基于状态估计的高速受电弓鲁棒预测控制

doi:10.19595/j.cnki.1000-6753.tces.200047

Abstract
Figure/Table
References
Related Citation (15)

Download: PDF (1324 KB) HTML (1 KB)
Export: BibTeX | EndNote (RIS)

Abstract Pantograph-catenary system plays an important role in the process of obtaining electric energy for high-speed trains. The continuous and stable contact state between pantograph and catenary is the key factor to ensure high-quality power acquisition for high-speed trains. With the increase of train speed, the vibration between pantograph and catenary becomes more severe, and the current collection quality becomes worse. In order to reduce the vibration of pantograph and improve the current collection quality, a robust predictive control strategy based on state estimation was proposed. Firstly, in order to obtain the pantograph state in complex electromagnetic environment, a method of state estimation of pantograph based on robust Kalman filter was proposed. Then, The integral of contact force tracking error was extended to the state space equation of pantograph catenary system, and a robust predictive controller was designed based on the control variables and output variables constraints. Finally, in the nonlinear pantograph catenary model, the effectiveness and robustness of the controller under different working conditions were verified. The simulation results show that the proposed state estimation method can accurately obtain the pantograph state, and the controller can reduce the standard deviation of pantograph catenary contact force and improve the pantograph catenary current collection quality of high-speed trains when the pantograph parameters are perturbed.

Key words： High-speed pantograph missing measurement state estimation robust predictive control

Received: 15 January 2020

PACS:

TM571

	Service

	E-mail this article
	Add to my bookshelf
	Add to citation manager
	E-mail Alert
	RSS
	Articles by authors
	Zhang Jing
	Song Baolin
	Xie Songlin
	Zhang Hantao
	Liu Zhigang

Cite this article:

Zhang Jing,Song Baolin,Xie Songlin等. Robust Predictive Control of High-Speed Pantograph Based on State Estimation[J]. Transactions of China Electrotechnical Society, 2021, 36(5): 1075-1083.

URL:

https://dgjsxb.ces-transaction.com/EN/10.19595/j.cnki.1000-6753.tces.200047 OR https://dgjsxb.ces-transaction.com/EN/Y2021/V36/I5/1075

[1] Wang Hongrui, Liu Zhigang, Song Yang, et al.Detection of contact wire irregularities using a quadratic time-frequency representation of the pantograph-catenary contact force[J]. IEEE Transactions on Instrumentation and Measurement, 2016, 65(6): 1385-1397.
[2] Lee H, Park W, Oh K, et al.Analysis of dynamic interaction between catenary and pantograph with experimental verification and performance evaluation in new high-speed line[J]. Vehicle System Dynamics, 2015, 53(8): 1117-1134.
[3] 韩志伟, 刘志刚, 张桂南, 等. 非接触式弓网图像检测技术研究综述[J]. 铁道学报, 2013, 35(6): 40-47.
Han Zhiwei, Liu Zhigang, Zhang Guinan, et al.Overview of non-contact image detection technology for pantograph-catenary monitoring[J]. Journal of the China Railway Society, 2013, 35(6): 40-47.
[4] Liu Zhendong, Jönsson P A, Stichel S, et al.Implications of the operation of multiple pantographs on the soft catenary systems in Sweden[J]. Proceedings of the Institution of Mechanical Engineers, Part F: Journal of Rail and Rapid Transit, 2016, 230(3): 971-983.
[5] 时光, 陈忠华, 郭凤仪, 等. 波动载荷下弓网接触电阻特性及建模研究[J]. 电工技术学报, 2019, 34(11): 2287-2295.
Shi Guang, Chen Zhonghua, Guo Fengyi, et al.Research on characteristic of the contact resistance of pantograph-catenary under load fluctuation[J]. Transactions of China Electrotechnical Society, 2019, 34(11): 2287-2295.
[6] Zhang Jing, Liu Zhigang, Song Yang, et al.Study on multidisciplinary integrated design of high-speed pantograph based on parameters design, dynamics analysis and optimal control[C]//35th Chinese Control Conference(CCC), Chengdu, China, 2016: 10253-10257.
[7] 鲁小兵, 刘志刚. 高速铁路受电弓主动控制算法适用性研究[J]. 西南交通大学学报, 2015, 50(2): 233-240.
Lu Xiaobing, Liu Zhigang.Applicability of active control algorithms for pantographs of high-speed railway[J]. Journal of Southwest Jiaotong University, 2015, 50(2): 233-240.
[8] 时光, 陈忠华, 刘健辰, 等. 基于伺服模型的受电弓LQR控制[J]. 电子测量与仪器学报, 2016, 30(7): 1120-1126.
Shi Guang, Chen Zhonghua, Liu Jiancheng, et al.Research of LQR control of pantograph based on servo model[J]. Journal of Electronic Measurement and Instrumentation, 2016, 30(7): 1120-1126.
[9] 庄哲, 施莹, 彭飞, 等. 受电弓状态反馈线性化主动控制方法研究[J]. 机械科学与技术, 2017, 36(10): 1604-1610.
Zhuang Zhe, Shi Ying, Peng Fei, et al.Research on active control method of state feedback linearization for pantograph[J]. Mechanical Science and Technology for Aerospace Engineering, 2017, 36(10): 1604-1610.
[10] Lin Y, Shieh N, Liu V.Optimal control for rail vehicle pantograph systems with actuator delays[J]. IET Control Theory and Applications, 2015, 9(13): 1917-1926.
[11] 任志玲, 林冬, 王月, 等. 基于预测控制的弓网系统半主动控制策略研究[J]. 系统仿真学报, 2017, 29(5): 1086-1092.
Ren Zhiling, Lin Dong, Wang Yue, et al.Research on semi-active control strategy of high speed railway pantograph-catenary system based on model predictive control[J]. Journal of System Simulation, 2017, 29(5): 1086-1092.
[12] 时光, 陈忠华, 郭凤仪, 等. 基于最优载荷的受电弓自适应终端滑模控制[J]. 电工技术学报, 2017, 32(4): 140-146, 153.
Shi Guang, Chen Zhonghua, Guo Fengyi, et al.Adaptive terminal sliding mode control of pantograph based on optimal load[J]. Transactions of China Electrotechnical Society, 2017, 32(4): 140-146, 153.
[13] 时光, 陈忠华, 郭凤仪, 等. 弓网接触力反馈线性化控制[J]. 控制理论与应用, 2016, 33(1): 85-91.
Shi Guang, Chen Zhonghua, Guo Fengyi, et al.Feedback linearization control of load between pantograph and catenary[J]. Control Theory & Applications, 2016, 33(1): 85-91.
[14] Song Yang, Liu Zhigang, Wang Hongrui, et al.Nonlinear modelling of high-speed catenary based on analytical expressions of cable and truss elements[J]. Vehicle System Dynamics, 2015, 53(10): 1455-1479.
[15] 崔营波. 高速列车受电弓半主动控制方法研究及应用[D]. 北京: 北京交通大学, 2018.
[16] 黄珏, 颜冰, 徐朋豪. 带有量测数据丢失的虚观测鲁棒卡尔曼滤波算法[J]. 华中科技大学学报(自然科学版), 2016, 44(9): 55-58.
Huang Yu, Yan Bing, Xu Penghao.Virtual observation robust Kalman filter algorithm with missing measurement[J]. Journal of Huazhong University of Science and Technology(Natural Science Edition), 2016, 44(9): 55-58.
[17] 刘芳, 马杰, 苏卫星, 等. 基于自适应回归扩展卡尔曼滤波的电动汽车动力电池全生命周期的荷电状态估算方法[J]. 电工技术学报, 2020, 35(4): 698-707.
Liu Fang, Ma Jie, Su Weixing, et al.State of charge estimation method of electric vehicle power battery life cycle based on auto regress extended Kalman filter[J]. Transactions of China Electrotechnical Society, 2020, 35(4): 698-707.
[18] 李扬, 李京, 陈亮, 等. 复杂噪声条件下基于抗差容积卡尔曼滤波的发电机动态状态估计[J]. 电工技术学报, 2019, 34(17): 3651-3660.
Li Yang, Li Jing, Chen Liang, et al.Dynamic state estimation of synchronous machines based on robust cubature Kalman filter under complex measurement noise conditions[J]. Transactions of China Electrotechnical Society, 2019, 34(17): 3651-3660.
[19] 谷苗, 夏超英, 田聪颖. 基于综合型卡尔曼滤波的锂离子电池荷电状态估算[J]. 电工技术学报, 2019, 34(2): 419-426.
Gu Miao, Xia Chaoying, Tian Congying.Li-ion battery state of charge estimation based on comprehensive Kalman filter[J]. Transactions of China Electrotechnical Society, 2019, 34(2): 419-426.
[20] 王大方, 李琪, 张鹏, 等. 带有相电压补偿基于EKF的无传感器感应电机转速估计[J]. 电机与控制学报, 2019, 23(1): 35-44.
Wang Dafang, Li Qi, Zhang Peng, et al.Speed estimation method based on extended Kalman filter with phase voltage compensation for sensorless ACIM drives[J]. Electric Machines and Control, 2019, 23(1): 35-44.
[21] Yin Zhonggang, Gao Fengtao, Zhang Yanqing, et al.A review of nonlinear kalman filter appling to sensorless control for AC motor drives[J]. CES Transactions on Electrical Machines and Systems, 2019, 3(4): 351-362.
[22] 沈锋, 徐广辉, 桑靖. 一种自适应变分贝叶斯容积卡尔曼滤波方法[J]. 电机与控制学报, 2015, 19(4): 94-99.
Shen Feng, Xu Guanghui, Sang Jing.Adaptive variational Bayesian cubature Kalman filtering[J]. Electric Machines and Control, 2015, 19(4): 94-99.
[23] 李伟, 张勇军, 肖雄. 实时电感辨识的模型预测并网逆变器控制方法[J]. 电工技术学报, 2018, 33(15): 3450-3460.
Li Wei, Zhang Yongjun, Xiao Xiong.The model predictive grid-connected inverter control method based on real-time inductance identification[J]. Transactions of China Electrotechnical Society, 2018, 33(15): 3450-3460.
[24] Minouchehr N, Hosseini-Sani S K, Vaezi N, et al. LMI-based robust constrained model predictive control of two-wheeled inverted pendulum[C]// Second International Congress on Technology, Communication and Knowledge, Mashhad, Iran, 2015, DOI: 10.1109/ICTCK.2015.7582658.