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Event-Triggered Adaptive Dynamic Programming for Continuous-Time Nonlinear Two-Player Zero-Sum Game

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Book cover Neural Information Processing (ICONIP 2018)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 11307))

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Abstract

In this paper, an event-triggered adaptive dynamic programming (ADP) algorithm is developed to solve the two-player zero-sum game problem of continuous-time nonlinear systems. First, a critic neural network is employed to approximate the optimal value function. Then, an event-triggered ADP method is proposed, which guarantees the stability of the closed-loop system. The developed method can save the amount of computation as the control law and disturbance law that update only when the pre-designed triggering condition is violated. Finally, its effectiveness is verified through simulation results.

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Acknowledgements

This work was supported in part by the National Natural Science Foundation of China under Grants 61873350, 61503377, 61533017 and U1501251.

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Authors and Affiliations

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

    Shan Xue & Yueheng Li

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

    Biao Luo

  3. School of Automation, Guangdong University of Technology, Guangzhou, 510006, China

    Derong Liu

Authors
  1. Shan Xue
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  2. Biao Luo
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  3. Derong Liu
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  4. Yueheng Li
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Corresponding author

Correspondence to Biao Luo .

Editor information

Editors and Affiliations

  1. The Chinese Academy of Sciences, Beijing, China

    Long Cheng

  2. City University of Hong Kong, Kowloon, Hong Kong

    Andrew Chi Sing Leung

  3. Kobe University, Kobe, Japan

    Seiichi Ozawa

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Xue, S., Luo, B., Liu, D., Li, Y. (2018). Event-Triggered Adaptive Dynamic Programming for Continuous-Time Nonlinear Two-Player Zero-Sum Game. In: Cheng, L., Leung, A., Ozawa, S. (eds) Neural Information Processing. ICONIP 2018. Lecture Notes in Computer Science(), vol 11307. Springer, Cham. https://doi.org/10.1007/978-3-030-04239-4_2

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  • DOI: https://doi.org/10.1007/978-3-030-04239-4_2

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-04238-7

  • Online ISBN: 978-3-030-04239-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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