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Adaptive Dynamic Programming for Human Postural Balance Control

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Neural Information Processing (ICONIP 2017)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10637))

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Abstract

This paper provides a basis for studying human postural balance control about upright stance using adaptive dynamic programming (ADP) theory. Previous models of human sensorimotor control rely on a priori knowledge of system dynamics. Here, we provide an alternative framework based on the ADP theory. The main advantage of this new framework is that the system model is no longer required, and an adaptive optimal controller is obtained directly from input and state data. We apply this theory to simulate human balance behavior, and the obtained results are consistent with the experiment data presented in the past literature.

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Authors and Affiliations

  1. Department of Electrical and Computer Engineering, Tandon School of Engineering, New York University, 5 Metrotech Center, Brooklyn, NY, 11201, USA

    Eric Mauro, Tao Bian & Zhong-Ping Jiang

Authors
  1. Eric Mauro
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  2. Tao Bian
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  3. Zhong-Ping Jiang
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Corresponding author

Correspondence to Eric Mauro .

Editor information

Editors and Affiliations

  1. Guangdong University of Technology, Guangzhou, China

    Derong Liu

  2. Guangdong University of Technology, Guangzhou, China

    Shengli Xie

  3. South China University of Technology, Guangzhou, China

    Yuanqing Li

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

    Dongbin Zhao

  5. King Fahd University of Petroleum and Minerals, Dhahran, Saudi Arabia

    El-Sayed M. El-Alfy

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Mauro, E., Bian, T., Jiang, ZP. (2017). Adaptive Dynamic Programming for Human Postural Balance Control. In: Liu, D., Xie, S., Li, Y., Zhao, D., El-Alfy, ES. (eds) Neural Information Processing. ICONIP 2017. Lecture Notes in Computer Science(), vol 10637. Springer, Cham. https://doi.org/10.1007/978-3-319-70093-9_26

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  • DOI: https://doi.org/10.1007/978-3-319-70093-9_26

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-70092-2

  • Online ISBN: 978-3-319-70093-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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