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基于多智能体模型的动车组分布式预测控制

李中奇 杨辉 张坤鹏 付雅婷

李中奇, 杨辉, 张坤鹏, 付雅婷. 基于多智能体模型的动车组分布式预测控制. 自动化学报, 2014, 40(11): 2625-2631. doi: 10.3724/SP.J.1004.2014.02625
引用本文: 李中奇, 杨辉, 张坤鹏, 付雅婷. 基于多智能体模型的动车组分布式预测控制. 自动化学报, 2014, 40(11): 2625-2631. doi: 10.3724/SP.J.1004.2014.02625
LI Zhong-Qi, YANG Hui, ZHANG Kun-Peng, FU Ya-Ting. Distributed Model Predictive Control Based on Multi-agent Model for Electric Multiple Units. ACTA AUTOMATICA SINICA, 2014, 40(11): 2625-2631. doi: 10.3724/SP.J.1004.2014.02625
Citation: LI Zhong-Qi, YANG Hui, ZHANG Kun-Peng, FU Ya-Ting. Distributed Model Predictive Control Based on Multi-agent Model for Electric Multiple Units. ACTA AUTOMATICA SINICA, 2014, 40(11): 2625-2631. doi: 10.3724/SP.J.1004.2014.02625

基于多智能体模型的动车组分布式预测控制


DOI: 10.3724/SP.J.1004.2014.02625
详细信息
  • 基金项目:

    Supported by National Natural Science Foundation of China (61164013, U1334211, 51174091), the Key Program of China Ministry of Railway (2011Z002-D), and Natural Science Foundation of Jiangxi Province (20122BAB201021)

Distributed Model Predictive Control Based on Multi-agent Model for Electric Multiple Units

More Information
  • Fund Project:

    Supported by National Natural Science Foundation of China (61164013, U1334211, 51174091), the Key Program of China Ministry of Railway (2011Z002-D), and Natural Science Foundation of Jiangxi Province (20122BAB201021)

  • 摘要: 以高速铁路普遍采用的动力分散式动车组为研究对象.针对动车组由若干动力单元相互耦合组成的结构特点,以各动力单元为智能体.结合动车组牵引特性曲线和实际运行数据,应用减法聚类和模式分类算法建立各智能体多模型集.依据各智能体网络拓扑结构和相互耦合约束关系,建立动车组多智能体模型.针对各智能体的耦合约束,采用PID和GPC相结合的平稳起动切换控制策略和基于邻域优化的多智能体分布式协调控制算法,实现各智能体对给定速度的同步跟踪.基于CRH380A型动车组运行数据的仿真结果验证了本文方法的有效性.
  • [1] Raghunathan R S, Kim H D, Setoguchi T. Aerodynamics of high-speed railway train. Aerospace Science, 2002, 38(6-7): 469-514
    [2] Zhong Lu-Sheng, Yan Zheng, Yang Hui, Qi Ye-Peng, Zhang Kun-Peng, Fan Xiao-Ping. Predictive control of high-speed train based on data driven subspace approach. Journal of the China Railway Society, 2013, 35(4): 77-83 (in Chinese)
    [3] Yang C D, Sun Y P. Mixed H2/H∞ cruise controller design for high speed train. International Journal of Control, 2001, 74(9): 905-920
    [4] Song Q, Song Y D, Tang T, Ning B. Computationally inexpensive tracking control of high-speed trains with traction/braking saturation. IEEE Transactions on Intelligent Transportation Systems, 2011, 12(4): 1116-1125
    [5] Song Q, Song Y D. Data-based fault-tolerant control of high-speed trains with traction/braking notch nonlinearities and actuator failures. IEEE Transactions on Neural Networks, 2011, 22(12): 2250-2261
    [6] Yang Hui, Zhang Kun-Peng, Wang Xin, Zhong Lu-Sheng. Generalized multiple-model predictive control method of high-speed train. Journal of the China Railway Society, 2011, 33(8): 80-87 (in Chinese)
    [7] Cai Xing, Xie Lei, Su Hong-Ye, Gu Yong. Distributed model predictive control based on cascade processes. Acta Automatica Sinica, 2013, 39(5): 510-518 (in Chinese)
    [8] Liu J F, Chen X Z, De la Peña D M, Christofides P D. Iterative distributed model predictive control of nonlinear systems: handling asynchronous, delayed measurements. IEEE Transactions on Automatic Control, 2012, 57(2): 528-534
    [9] Galbusera L, Ferrari T G, Scattolini R. A hybrid model predictive control scheme for containment and distributed sensing in multi-agent systems. Systems Control Letters, 2013, 62(5): 413-419
    [10] Du Xiao-Ning, Xi Yu-Geng, Li Shao-Yuan. Distributed optimization algorithm for predicteve control. Control Theory and Applications, 2002, 19(5): 793-796 (in Chinese)
    [11] Chen Qing, Li Shao-Yuan, Xi Yu-Geng. Distributed decoupling predictive control of a kind of cascade processes. Control and Decision, 2004, 19(6): 647-650 (in Chinese)
    [12] Zheng Y, Li S Y, Li N. Distributed model prdictive control over network information exchange for large-scale systems. Control Engineering Practice, 2011, 19(5): 757-769
    [13] Zheng Y, Li S Y, Qiu H. Networked coordination-based distributed model predictive control for large-scale system. IEEE Transactions on Control Systems Technology, 2013, 21(3): 991-998
    [14] Vaccarini M, Longhi S, Katebi M R. Unconstrained networked decentralized model predictive control. Journal of Process Control, 2009, 19(2): 328-339
    [15] Liu Yu-Bo, Luo Xiong-Lin, Xu Feng. Global coordination and stability analysis for distributed model predictive control system. Journal of Chemical Industry and Engineering, 2013, 64(4): 1318-1331 (in Chinese)
    [16] Van Antwerp J G, Braatz R D. Model predictive control of large scale processes. Journal of Process Control, 2000, 10(1): 1-8
    [17] Narendra K S, Han Z. The changing face of adaptive control: the use of multiple models. Annual Reviews in Control, 2011, 35(1): 1-12
    [18] Huang Miao, Wang Xin, Wang Zhen-Lei. Multiple models adaptive control based on time series for a class of nonlinear systems. Acta Automatica Sinica, 2013, 39(5): 581-586 (in Chinese)
    [19] Liu Lin-Lin, Zhou Li-Fang, Ji Ting, Zhao Yu-Hong. Research on model switching method of multi-hierarchical model predictive control systems. Acta Automatica Sinica, 2013, 39(5): 626-630 (in Chinese)
    [20] Torun Y, Tohumoglu G. Designing simulated annealing and subtractive clustering based fuzzy classifier. Applied Soft Computing, 2011, 2(1): 2193-2201
    [21] Afsari F, Eftekhari M, Eslami E, Woo P Y. Interpretability-based fuzzy decision tree classifier a hybrid of the subtractive clustering and the multi-objective evolutionary algorithm. Soft Computing, 2013, 17(9): 1673-1686
    [22] Sun Ming-Xuan, Bi Hong-Bo. Learning identification: least squares algorithms and their repetitive consistency. Acta Automatica Sinica, 2012, 38(5): 698-706 (in Chinese)
    [23] Jiang Jing. Traction system parameter matching design and research of new-generation high-speed EMUs. Electric Drive for Locomotives, 2011, 52(3): 9-12 (in Chinese)
    [24] Zhang Shu-Guang. CTCS-3 Train Control System General Technology Scheme. Beijing: China Railway Publishing House, 2008. 9-12 (in Chinese)
    [25] Li Rui-Chun. Research on high-speed train and raising speed train vehicle hook buffer mechanism. Railway Locomotive and Car, 2013, 24(6): 15-21 (in Chinese)
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    [17] 李柠, 李少远, 席裕庚. MIMO系统的多模型预测控制[J]. 自动化学报, 2003, 29(4): 516-523.
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出版历程
  • 收稿日期:  2013-06-17
  • 修回日期:  2013-11-12
  • 刊出日期:  2014-11-20

基于多智能体模型的动车组分布式预测控制

doi: 10.3724/SP.J.1004.2014.02625
    基金项目:

    Supported by National Natural Science Foundation of China (61164013, U1334211, 51174091), the Key Program of China Ministry of Railway (2011Z002-D), and Natural Science Foundation of Jiangxi Province (20122BAB201021)

摘要: 以高速铁路普遍采用的动力分散式动车组为研究对象.针对动车组由若干动力单元相互耦合组成的结构特点,以各动力单元为智能体.结合动车组牵引特性曲线和实际运行数据,应用减法聚类和模式分类算法建立各智能体多模型集.依据各智能体网络拓扑结构和相互耦合约束关系,建立动车组多智能体模型.针对各智能体的耦合约束,采用PID和GPC相结合的平稳起动切换控制策略和基于邻域优化的多智能体分布式协调控制算法,实现各智能体对给定速度的同步跟踪.基于CRH380A型动车组运行数据的仿真结果验证了本文方法的有效性.

English Abstract

李中奇, 杨辉, 张坤鹏, 付雅婷. 基于多智能体模型的动车组分布式预测控制. 自动化学报, 2014, 40(11): 2625-2631. doi: 10.3724/SP.J.1004.2014.02625
引用本文: 李中奇, 杨辉, 张坤鹏, 付雅婷. 基于多智能体模型的动车组分布式预测控制. 自动化学报, 2014, 40(11): 2625-2631. doi: 10.3724/SP.J.1004.2014.02625
LI Zhong-Qi, YANG Hui, ZHANG Kun-Peng, FU Ya-Ting. Distributed Model Predictive Control Based on Multi-agent Model for Electric Multiple Units. ACTA AUTOMATICA SINICA, 2014, 40(11): 2625-2631. doi: 10.3724/SP.J.1004.2014.02625
Citation: LI Zhong-Qi, YANG Hui, ZHANG Kun-Peng, FU Ya-Ting. Distributed Model Predictive Control Based on Multi-agent Model for Electric Multiple Units. ACTA AUTOMATICA SINICA, 2014, 40(11): 2625-2631. doi: 10.3724/SP.J.1004.2014.02625
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