IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v10y2017i12p2019-d121379.html
   My bibliography  Save this article

Global Feedback Control for Coordinated Linear Switched Reluctance Machines Network with Full-State Observation and Internal Model Compensation

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)

  • J. F. 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 discusses the tracking coordination of a linear switched reluctance machine (LSRM) network based on a global feedback control strategy with a full-state observation framework. The observer is allocated on the follower instead of the leader to form a leader–follower–observer network, by utilizing the leader as the global feedback tracking controller and the observer as the observation of the full states. The internal model compensator (IMC) is applied to the leader for the improvement of the network performance. The full-state information of the LSRM network is reconfigured by the output of the LSRM where the observer is located to provide necessary feedback information to the leader. Then, the controllability and observability of the leader–follower–observer network with the IMC are inspected, serving as a basis for the design of the global controller with the IMC and full-state observer. Experimentation verifies the effectiveness of the proposed network control scheme and the results demonstrate that both the absolute and the relative accuracy can be simultaneously improved, compared to the LSRM network with only the consensus algorithm and no global feedback mechanism.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:10:y:2017:i:12:p:2019-:d:121379
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/10/12/2019/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/10/12/2019/
    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. 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.
    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. 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.
    Full references (including those not matched with items on IDEAS)

    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 & 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.
    2. Wei, Bo & Liu, Jie & Wei, Daijun & Gao, Cai & Deng, Yong, 2015. "Weighted k-shell decomposition for complex networks based on potential edge weights," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 420(C), pages 277-283.
    3. Andreas Koulouris & Ioannis Katerelos & Theodore Tsekeris, 2013. "Multi-Equilibria Regulation Agent-Based Model of Opinion Dynamics in Social Networks," Interdisciplinary Description of Complex Systems - scientific journal, Croatian Interdisciplinary Society Provider Homepage: http://indecs.eu, vol. 11(1), pages 51-70.
    4. He, He & Yang, Bo & Hu, Xiaoming, 2016. "Exploring community structure in networks by consensus dynamics," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 450(C), pages 342-353.
    5. Ellinas, Christos & Allan, Neil & Johansson, Anders, 2016. "Project systemic risk: Application examples of a network model," International Journal of Production Economics, Elsevier, vol. 182(C), pages 50-62.
    6. 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.
    7. Meng, Tao & Duan, Gaopeng & Li, Aming & Wang, Long, 2023. "Control energy scaling for target control of complex networks," Chaos, Solitons & Fractals, Elsevier, vol. 167(C).
    8. Yan Zhang & Antonios Garas & Frank Schweitzer, 2019. "Control Contribution Identifies Top Driver Nodes In Complex Networks," Advances in Complex Systems (ACS), World Scientific Publishing Co. Pte. Ltd., vol. 22(07n08), pages 1-15, December.
    9. 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.
    10. Yang, Xu-Hua & Lou, Shun-Li & Chen, Guang & Chen, Sheng-Yong & Huang, Wei, 2013. "Scale-free networks via attaching to random neighbors," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(17), pages 3531-3536.
    11. Zhang, Rui & Wang, Xiaomeng & Cheng, Ming & Jia, Tao, 2019. "The evolution of network controllability in growing networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 520(C), pages 257-266.
    12. Wouter Vermeer & Otto Koppius & Peter Vervest, 2018. "The Radiation-Transmission-Reception (RTR) model of propagation: Implications for the effectiveness of network interventions," PLOS ONE, Public Library of Science, vol. 13(12), pages 1-21, December.
    13. Neil Johnson & Guannan Zhao & Eric Hunsader & Jing Meng & Amith Ravindar & Spencer Carran & Brian Tivnan, 2012. "Financial black swans driven by ultrafast machine ecology," Papers 1202.1448, arXiv.org.
    14. Chen, Shi-Ming & Xu, Yun-Fei & Nie, Sen, 2017. "Robustness of network controllability in cascading failure," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 471(C), pages 536-539.
    15. Xizhe Zhang & Huaizhen Wang & Tianyang Lv, 2017. "Efficient target control of complex networks based on preferential matching," PLOS ONE, Public Library of Science, vol. 12(4), pages 1-10, April.
    16. Badhwar, Rahul & Bagler, Ganesh, 2017. "A distance constrained synaptic plasticity model of C. elegans neuronal network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 469(C), pages 313-322.
    17. Lazaro M Sanchez-Rodriguez & Yasser Iturria-Medina & Erica A Baines & Sabela C Mallo & Mehdy Dousty & Roberto C Sotero & on behalf of The Alzheimer’s Disease Neuroimaging Initiative, 2018. "Design of optimal nonlinear network controllers for Alzheimer's disease," PLOS Computational Biology, Public Library of Science, vol. 14(5), pages 1-24, May.
    18. Pang, Shao-Peng & Hao, Fei, 2018. "Effect of interaction strength on robustness of controlling edge dynamics in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 497(C), pages 246-257.
    19. Christos Ellinas & Neil Allan & Anders Johansson, 2016. "Exploring Structural Patterns Across Evolved and Designed Systems: A Network Perspective," Systems Engineering, John Wiley & Sons, vol. 19(3), pages 179-192, May.
    20. Hiroyasu Inoue, 2015. "Analyses of Aggregate Fluctuations of Firm Network Based on the Self-Organized Criticality Model," Papers 1512.05066, arXiv.org, revised Apr 2016.

    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:12:p:2019-:d:121379. 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.