列车驾驶容错控制技术研究
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摘要
列车全自动驾驶技术凭借其安全、灵活、经济以及高服务质量的特点正成为未来城市轨道交通列车驾驶控制技术发展的方向。列车自动驾驶系统的一个重要性能指标是精确停车,该性能指标要求自动驾驶精确控制算法能够确保列车在任何条件下都能实现精确停车,该功能的实现不仅方便了旅客上下车,同时也保证了行车效率,因此该算法的研究有其特有的现实意义。另外,列车经历长时间连续运行,其载荷条件和运行环境会随时间变化,这使得列车出现故障的可能性较高。随着控制系统变得越来越复杂,而实际的应用对系统的可靠性和可维护性的要求越来越高,也需要它们具备检错和容错的能力,以便在较长一段时间维持正常的运作。因此开展对列车驾驶故障检测和容错控制技术的研究具有重大和深远的意义。
本文基于列车运行控制的实际应用需求,介绍了众多学者在故障检测和容错控制领域所进行了持续深入的研究及所取得了重要的成就。这些成就为研究具有检错和容错能力的列车自动驾驶控制算法奠定了理论基础。
本文基于已提出的城市轨道列车制动模型,采用U1O算法对列车的传感器故障进行监测,通过阈值判断大小的方法来判断列车传感器是否发生故障,解决了在列车测量条件复杂的环境下的故障诊断问题。
本文研究了列车运行过程中的性能监测的问题,采用检测滤波器的方法对列车的制动率变化进行了实时监测,同时还尝试了自适应控制参数估计和IMM模型估计的设计方法,并给出相应的对比分析结果。
基于上面所描述的模块提供的检测信息,本文最后提出了基于模型参考滑模控制的列车精确停车容错控制算法。该控制算法根据目标停车点和停车阶段开始前的列车速度,通过参考模型生成目标曲线,解决了目标曲线生成的问题；并通过跟踪目标曲线完成停车。利用变结构控制的强鲁棒性很好地克服列车参数(传输延时、相应时间、制动率)变化对列车运行的影响。同时,该控制算法还可以灵活地根据故障检测的结果快速地调整参考模型以保证列车运行性能维持一致。
实验室仿真试验全面地检验和体现了所提出性能检测与控制算法的有效性,在仿真过程中,即使考虑了许多苛刻的条件,但仿真结果仍然表明所提出的算法非常有效。
理论分析和实验室仿真都表明了本文所提出的算法具有优越性。本文的研究成果对列车全自动驾驶系统的进一步研究具有重要意义。
Because of its characteristics of safety, flexibility, economical efficiency and high-level service, train Fully Automatic Operation (FAO) is becoming the development trend of future urban rail transit system. Accurate stopping is one of the most important performance indexes of train automatic operation system, it require the train vehicle maintain uniform stopping accuracy to ensure the running safety. Therefore, realizing accurate stopping under any circumstance is the center of train fully automatic operation controller research. In addition, train vehicles have to run continuously for long time and their loads as well as external environment change over time. So it is inevitable that the train vehicles would have some faults. Therefore, conducting the research of developing train vehicle operation faults detection and fault-tolerant control technology is highly essential.
Based on the practical requirement of automatic train operation, this thesis briefly describes the theoretical research of Fault Detection and Fault-tolerant Control and points out that these theoretical achievements pave way for the research of train fully automatic operation fault-tolerant controller.
With the available urban rail vehicle dynamic braking system, this thesis first studies the problem of train sensors fault detection and propose a real-time detection algorithm base on the UIO state estimation theory.
This thesis also studies the problem of train braking performance supervision. By comparing with the approach of adaptive control parameter estimation and IMM model estimation, this thesis finally propose a more advanced solution based on the detection filter state estimation theory.
Based on the outcome of train sensors fault detection and train braking performance supervision, the thesis finally addressing the problem of train automatic fault-tolerant operation and propose an accurate control algorithm based on model following sliding mode control algorithm. It is verified that the proposed algorithm can perform online target curve generation and overcome the impact of train parameters drift and module faults.
In order to show the effectiveness of the proposed performance supervision and control algorithms, this thesis conducts series of numerical simulations in laboratory. Even though considering many harsh conditions, the simulation results indicate that the proposed algorithms are highly effective.
Theoretical analysis and verification simulations show the superiority of the proposed onboard controller frame. Therefore, the achievement of this thesis will be of help for the overall research of train fully automatic operation system.
本文基于列车运行控制的实际应用需求,介绍了众多学者在故障检测和容错控制领域所进行了持续深入的研究及所取得了重要的成就。这些成就为研究具有检错和容错能力的列车自动驾驶控制算法奠定了理论基础。
本文基于已提出的城市轨道列车制动模型,采用U1O算法对列车的传感器故障进行监测,通过阈值判断大小的方法来判断列车传感器是否发生故障,解决了在列车测量条件复杂的环境下的故障诊断问题。
本文研究了列车运行过程中的性能监测的问题,采用检测滤波器的方法对列车的制动率变化进行了实时监测,同时还尝试了自适应控制参数估计和IMM模型估计的设计方法,并给出相应的对比分析结果。
基于上面所描述的模块提供的检测信息,本文最后提出了基于模型参考滑模控制的列车精确停车容错控制算法。该控制算法根据目标停车点和停车阶段开始前的列车速度,通过参考模型生成目标曲线,解决了目标曲线生成的问题；并通过跟踪目标曲线完成停车。利用变结构控制的强鲁棒性很好地克服列车参数(传输延时、相应时间、制动率)变化对列车运行的影响。同时,该控制算法还可以灵活地根据故障检测的结果快速地调整参考模型以保证列车运行性能维持一致。
实验室仿真试验全面地检验和体现了所提出性能检测与控制算法的有效性,在仿真过程中,即使考虑了许多苛刻的条件,但仿真结果仍然表明所提出的算法非常有效。
理论分析和实验室仿真都表明了本文所提出的算法具有优越性。本文的研究成果对列车全自动驾驶系统的进一步研究具有重要意义。
Because of its characteristics of safety, flexibility, economical efficiency and high-level service, train Fully Automatic Operation (FAO) is becoming the development trend of future urban rail transit system. Accurate stopping is one of the most important performance indexes of train automatic operation system, it require the train vehicle maintain uniform stopping accuracy to ensure the running safety. Therefore, realizing accurate stopping under any circumstance is the center of train fully automatic operation controller research. In addition, train vehicles have to run continuously for long time and their loads as well as external environment change over time. So it is inevitable that the train vehicles would have some faults. Therefore, conducting the research of developing train vehicle operation faults detection and fault-tolerant control technology is highly essential.
Based on the practical requirement of automatic train operation, this thesis briefly describes the theoretical research of Fault Detection and Fault-tolerant Control and points out that these theoretical achievements pave way for the research of train fully automatic operation fault-tolerant controller.
With the available urban rail vehicle dynamic braking system, this thesis first studies the problem of train sensors fault detection and propose a real-time detection algorithm base on the UIO state estimation theory.
This thesis also studies the problem of train braking performance supervision. By comparing with the approach of adaptive control parameter estimation and IMM model estimation, this thesis finally propose a more advanced solution based on the detection filter state estimation theory.
Based on the outcome of train sensors fault detection and train braking performance supervision, the thesis finally addressing the problem of train automatic fault-tolerant operation and propose an accurate control algorithm based on model following sliding mode control algorithm. It is verified that the proposed algorithm can perform online target curve generation and overcome the impact of train parameters drift and module faults.
In order to show the effectiveness of the proposed performance supervision and control algorithms, this thesis conducts series of numerical simulations in laboratory. Even though considering many harsh conditions, the simulation results indicate that the proposed algorithms are highly effective.
Theoretical analysis and verification simulations show the superiority of the proposed onboard controller frame. Therefore, the achievement of this thesis will be of help for the overall research of train fully automatic operation system.
引文
[1]Yasunohu, S., Miyamoto, S., and H. Ihara, Fuzzy Control for Automatic Train Operation System,4th IFAC/IFIP/IFRS Int. Conf. on Transportation Systems, pp39-45,1983.
[2]Yasunohu, S., Miyamoto, S., et al, Application of Predictive Fuzzy Control to Automatic Train Operation Controller, Proc.of IECON'84, pp657-662,1984.
[3]S Yasunobu, S Miyamoto, Automatic Train Operation System by Predictive Fuzzy Control, Industrial Applications of Fuzzy Control (M. Sugeno ed.), North-Holland, pp1-18,1985.
[4]H Oshima, S Yasunobu, S Sekino, Automatic Train Operation Based on Predictive Fuzzy Control, International Workshop on Artificial Intelligence for Industrial Application,1988.
[5]S Yasunobu, S Miyamoto, H Ihara., A Fuzzy Control for Train Automatic Stop Control. Trans, of the Society of Instrument and Control Engineers,2002,2(1):1-9.
[6]Chang C.S., Xu D.Y., Differential Evolution Based Tuning of Fuzzy Automatic Train Operation for Mass Rapid Transit System, IEE proceedings of Electric Power Application, pp206-212,2000.
[7]Li Hui-chao, Zhai Miao, Feng Xiao-yun. Research and Simulation of a High-performance Algorithm for Subway Train Control, Proceedings of the 7th World Congress on Intelligent Control and Automation, pp4387-4390,2008.
[8]C.M.Cheng, N.W.Rees, A. Chan. Fuzzy Speed Control of A Coal Train, UKACC International Conference on Control, Vol.1, pp525-530,1998.
[9]王卓,王艳辉,贾利民,李平.基于ANFIS的高速列车制动控制仿真研究,铁道学报,Vo1.27,No.3,113-117.
[10]余进,钱清泉,何正友.两级模糊神经网络在高速列车ATO系统中的应用研究,铁道学报,Vol.30,No.50,pp52-56.
[11]孙晓炜,陈永生.基于模糊预测控制策略的ATO仿真,计算机工程与应用,pp214-217.
[12]于振宇,陈德旺.城轨列车制动模型及参数辨识.铁道学报,已接收
[13]罗仁士,王义惠,于振宇,唐涛.城轨列车自适应精确停车控制算法研究,铁道学报,已接收.
[14]王义惠,罗仁士,于振宇,宁滨.考虑ATP限速的ATO控制算法研究,铁道学报,已接收.
[15]Wang Hong, Chai Tian-you, Ding Jin-liang, Brown Martin. "Data Driven Fault Diagnosis and Fault Tolerant Control:Some Advances and Possible New directions", ACTA AUTOMATICA SINICA, Vol.35, No.6, pp739-747,2009.
[16]P.Goel, G.Dedeoglu, S.I.Roumeliotis, G.S.Sukhatme, "Fault Detection and Identification in a mobile Robot", Proc. of 1998 IEEE/RSJ Int.Conf.on Robotics and Automation, pp2302-2307.
[17]M. Hashimoto, H. Kawashima, F. Oba, "Robust Dead Reckoning System with Interacting Multiple Model Based Sensor Fault Diagnosis for Mobile Robot", Proc. Of the fourth IFAC Symp. on Intelligent Autonomous Vehicles, pp157-162.
[18]Masafumi Hashimoto, Hiroyuki Kawashima, Fuminori Oba. A Multi-Model Based Fault Detection and Diagnosis of Internal Sensor for Mobile Robot. Proceedings of the 2003 IEEE/RSJ intl. Conference on Intelligent Robots and Systems, pp3787-3792,2003.
[19]Y. Zhang and X.R.Li, "Detection and Diagnosis of Sensor and Actuator Failures Using IMM Estimator", IEEE Trans, on Aerospace and Electronic Systems,34-4, pp1293-1313.
[20]Yang Zhi-jun, Qi Xiao-hui, Shan Gan-lin. Fault Tolerant Fight Control Law Design with a Model-Following Sliding Mode Controller, International Conference on Measuring Technology and Mechatronics Automation, pp665-668.
[21]Starowiecki M, Yang H, Jiang B. Progressive Accommodation Based on A Modular Approach, SAFEPROCESS 2009, pp259-264.
[22]Maciejowski J, Chu P, Mulder J A, Joosten D. MPC Fault-tolerant Flight Control Case Study:Flight 1862, SAFEPROCESS 2003, pp121-126.
[23]Zhou K, Ren z. A New Controller Architecture For High Performance, Robust and Fault-tolerant Control, IEEE Transaction on Automatic Control, pp1613-1618,2001.
[24]Ganguli S, Marcos A, Balas G. Reconfigurable LPV Control Design For B-747-100/200 Longitual Axis, Proceeding of American Control Conference, pp3612-3617,2002.
[25]Alwi H, Edwards C. Fault Tolerant Control Using Sliding Mode with On-line Control Allocation, Automatica, ppl859-1866,2008.
[26]唐涛,黄良骥.列车自动驾驶系统控制算法综述.铁道学报,2003,25(2)：98-102.
[27]MODONBOARD SUBPROJECT DELIVERABLE REPORT D10-Intelligent Automatic Driver Specification and Simulation Report, http://www.modurban.org/documents.php
[28]Emire Kiyak, Ayse Kahvecioglu and Fikret Caliskan. Aircraft Sensor and Actuator Fault Detection, Isolation, and accommodation, Journal of Aerospace Engineering, ASCE, January2011, pp46-58.
[29]Jiang Jin. Fault tolerant control systems-an introductory overview. Acta Automatica Sinica, 2005,31(1):161-174.
[30]吕劲松.广州地铁车辆制动系统[J],机车电传动,1999,(1)：19-25.
[31]彭俊彬.动车组牵引与制动[M],北京：中国铁道出版社,2007.
[32]Seah, C.E.& Hwang, I., Terminal-Area aircraft tracking using hybrid estimation [J]. Journal of Guidance, Control, and Dynamics,32 (3), pp.836-84,2009.
[33]Hwang, I., Balakrishnan H.& Tomlin C, State estimation for hybrid systems:applications to aircraft tracking [C]. IEE Proceedings of Control Theory Application,153(5), pp.556-566,2006.
[34]Yihui Wang, Renshi Luo, Fang Cao, Bin Ning. Train Tracking Problem Using a Hybrid System Model, proceeding of 12th International Conference on Computer System Design and Operation in the Railway and Other Transit System.
[35]Renshi Luo, Zhenyu Yu, Tao Tang. Braking Performance Supervision of Urban Rail Vehicle,to be presented in 2011 World Congress of Engineering and Technology.
[36]Renshi Luo, Zhenyu Yu, Tao Tang. Accurate Train Stopping by Model Following Sliding Mode Control, to be presented in 13th IEEE Joint International Computer Science and Information Technology Conference. (El indexed)
[37]Hassan K. Khalil. "Nonlinear System, Third Edition". Publishing House of Electronics Industry. ISBN 978-7-121-03704-7.
[38]YOSHIZAWA T. Liapunov's Function and Boundedness of Solutions[J], Funkcialaj Ekvacicj,1958, (2):71-103.
[2]Yasunohu, S., Miyamoto, S., et al, Application of Predictive Fuzzy Control to Automatic Train Operation Controller, Proc.of IECON'84, pp657-662,1984.
[3]S Yasunobu, S Miyamoto, Automatic Train Operation System by Predictive Fuzzy Control, Industrial Applications of Fuzzy Control (M. Sugeno ed.), North-Holland, pp1-18,1985.
[4]H Oshima, S Yasunobu, S Sekino, Automatic Train Operation Based on Predictive Fuzzy Control, International Workshop on Artificial Intelligence for Industrial Application,1988.
[5]S Yasunobu, S Miyamoto, H Ihara., A Fuzzy Control for Train Automatic Stop Control. Trans, of the Society of Instrument and Control Engineers,2002,2(1):1-9.
[6]Chang C.S., Xu D.Y., Differential Evolution Based Tuning of Fuzzy Automatic Train Operation for Mass Rapid Transit System, IEE proceedings of Electric Power Application, pp206-212,2000.
[7]Li Hui-chao, Zhai Miao, Feng Xiao-yun. Research and Simulation of a High-performance Algorithm for Subway Train Control, Proceedings of the 7th World Congress on Intelligent Control and Automation, pp4387-4390,2008.
[8]C.M.Cheng, N.W.Rees, A. Chan. Fuzzy Speed Control of A Coal Train, UKACC International Conference on Control, Vol.1, pp525-530,1998.
[9]王卓,王艳辉,贾利民,李平.基于ANFIS的高速列车制动控制仿真研究,铁道学报,Vo1.27,No.3,113-117.
[10]余进,钱清泉,何正友.两级模糊神经网络在高速列车ATO系统中的应用研究,铁道学报,Vol.30,No.50,pp52-56.
[11]孙晓炜,陈永生.基于模糊预测控制策略的ATO仿真,计算机工程与应用,pp214-217.
[12]于振宇,陈德旺.城轨列车制动模型及参数辨识.铁道学报,已接收
[13]罗仁士,王义惠,于振宇,唐涛.城轨列车自适应精确停车控制算法研究,铁道学报,已接收.
[14]王义惠,罗仁士,于振宇,宁滨.考虑ATP限速的ATO控制算法研究,铁道学报,已接收.
[15]Wang Hong, Chai Tian-you, Ding Jin-liang, Brown Martin. "Data Driven Fault Diagnosis and Fault Tolerant Control:Some Advances and Possible New directions", ACTA AUTOMATICA SINICA, Vol.35, No.6, pp739-747,2009.
[16]P.Goel, G.Dedeoglu, S.I.Roumeliotis, G.S.Sukhatme, "Fault Detection and Identification in a mobile Robot", Proc. of 1998 IEEE/RSJ Int.Conf.on Robotics and Automation, pp2302-2307.
[17]M. Hashimoto, H. Kawashima, F. Oba, "Robust Dead Reckoning System with Interacting Multiple Model Based Sensor Fault Diagnosis for Mobile Robot", Proc. Of the fourth IFAC Symp. on Intelligent Autonomous Vehicles, pp157-162.
[18]Masafumi Hashimoto, Hiroyuki Kawashima, Fuminori Oba. A Multi-Model Based Fault Detection and Diagnosis of Internal Sensor for Mobile Robot. Proceedings of the 2003 IEEE/RSJ intl. Conference on Intelligent Robots and Systems, pp3787-3792,2003.
[19]Y. Zhang and X.R.Li, "Detection and Diagnosis of Sensor and Actuator Failures Using IMM Estimator", IEEE Trans, on Aerospace and Electronic Systems,34-4, pp1293-1313.
[20]Yang Zhi-jun, Qi Xiao-hui, Shan Gan-lin. Fault Tolerant Fight Control Law Design with a Model-Following Sliding Mode Controller, International Conference on Measuring Technology and Mechatronics Automation, pp665-668.
[21]Starowiecki M, Yang H, Jiang B. Progressive Accommodation Based on A Modular Approach, SAFEPROCESS 2009, pp259-264.
[22]Maciejowski J, Chu P, Mulder J A, Joosten D. MPC Fault-tolerant Flight Control Case Study:Flight 1862, SAFEPROCESS 2003, pp121-126.
[23]Zhou K, Ren z. A New Controller Architecture For High Performance, Robust and Fault-tolerant Control, IEEE Transaction on Automatic Control, pp1613-1618,2001.
[24]Ganguli S, Marcos A, Balas G. Reconfigurable LPV Control Design For B-747-100/200 Longitual Axis, Proceeding of American Control Conference, pp3612-3617,2002.
[25]Alwi H, Edwards C. Fault Tolerant Control Using Sliding Mode with On-line Control Allocation, Automatica, ppl859-1866,2008.
[26]唐涛,黄良骥.列车自动驾驶系统控制算法综述.铁道学报,2003,25(2)：98-102.
[27]MODONBOARD SUBPROJECT DELIVERABLE REPORT D10-Intelligent Automatic Driver Specification and Simulation Report, http://www.modurban.org/documents.php
[28]Emire Kiyak, Ayse Kahvecioglu and Fikret Caliskan. Aircraft Sensor and Actuator Fault Detection, Isolation, and accommodation, Journal of Aerospace Engineering, ASCE, January2011, pp46-58.
[29]Jiang Jin. Fault tolerant control systems-an introductory overview. Acta Automatica Sinica, 2005,31(1):161-174.
[30]吕劲松.广州地铁车辆制动系统[J],机车电传动,1999,(1)：19-25.
[31]彭俊彬.动车组牵引与制动[M],北京：中国铁道出版社,2007.
[32]Seah, C.E.& Hwang, I., Terminal-Area aircraft tracking using hybrid estimation [J]. Journal of Guidance, Control, and Dynamics,32 (3), pp.836-84,2009.
[33]Hwang, I., Balakrishnan H.& Tomlin C, State estimation for hybrid systems:applications to aircraft tracking [C]. IEE Proceedings of Control Theory Application,153(5), pp.556-566,2006.
[34]Yihui Wang, Renshi Luo, Fang Cao, Bin Ning. Train Tracking Problem Using a Hybrid System Model, proceeding of 12th International Conference on Computer System Design and Operation in the Railway and Other Transit System.
[35]Renshi Luo, Zhenyu Yu, Tao Tang. Braking Performance Supervision of Urban Rail Vehicle,to be presented in 2011 World Congress of Engineering and Technology.
[36]Renshi Luo, Zhenyu Yu, Tao Tang. Accurate Train Stopping by Model Following Sliding Mode Control, to be presented in 13th IEEE Joint International Computer Science and Information Technology Conference. (El indexed)
[37]Hassan K. Khalil. "Nonlinear System, Third Edition". Publishing House of Electronics Industry. ISBN 978-7-121-03704-7.
[38]YOSHIZAWA T. Liapunov's Function and Boundedness of Solutions[J], Funkcialaj Ekvacicj,1958, (2):71-103.