002.
    [141] 史忠植.智能主体及其应用[M].北京:科学出版社,2000.
    [142] 何炎强,陈莘明.Agent和多Agent系统的设计与应用[M].武汉:武汉大学出社,2001.
    [143] R Brooks. Intelligence without representation[J].Artifical Intelligence, 1991,47:139-159.
    [144] X P Ma, J J Ni, L Z Xu. Reasearch on the multi-agent modeling and simulating method of CAS and the agent rule learning[A]. Proceedings of 2005 International Conference on Machine Learning and Cybernetics[C], 2005,1,(18):243-248.
    [145] A H Tan, D Xiao. Self-organizing cognitive agents and reinforcement learning in multi-agent environment[A].IEEE/WIC/ACM International Conference on Intelligent Agent Technology[C], 2005:351-357.
    [146] Y Tahara, A Ohsuga, S Honiden. Agent system development method based on agent patterns[A].Proceedings of the 1999 International Conference on Software Engineering[C], 1999:356-367.
    [147] B J Grosz, S Kraus.Collaborative plan foe group activities[J].Artifical Intelligence,1996,86(2):269-357.
    [148] B.Chaibdraa, B Moulin. Trends in distributed artificial intelligence[J]. Artifical Intelligence, 1992,56(6):35-66.
    [149] J C Moon, S J Kang. A multi-agent architecture for intelligent home network service using tuple space model[J]. IEEE Transactions on Consumer Electronics, 2000, 46(3):791-794.
    [150] K S Barber, T H Liu, S Ramaswamy. Conflict detection during plan integration for multi-agent systems [J]. IEEE Transactions on Systems, Man and Cybernetics,2001,31(4): 616-628.
    [151] M Egerstedt, X M Hu.Formation constrained multi-agent control[J]. IEEE Transactions on Robotics and Automation, 2001,17(6):947-951.
    [152] R R Yager. Penalizing strategic preference manipulation in multi-agent decision making[J]. IEEE Transactions on Fuzzy Systems, 2001, 9(3):393-403.
    [153] S Noh, P J Gmytrasiewicz. Flexible Multi-Agent Decision Making Under Time Pressure[J].IEEE Transactions on Systems, Man and Cybernetics, 2005,35(5):697-707.
    [154] B Burmeister, A Haddadi, G Matylis. Application of multi-agent systems in traffic and transportation[A].Proceedings of IEE Conference on Software Engineering[C], 144(1):51-60.
    [155] P Tabuada, G J Pappas, P Lima. Motion feasibility of multi-agent formations[J]. IEEE Transactions on Robotics, 2005,21 (3): 387-392.
    [156] R Sikora. Coordination mechanisms for multi-agent manufacturing systems: applications to integrated manufacturing scheduling[J].IEEE Transactions on Engineering Management, 1997, 44(2): 175-187.
    [157] H T Ou, W D Zhang, W Y Zhang, X M Xu. A novel multi-agent Q-learning algorithm in cooperative multi-agent system[A]. Proceedings of the 3rd World Congress on intelligent Control and Automation[C], 2000,1 (28):272-276.
    [158] T Balch, R C Arkin. Behavior-based formation control for multirobot Teams[J]. IEEE Transactions on Robotics and Automation, 1998,14(6): 926-939.
    [159] P Ogern, M Egerstedt, X Hu. A control Lyapunov function approach to multi-agent coordination[A].Proceedings of IEEE Conference on Decision and Control[C], 2001,2:1150-1152.
    [160] D Swaroop, J K Hedrick. String stability of interconnected systems[J]. IEEE Transactions on Automatic Control, 1996,41(3): 349-357.
    [161] M Berna-Koes, I Nourbakhsh, K Sycara.Communication efficiency in multi-agent systems[A]. Proceedings of IEEE Conference on Robotics and Automation[C],2004,3:2129-2134.
    [162] Z J Gao, G Z Yan, G Q Ding, H Huang. Research of communication mechanism of multi-agent robot systems[A]. Proceedings of International Symposium on Micromechatronics and Human Science[C], 2001:75-79.
    [163] A Riera, M Lama, E Sanchez, R Amorim, X A Vila, S Barro.Study of communication in a multi-agent system for collaborative learning scenarios[A]. Proceedings of 12th Euromicro Conference on Parallel, Distributed and Network-Based Processing[C],2004:233-240.

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