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Event-Triggered Constrained \(H_\infty \) Control Using Concurrent Learning and ADP

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Neural Information Processing (ICONIP 2023)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1962))

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Abstract

In this paper, an optimal control algorithm based on concurrent learning and adaptive dynamic programming for event-triggered constrained \(H_\infty \) control is developed. First, the \(H_\infty \) control system under consideration is based on event-triggered constrained input and time-triggered external disturbance, which saves resources and reduces the network bandwidth burden. Second, in the implementation of the control scheme, a critic neural network is designed to approximate unknown value function. Moreover, concurrent learning techniques participate in weight training, making the implementation process simple and effective. Lastly, the stability of the system and the effectiveness of the algorithm are demonstrated through theorem proofs and simulation results.

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Acknowledgments

This paper was supported in part by the National Natural Science Foundation of China under Grants 62022094, 62073085, 62373375, and the Zhejiang Lab (No. 2021NB0AB01), in part by Scientific Research Fund of Hainan University under Grant KYQD(ZR)23025.

Author information

Authors and Affiliations

  1. School of Information and Communication Engineering, Hainan University, Haikou, 570100, China

    Shan Xue & Dongsheng Guo

  2. School of Automation, Central South University, Changsha, 410083, China

    Biao Luo

  3. School of System Design and Intelligent Manufacturing, Southern University of Science and Technology, Shenzhen, 518055, 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. Dongsheng Guo
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Corresponding author

Correspondence to Biao Luo .

Editor information

Editors and Affiliations

  1. School of Automation, Central South University, Changsha, China

    Biao Luo

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

    Long Cheng

  3. Institute of Cyber-Systems and Control, Zhejiang University, Hangzhou, China

    Zheng-Guang Wu

  4. School of Automation, Guangdong University of Technology, Guangzhou, China

    Hongyi Li

  5. School of Electrical Engineering and Telecommunications, UNSW Sydney, Sydney, NSW, Australia

    Chaojie Li

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Xue, S., Luo, B., Liu, D., Guo, D. (2024). Event-Triggered Constrained \(H_\infty \) Control Using Concurrent Learning and ADP. In: Luo, B., Cheng, L., Wu, ZG., Li, H., Li, C. (eds) Neural Information Processing. ICONIP 2023. Communications in Computer and Information Science, vol 1962. Springer, Singapore. https://doi.org/10.1007/978-981-99-8132-8_26

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  • DOI: https://doi.org/10.1007/978-981-99-8132-8_26

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-99-8131-1

  • Online ISBN: 978-981-99-8132-8

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