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International Symposium on Neural Networks

ISNN 2015: Advances in Neural Networks – ISNN 2015 pp 3–10Cite as

A Novel T-S Fuzzy Model Based Adaptive Synchronization Control Scheme for Nonlinear Large-Scale Systems with Uncertainties and Time-Delay

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 9377))

Abstract

In this paper, a novel T-S fuzzy model based adaptive synchronization scheme for nonlinear large-scale systems with uncertainties and time-delay is proposed. Based on the universal approximation property of T-S fuzzy model, a nonlinear large-scale system is established and fuzzy adaptive controllers are designed under Parallel Distributed Compensation (PDC) for overcoming the unknown uncertainties in systems and the time-delay in communication. Furthermore, under some certain condition, this synchronization scheme can be transformed into pinning synchronization control, which will indeed save much resource. Finally, a numerical simulation example is taken to show the effectiveness of the proposed adaptive synchronization scheme.

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Author information

Authors and Affiliations

  1. College of Information Science and Engineering, Northeastern University, Shenyang, China, 110819

    He Jiang & Dongsheng Yang

Authors
  1. He Jiang
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  2. Dongsheng Yang
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Editor information

Editors and Affiliations

  1. Department of Computer Science and Technology, Tsinghua University, Beijing, China

    Xiaolin Hu

  2. Fuzhou University, Fuzhou, China

    Yousheng Xia

  3. School of Information Science and Technology, Sun Yat-sen University, Guangzhou, China

    Yunong Zhang

  4. Chinese Academy of Sciences, Institute of Automation, Beijing, China

    Dongbin Zhao

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© 2015 Springer International Publishing Switzerland

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Jiang, H., Yang, D. (2015). A Novel T-S Fuzzy Model Based Adaptive Synchronization Control Scheme for Nonlinear Large-Scale Systems with Uncertainties and Time-Delay. In: Hu, X., Xia, Y., Zhang, Y., Zhao, D. (eds) Advances in Neural Networks – ISNN 2015. ISNN 2015. Lecture Notes in Computer Science(), vol 9377. Springer, Cham. https://doi.org/10.1007/978-3-319-25393-0_1

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  • DOI: https://doi.org/10.1007/978-3-319-25393-0_1

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-25392-3

  • Online ISBN: 978-3-319-25393-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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