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Neural Dynamic Programming for Event-Based Nonlinear Adaptive Robust Stabilization

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Neural Information Processing (ICONIP 2016)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 9947))

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Abstract

In this paper, we develop an event-based adaptive robust stabilization method for continuous-time nonlinear systems with uncertain terms via a self-learning technique called neural dynamic programming. Through system transformation, it is proven that the robustness of the uncertain system can be achieved by designing an event-triggered optimal controller with respect to the nominal system under a suitable triggering condition. Then, the idea of neural dynamic programming is adopted to perform the main controller design task by building and training a critic network. Finally, the effectiveness of the present adaptive robust control strategy is illustrated via a simulation example.

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Acknowledgment

This work was supported in part by the National Natural Science Foundation of China under Grants 61233001, 61273140, 61304086, 61402260, 61533017, and U1501251, in part by Beijing Natural Science Foundation under Grant 4162065, in part by Shandong Province Higher Educational Science and Technology Program (J13LN42), in part by The Excellent Young and Middle-Aged Scientist Award Foundation of Shandong Province (BS2013DX043), and in part by the Early Career Development Award of SKLMCCS.

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Authors and Affiliations

  1. The State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China

    Ding Wang & Hongwen Ma

  2. School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing, 100083, China

    Derong Liu

  3. School of Management Science and Engineering, Shandong University of Finance and Economics, Jinan, 250014, China

    Huidong Wang

Authors
  1. Ding Wang
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  2. Hongwen Ma
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  3. Derong Liu
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  4. Huidong Wang
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Corresponding author

Correspondence to Ding Wang .

Editor information

Editors and Affiliations

  1. The University of Tokyo, Tokyo, Japan

    Akira Hirose

  2. Kobe University, Kobe, Japan

    Seiichi Ozawa

  3. Okinawa Institute of Science and Technology Graduate University, Onna, Japan

    Kenji Doya

  4. Nara Institute of Science and Technology, Ikoma, Japan

    Kazushi Ikeda

  5. Kyungpook National University, Daegu, Korea (Republic of)

    Minho Lee

  6. Chinese Academy of Sciences, Beijing, China

    Derong Liu

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© 2016 Springer International Publishing AG

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Wang, D., Ma, H., Liu, D., Wang, H. (2016). Neural Dynamic Programming for Event-Based Nonlinear Adaptive Robust Stabilization. In: Hirose, A., Ozawa, S., Doya, K., Ikeda, K., Lee, M., Liu, D. (eds) Neural Information Processing. ICONIP 2016. Lecture Notes in Computer Science(), vol 9947. Springer, Cham. https://doi.org/10.1007/978-3-319-46687-3_16

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  • DOI: https://doi.org/10.1007/978-3-319-46687-3_16

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-46686-6

  • Online ISBN: 978-3-319-46687-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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