IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v10y2017i11p1728-d116650.html
   My bibliography  Save this article

Controllability and Leader-Based Feedback for Tracking the Synchronization of a Linear-Switched Reluctance Machine Network

Author

Listed:
  • Bo Zhang

    (College of Mechatronics and Control Engineering, Shenzhen University, Shenzhen 518060, China
    Laboratory of Advanced Unmanned Systems Technology, Research Institute of Northwestern Polytechnical University in Shenzhen, Shenzhen 518060, China)

  • Jianping Yuan

    (Laboratory of Advanced Unmanned Systems Technology, Research Institute of Northwestern Polytechnical University in Shenzhen, Shenzhen 518060, China)

  • Jianfei Pan

    (College of Mechatronics and Control Engineering, Shenzhen University, Shenzhen 518060, China)

  • Xiaoyu Wu

    (College of Mechatronics and Control Engineering, Shenzhen University, Shenzhen 518060, China)

  • Jianjun Luo

    (Laboratory of Advanced Unmanned Systems Technology, Research Institute of Northwestern Polytechnical University in Shenzhen, Shenzhen 518060, China)

  • Li Qiu

    (College of Mechatronics and Control Engineering, Shenzhen University, Shenzhen 518060, China)

Abstract

This paper investigates the controllability of a closed-loop tracking synchronization network based on multiple linear-switched reluctance machines (LSRMs). The LSRM network is constructed from a global closed-loop manner, and the closed loop only replies to the input and output information from the leader node. Then, each local LSRM node is modeled as a general second-order system, and the model parameters are derived by the online system identification method based on the least square method. Next, to guarantee the LSRM network’s controllability condition, a theorem is deduced that clarifies the relationship among the LSRM network’s controllability, the graph controllability of the network and the controllability of the node dynamics. A state feedback control strategy with the state observer located on the leader is then proposed to improve the tracking performance of the LSRM network. Last, both the simulation and experiment results prove the effectiveness of the network controller design scheme and the results also verify that the leader-based global feedback strategy not only improves the tracking performance but also enhances the synchronization accuracy of the LSRM network experimentally.

Suggested Citation

  • Bo Zhang & Jianping Yuan & Jianfei Pan & Xiaoyu Wu & Jianjun Luo & Li Qiu, 2017. "Controllability and Leader-Based Feedback for Tracking the Synchronization of a Linear-Switched Reluctance Machine Network," Energies, MDPI, vol. 10(11), pages 1-18, October.
    • Handle: RePEc:gam:jeners:v:10:y:2017:i:11:p:1728-:d:116650
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/10/11/1728/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/10/11/1728/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Bo Zhang & J.F. Pan & Jianping Yuan & Wufeng Rao & Li Qiu & Jianjun Luo & Honghua Dai, 2017. "Tracking Control with Zero Phase-Difference for Linear Switched Reluctance Machines Network," Energies, MDPI, vol. 10(7), pages 1-15, July.
    2. Feng Lu & Yu Ye & Jinquan Huang, 2017. "Gas Turbine Engine Identification Based on a Bank of Self-Tuning Wiener Models Using Fast Kernel Extreme Learning Machine," Energies, MDPI, vol. 10(9), pages 1-17, September.
    3. Yang-Yu Liu & Jean-Jacques Slotine & Albert-László Barabási, 2011. "Controllability of complex networks," Nature, Nature, vol. 473(7346), pages 167-173, May.
    4. Zhengzhong Yuan & Chen Zhao & Zengru Di & Wen-Xu Wang & Ying-Cheng Lai, 2013. "Exact controllability of complex networks," Nature Communications, Nature, vol. 4(1), pages 1-9, December.
    5. Bo Zhang & Jianping Yuan & Jianjun Luo & Xiaoyu Wu & Li Qiu & J.F. Pan, 2017. "Hierarchical Distributed Motion Control for Multiple Linear Switched Reluctance Machines," Energies, MDPI, vol. 10(9), pages 1-15, September.
    6. Zhongwei Deng & Lin Yang & Yishan Cai & Hao Deng, 2016. "Online Identification with Reliability Criterion and State of Charge Estimation Based on a Fuzzy Adaptive Extended Kalman Filter for Lithium-Ion Batteries," Energies, MDPI, vol. 9(6), pages 1-16, June.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Bo Zhang & Jianping Yuan & J. F. Pan & Xiaoyu Wu & Jianjun Luo & Li Qiu, 2017. "Global Feedback Control for Coordinated Linear Switched Reluctance Machines Network with Full-State Observation and Internal Model Compensation," Energies, MDPI, vol. 10(12), pages 1-19, December.
    2. Jordi Garcia-Amorós, 2018. "Linear Hybrid Reluctance Motor with High Density Force," Energies, MDPI, vol. 11(10), pages 1-14, October.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Bo Zhang & Jianping Yuan & J. F. Pan & Xiaoyu Wu & Jianjun Luo & Li Qiu, 2017. "Global Feedback Control for Coordinated Linear Switched Reluctance Machines Network with Full-State Observation and Internal Model Compensation," Energies, MDPI, vol. 10(12), pages 1-19, December.
    2. Yang, Hyeonchae & Jung, Woo-Sung, 2016. "Structural efficiency to manipulate public research institution networks," Technological Forecasting and Social Change, Elsevier, vol. 110(C), pages 21-32.
    3. Tao Jia & Robert F Spivey & Boleslaw Szymanski & Gyorgy Korniss, 2015. "An Analysis of the Matching Hypothesis in Networks," PLOS ONE, Public Library of Science, vol. 10(6), pages 1-12, June.
    4. Hu, Ying & Yu, Yang & Mardani, Abbas, 2021. "Selection of carbon emissions control industries in China: An approach based on complex networks control perspective," Technological Forecasting and Social Change, Elsevier, vol. 172(C).
    5. Li, Sheng & Liu, Wenwen & Wu, Ruizi & Li, Junli, 2023. "An adaptive attack model to network controllability," Reliability Engineering and System Safety, Elsevier, vol. 235(C).
    6. Li, Jian & Dueñas-Osorio, Leonardo & Chen, Changkun & Shi, Congling, 2016. "Connectivity reliability and topological controllability of infrastructure networks: A comparative assessment," Reliability Engineering and System Safety, Elsevier, vol. 156(C), pages 24-33.
    7. Li, Jian & Dueñas-Osorio, Leonardo & Chen, Changkun & Berryhill, Benjamin & Yazdani, Alireza, 2016. "Characterizing the topological and controllability features of U.S. power transmission networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 453(C), pages 84-98.
    8. Ding, Jie & Wen, Changyun & Li, Guoqi, 2017. "Key node selection in minimum-cost control of complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 486(C), pages 251-261.
    9. Li, Meizhu & Zhang, Qi & Deng, Yong, 2018. "Evidential identification of influential nodes in network of networks," Chaos, Solitons & Fractals, Elsevier, vol. 117(C), pages 283-296.
    10. Wei, Liang & Li, Faxu & Zhao, Haixing & Deng, Bo, 2024. "On the minimum driver node set of k-uniform linear hypertree networks," Applied Mathematics and Computation, Elsevier, vol. 474(C).
    11. Li, Xin-Feng & Lu, Zhe-Ming, 2016. "Optimizing the controllability of arbitrary networks with genetic algorithm," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 447(C), pages 422-433.
    12. Liu, Suling & Xu, Qiong & Chen, Aimin & Wang, Pei, 2020. "Structural controllability of dynamic transcriptional regulatory networks for Saccharomyces cerevisiae," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 537(C).
    13. Nie, Sen & Wang, Xuwen & Wang, Binghong, 2015. "Effect of degree correlation on exact controllability of multiplex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 436(C), pages 98-102.
    14. Yang, Qing-Lin & Wang, Li-Fu & Zhao, Guo-Tao & Guo, Ge, 2020. "A coarse graining algorithm based on m-order degree in complex network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 558(C).
    15. Pang, Shaopeng & Hao, Fei, 2017. "Optimizing controllability of edge dynamics in complex networks by perturbing network structure," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 470(C), pages 217-227.
    16. Rinaldi, Marco, 2018. "Controllability of transportation networks," Transportation Research Part B: Methodological, Elsevier, vol. 118(C), pages 381-406.
    17. Sun, Peng Gang & Ma, Xiaoke & Chi, Juan, 2017. "Dominating complex networks by identifying minimum skeletons," Chaos, Solitons & Fractals, Elsevier, vol. 104(C), pages 182-191.
    18. Wang, Pei & Xu, Shuang, 2017. "Spectral coarse grained controllability of complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 478(C), pages 168-176.
    19. Yuanyuan Ma & Lingxuan Li, 2018. "Crisis Spreading Model of the Shareholding Networks of Listed Companies and Their Main Holders and Their Controllability," Complexity, Hindawi, vol. 2018, pages 1-17, December.
    20. Guo, Tianjiao & Tu, Lilan & Guo, Yifei & Hu, Jia & Su, Qingqing, 2023. "Control-capacity analysis and optimized construction for controlled interdependent networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 616(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:10:y:2017:i:11:p:1728-:d:116650. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.