基于节能优化的列车自动驾驶算法研究
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摘要
随着社会的进步和发展,人们对列车运行安全、时间和舒适度的要求不断提高,与此同时,计算机、通信和控制技术得到巨大的发展。于是,人们开始研究应用这些先进的技术,设计实现列车自动驾驶系统,代替司机的操作,实现列车的自动驾驶,达到安全、准时、节能、准确停车和舒适的目的。
本文在对列车自动驾驶系统的结构、功能和工作原理的深入研究基础上,对列车运行过程中的工况和在各种工况下的受力情况进行了分析明确了工况转换的原则,并对驾驶策略进行了研究。采用数学的方法对列车自动驾驶过程进行了描述,建立了节能优化操纵的数学模型。采用CRH-2型动车组的数据,以数学模型为基础,在模拟线路上应用粒子群.优化算法对列车运行过程进行优化。所得ATO目标曲线基本满足了列车运行过程中对安全性、准时性、舒适性和准确停车的要求,同时节约了能耗。然后,根据实际情况,提出对速度控制器的性能要求,并应用MATLAB,设计了ATO系统仿真软件。文中采用预测函数控制作为核心算法,设计了ATO系统的速度控制器,并与广义预测控制算法设计的速度控制器进行对比、分析,结果表明前者在鲁棒性、稳定性和超调等方面优于后者。从而,验证了将预测函数控制算法应用到列车自动驾驶系统中的可行性。
研究结果表明,采用以上方法设计的ATO系统仿真软件,适用于不同线路和列车,可有效降低天气、路况等不确定因素对列车运行带来的影响,能够满足列车自动驾驶的各项指标。
With the social progress and development as well as the huge improvement on technologies of computer, communication and control, there has been an increasing demand for the safety, running time and riding comfort of a train. Therefore, people began to apply these technologies to the design of automatic train control system so as to replace the operation of the driver, realize automatic operation and achieve purposes of safety, punctuality, energy efficiency, precise positioning parking and comfort. On the basis of in-depth study on the structure, function and working principle of the automatic train control system, this paper begins with an analysis on the operating mode during the operation process of the train, along with the force conditions under various operating modes. After ascertaining the switching principle of the operating mode, the author conducts a study on the driving strategies and applies mathematical methods in the description of automatic train operation process, followed by the establishment of a mathematical model for the energy-saving and optimized control. Based on the data of CRH-2 and mathematical model, the author optimizes the running process of the train on the simulate route by the Particle Swarm Optimization Algorithm. The ATO target curve basically achieves the requirements of safety, punctuality, comfort and precise positioning parking, with a great cut-down on energy. In consideration of the actual conditions, the author presents the requirement on the performance of speed controller and designs a simulation software for ATO system by MATLAB. With Predictive Functional Control as the core algorithm, a speed controller of ATO system is designed and then compared with the speed controller designed by Generalized Predictive Control algorithm. The results show that the former speed controller is superior to the latter one in terms of robustness, stability and overshoot. Thereby, the feasibility of the application of Predictive Functional Control algorithm in the automatic train control system is validated.
The research results indicate that the simulation software for ATO system designed by the above-mentioned methods can be applied to various routes and trains. It can also effectively reduce the impact of weather, road conditions and other uncertainties on train operations, achieving the targets of automatic train operation.
本文在对列车自动驾驶系统的结构、功能和工作原理的深入研究基础上,对列车运行过程中的工况和在各种工况下的受力情况进行了分析明确了工况转换的原则,并对驾驶策略进行了研究。采用数学的方法对列车自动驾驶过程进行了描述,建立了节能优化操纵的数学模型。采用CRH-2型动车组的数据,以数学模型为基础,在模拟线路上应用粒子群.优化算法对列车运行过程进行优化。所得ATO目标曲线基本满足了列车运行过程中对安全性、准时性、舒适性和准确停车的要求,同时节约了能耗。然后,根据实际情况,提出对速度控制器的性能要求,并应用MATLAB,设计了ATO系统仿真软件。文中采用预测函数控制作为核心算法,设计了ATO系统的速度控制器,并与广义预测控制算法设计的速度控制器进行对比、分析,结果表明前者在鲁棒性、稳定性和超调等方面优于后者。从而,验证了将预测函数控制算法应用到列车自动驾驶系统中的可行性。
研究结果表明,采用以上方法设计的ATO系统仿真软件,适用于不同线路和列车,可有效降低天气、路况等不确定因素对列车运行带来的影响,能够满足列车自动驾驶的各项指标。
With the social progress and development as well as the huge improvement on technologies of computer, communication and control, there has been an increasing demand for the safety, running time and riding comfort of a train. Therefore, people began to apply these technologies to the design of automatic train control system so as to replace the operation of the driver, realize automatic operation and achieve purposes of safety, punctuality, energy efficiency, precise positioning parking and comfort. On the basis of in-depth study on the structure, function and working principle of the automatic train control system, this paper begins with an analysis on the operating mode during the operation process of the train, along with the force conditions under various operating modes. After ascertaining the switching principle of the operating mode, the author conducts a study on the driving strategies and applies mathematical methods in the description of automatic train operation process, followed by the establishment of a mathematical model for the energy-saving and optimized control. Based on the data of CRH-2 and mathematical model, the author optimizes the running process of the train on the simulate route by the Particle Swarm Optimization Algorithm. The ATO target curve basically achieves the requirements of safety, punctuality, comfort and precise positioning parking, with a great cut-down on energy. In consideration of the actual conditions, the author presents the requirement on the performance of speed controller and designs a simulation software for ATO system by MATLAB. With Predictive Functional Control as the core algorithm, a speed controller of ATO system is designed and then compared with the speed controller designed by Generalized Predictive Control algorithm. The results show that the former speed controller is superior to the latter one in terms of robustness, stability and overshoot. Thereby, the feasibility of the application of Predictive Functional Control algorithm in the automatic train control system is validated.
The research results indicate that the simulation software for ATO system designed by the above-mentioned methods can be applied to various routes and trains. It can also effectively reduce the impact of weather, road conditions and other uncertainties on train operations, achieving the targets of automatic train operation.
引文
[1]万传军.列车自动控制系统的设计与实现[J].西安石油学院学报,2003(5)：69-72
[2]蔡爱华,季锦章.地铁信号系统的现状及发展趋势[J].电子工程师,2000(5)：1-4
[3]唐涛,黄良骥.列车自动驾驶系统控制算法综述[J].铁道学报,2003,Vol.25(2)：98-102
[4]林瑜筠.城市轨道交通信号[M].中国铁道出版社,2009
[5]Yasunobu S, et al. Application of Predictive Fuzzy Control to Automatic Train Operation Controller [A]. In:Proc. Of IEC-ON'84[C].1984.657-662
[6]Oshima H, et al. Automatic Train Operation System Based on Predictive Fuzzy Control [A]. In:Artificial Intelligence for Industrial Applications,1988. IEEE AI'88, Proceedings of the International Workshop[C].1988.485-489
[7]Sekine S, et al. Application of Fuzzy Neural Network control to Automatic Train Operation and Tuning of its Control Rules [A]. In:Volume:4, Fuzzy Systems,1995. Proceedings of 1995 IEEE International Conference[C].1995. 1941-1746
[8]Chang, C.S., S.S. Sim. Optimizing Train Movements throng Coast Control Using Genetic Algorithms [J]. IEEE,1997, Vol.144(1):65-73
[9]Chang, C.S., D.Y. Xu, H.B. Quek. Pareto-optimal Set Based Multiobjective Tuning of Fuzzy Automatic Train Operation for Mass Transit System [J]. IEEE,1999, Vol.146(5):577-583
[10]Seong-Ho Han, et al. An optimal automatic train operation control using genetic algorithms [J]. IEEE TENCON.1999.360-362
[11]X. Zhuan, X. Xia. Speed regulation with measured output feedback in the control of heavy haul [J]. Elsevier Ltd.2007, Vol.44(1):242-247
[12]张建华,贾利民,张锡第.新型模糊预测控制及其在列车自动运行过程中的应用[J].中国铁道科学,1996,Vol.17(4)：101-109
[13]Wang Jing, et al. Direct Fuzzy Neural Control with Application to Automatic Train Operation [J]. Control Theory and Applications.1998,391-399
[14]武妍,施鸿宝.基于神经网络的地铁列车运行过程的集成型智能控制[J].铁道学报.2000,Vol.22(3)：10-15
[15]董海鹰,党建武.基于框架式模糊Petri网列车专家控制系统知识表示研究[J].铁道学报,2000,Vol.22(3)：112-115
[16]李会超,翟森,冯晓云.一种高性能地铁列车运行控制算法研究与仿真[J].Proceedings of the 7th World Congress on Intelligent Control and Automation,2008
[17]国资委网站.中国通号城轨列车自动驾驶系统通过技术审查[DB/OL].http://finance.ifeng.con/roll/20091117/1473140.shtml,2009
[18]王长林,林颖.列车运行控制技术.成都：西南交通大学出版社.2008
[19]石红国.列车运行过程仿真及优化研究[D].西南交通大学博士学位论文.成都,2006
[20]曾建潮.微粒群算法[M].北京：科学出版社,2005
[21]Eberhart R, Kennedy J. A new optimizer using particle swarm theory[C]. In: Proceeding of the 6th International Symposium on Micro Machine and Human Science, NJ:IEEE CS,1995:39-43
[22]Kennedy J, Ebethart R. Particle Swam Optimization[C].In:Proceeding of IEEE International Conference on Neural Networks, Piscataway, NJ: IEEE CS,1995:1942-1948
[23]时速200和300公里动车组主要技术条件.铁运函[2006]462号
[24]黄秀玲.广义预测控制算法在ATO系统中的应用与研究.西南交通大学硕士学位论文,2009
[25]何庆.基于遗传算法和模糊专家系统的列车优化控制.西南交通大学硕士学位论文,2006
[26]饶晓璐.列车自动驾驶算法研究及仿真实现.西南交通大学硕士学位论文2010
[27]王伟.广义预测控制理论及其应用[M].北京：科学出版社,1998
[28]李国勇.智能控制及其MATLAB实现[M].北京：电子工业出版社,2005
[29]王自力.列车节能运行优化操纵的研究[J].西南交通大学学报.1994,Vol.29(3)：275-279
[30]崔世文,冯晓云.列车优化操纵与自动驾驶模式的研究与仿真[J].铁道机车车辆,2005,Vol.25(5)：9-12
[31]冯晓云,何鸿云,朱金陵.列车优化操纵原则及其优化操纵策略的数学描述.机车电传动,2001(4)：13-16
[32]吴启迪.智能微粒群算法研究及应用[M].北京：科学出版社,2002
[33]刘智勇.高速磁浮列车安全速度曲线算法及紧急制动控制研究[D].浙江大学博士学位论文,2008
[34]Clarke, D. W., C. Mohtadi. Properties of Generalized Predictive Control [J]. Automatica,1989, Vol.25(6):859-875
[35]Mehra, R.K., R. Rouhani, J. Ererno, J. Richalet, A. Rault. Model Algorithmic Control:Review and Recent Development[C].Eng. Foundation Conf. on Chemical Process Control 2,Sea Island,Georgia,1982,287-310.
[36]舒迪前,预测控制系统及其应用[M].北京：机械工业出版社,1998
[37]Eberhart, R., Shi Y. Comparison between Genetic Algorithm and Particle Swarm Optimization [M]. Lecture Notes in Computer Science (Evolutionary Programming VII). Berlin:Spring Press,1998
[38]刘建华,粒子群算法的基本理论及其改进研究[D].中南大学博士学位论文,2009
[39]高芳,智能粒子群优化算法研究[D].哈尔滨工业大学博士学位论文,2008
[40]丁勇.列车运行计算与操纵优化模拟系统的研究[D].北京交通大学博士学位论文,2005.
[41]Cutler, C.R., B.L. Ramaker. Dynamic Matrix Control—A Computer Control[C]. Proceedings of the Joint Automatic Control Conference, San Francisco, WP5-B,1980.
[42]Cutler, C.R., T. Perry. Real Time Optimization with Multivariable Control is required to Maximize Profits[C].The National Meeting of the American Institute of Chemical Engineers, Los Angeles, California,1982
[43]庄培栋,沈永良,张连柱,王海孝.加权广义预测控制算法的改进[J].黑龙江大学自然科学学报.2002,Vol.19(2)：33-35.
[44]金晓明,王树青,荣冈.预测函数控制(PFC)—一种新型预测控制策略.化工自动化及仪表,1999,Vol.26(2)：74-80
[45]Andres Vivas, Philippe Poigent, Francois Pierrot. Predictive functional control for parallel robot. Intl. Conference on Intelligent Robots and systems. Las Vegas, Nevada.2003, IEEE,2785-2790
[46]诸静,颜文俊,诸勇等.智能预测控制及其应用[M].杭州：浙江大学出版社,2002.
[47]韩璞,于萍,王国玉,王东风.单元机组负荷系统预测函数控制[J].电工技术学报,2004,Vol.19(10)：47-52
[48]陈垚光.精通MATLAB GUI设计[M].北京：电子工业出版社,2008
[49]李传军C语言与MATLAB接口—编程与实例[M].北京：北京邮电大学出版社,2004
[50]董辰辉,彭雪峰MATLAB 2008全程指南[M].北京：电子工业出版社,2009
[51]罗华飞MATLAB GUI设计学习手记[M].北京：北京航空航天大学出版社,2009
[2]蔡爱华,季锦章.地铁信号系统的现状及发展趋势[J].电子工程师,2000(5)：1-4
[3]唐涛,黄良骥.列车自动驾驶系统控制算法综述[J].铁道学报,2003,Vol.25(2)：98-102
[4]林瑜筠.城市轨道交通信号[M].中国铁道出版社,2009
[5]Yasunobu S, et al. Application of Predictive Fuzzy Control to Automatic Train Operation Controller [A]. In:Proc. Of IEC-ON'84[C].1984.657-662
[6]Oshima H, et al. Automatic Train Operation System Based on Predictive Fuzzy Control [A]. In:Artificial Intelligence for Industrial Applications,1988. IEEE AI'88, Proceedings of the International Workshop[C].1988.485-489
[7]Sekine S, et al. Application of Fuzzy Neural Network control to Automatic Train Operation and Tuning of its Control Rules [A]. In:Volume:4, Fuzzy Systems,1995. Proceedings of 1995 IEEE International Conference[C].1995. 1941-1746
[8]Chang, C.S., S.S. Sim. Optimizing Train Movements throng Coast Control Using Genetic Algorithms [J]. IEEE,1997, Vol.144(1):65-73
[9]Chang, C.S., D.Y. Xu, H.B. Quek. Pareto-optimal Set Based Multiobjective Tuning of Fuzzy Automatic Train Operation for Mass Transit System [J]. IEEE,1999, Vol.146(5):577-583
[10]Seong-Ho Han, et al. An optimal automatic train operation control using genetic algorithms [J]. IEEE TENCON.1999.360-362
[11]X. Zhuan, X. Xia. Speed regulation with measured output feedback in the control of heavy haul [J]. Elsevier Ltd.2007, Vol.44(1):242-247
[12]张建华,贾利民,张锡第.新型模糊预测控制及其在列车自动运行过程中的应用[J].中国铁道科学,1996,Vol.17(4)：101-109
[13]Wang Jing, et al. Direct Fuzzy Neural Control with Application to Automatic Train Operation [J]. Control Theory and Applications.1998,391-399
[14]武妍,施鸿宝.基于神经网络的地铁列车运行过程的集成型智能控制[J].铁道学报.2000,Vol.22(3)：10-15
[15]董海鹰,党建武.基于框架式模糊Petri网列车专家控制系统知识表示研究[J].铁道学报,2000,Vol.22(3)：112-115
[16]李会超,翟森,冯晓云.一种高性能地铁列车运行控制算法研究与仿真[J].Proceedings of the 7th World Congress on Intelligent Control and Automation,2008
[17]国资委网站.中国通号城轨列车自动驾驶系统通过技术审查[DB/OL].http://finance.ifeng.con/roll/20091117/1473140.shtml,2009
[18]王长林,林颖.列车运行控制技术.成都：西南交通大学出版社.2008
[19]石红国.列车运行过程仿真及优化研究[D].西南交通大学博士学位论文.成都,2006
[20]曾建潮.微粒群算法[M].北京：科学出版社,2005
[21]Eberhart R, Kennedy J. A new optimizer using particle swarm theory[C]. In: Proceeding of the 6th International Symposium on Micro Machine and Human Science, NJ:IEEE CS,1995:39-43
[22]Kennedy J, Ebethart R. Particle Swam Optimization[C].In:Proceeding of IEEE International Conference on Neural Networks, Piscataway, NJ: IEEE CS,1995:1942-1948
[23]时速200和300公里动车组主要技术条件.铁运函[2006]462号
[24]黄秀玲.广义预测控制算法在ATO系统中的应用与研究.西南交通大学硕士学位论文,2009
[25]何庆.基于遗传算法和模糊专家系统的列车优化控制.西南交通大学硕士学位论文,2006
[26]饶晓璐.列车自动驾驶算法研究及仿真实现.西南交通大学硕士学位论文2010
[27]王伟.广义预测控制理论及其应用[M].北京：科学出版社,1998
[28]李国勇.智能控制及其MATLAB实现[M].北京：电子工业出版社,2005
[29]王自力.列车节能运行优化操纵的研究[J].西南交通大学学报.1994,Vol.29(3)：275-279
[30]崔世文,冯晓云.列车优化操纵与自动驾驶模式的研究与仿真[J].铁道机车车辆,2005,Vol.25(5)：9-12
[31]冯晓云,何鸿云,朱金陵.列车优化操纵原则及其优化操纵策略的数学描述.机车电传动,2001(4)：13-16
[32]吴启迪.智能微粒群算法研究及应用[M].北京：科学出版社,2002
[33]刘智勇.高速磁浮列车安全速度曲线算法及紧急制动控制研究[D].浙江大学博士学位论文,2008
[34]Clarke, D. W., C. Mohtadi. Properties of Generalized Predictive Control [J]. Automatica,1989, Vol.25(6):859-875
[35]Mehra, R.K., R. Rouhani, J. Ererno, J. Richalet, A. Rault. Model Algorithmic Control:Review and Recent Development[C].Eng. Foundation Conf. on Chemical Process Control 2,Sea Island,Georgia,1982,287-310.
[36]舒迪前,预测控制系统及其应用[M].北京：机械工业出版社,1998
[37]Eberhart, R., Shi Y. Comparison between Genetic Algorithm and Particle Swarm Optimization [M]. Lecture Notes in Computer Science (Evolutionary Programming VII). Berlin:Spring Press,1998
[38]刘建华,粒子群算法的基本理论及其改进研究[D].中南大学博士学位论文,2009
[39]高芳,智能粒子群优化算法研究[D].哈尔滨工业大学博士学位论文,2008
[40]丁勇.列车运行计算与操纵优化模拟系统的研究[D].北京交通大学博士学位论文,2005.
[41]Cutler, C.R., B.L. Ramaker. Dynamic Matrix Control—A Computer Control[C]. Proceedings of the Joint Automatic Control Conference, San Francisco, WP5-B,1980.
[42]Cutler, C.R., T. Perry. Real Time Optimization with Multivariable Control is required to Maximize Profits[C].The National Meeting of the American Institute of Chemical Engineers, Los Angeles, California,1982
[43]庄培栋,沈永良,张连柱,王海孝.加权广义预测控制算法的改进[J].黑龙江大学自然科学学报.2002,Vol.19(2)：33-35.
[44]金晓明,王树青,荣冈.预测函数控制(PFC)—一种新型预测控制策略.化工自动化及仪表,1999,Vol.26(2)：74-80
[45]Andres Vivas, Philippe Poigent, Francois Pierrot. Predictive functional control for parallel robot. Intl. Conference on Intelligent Robots and systems. Las Vegas, Nevada.2003, IEEE,2785-2790
[46]诸静,颜文俊,诸勇等.智能预测控制及其应用[M].杭州：浙江大学出版社,2002.
[47]韩璞,于萍,王国玉,王东风.单元机组负荷系统预测函数控制[J].电工技术学报,2004,Vol.19(10)：47-52
[48]陈垚光.精通MATLAB GUI设计[M].北京：电子工业出版社,2008
[49]李传军C语言与MATLAB接口—编程与实例[M].北京：北京邮电大学出版社,2004
[50]董辰辉,彭雪峰MATLAB 2008全程指南[M].北京：电子工业出版社,2009
[51]罗华飞MATLAB GUI设计学习手记[M].北京：北京航空航天大学出版社,2009