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Memory-based Human Postural Regulation Control: An Asynchronous Semi-Markov Model Approach

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  • Robot and Applications
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Abstract

This article investigates the human postural regulation problem from the dynamical system perspective, which is also applicable for human-like robotics. More precisely, since the dynamical human posture parameters may change caused by varying load or environment abrupt, the semi-Markov jump process is employed to model the human standing postural dynamics with multiple modes. Furthermore, a novel memorized regulation strategy is developed for guaranteeing the stable standing such that the past memory information can be well utilized. In particular, the asynchronous regulation procedure is considered for better describing the human postural model with mismatched jumping modes. By model transformation and stochastic analysis, mode-dependent regulation criteria with state feedback model are established by convex optimization approach, based on which the mode-dependent regulation gains are designed accordingly. Finally, the feasibility and effectiveness of our proposed regulation strategy is verified via an illustrative example of quiet upright standing posture.

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Authors and Affiliations

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

    Chao Ma & Hang Fu

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

    Wei Wu

Authors
  1. Chao Ma
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  2. Hang Fu
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Correspondence to Wei Wu.

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Chao Ma received his B.S. degree in automation from Central South University, Changsha, China, in 2007, his M.S. and Ph.D. degrees in control science and engineering from the Harbin Institute of Technology, Harbin, China, in 2010 and 2015, respectively. Currently he is an associate professor at the School of Mechanical Engineering, University of Science and Technology Beijing, China. His current research interests include intelligent robot systems, intelligent agents, and robot control.

Hang Fu received his B.E. and M.S. degrees in mechanical engineering in 2017 and 2020, respectively, from University of Science and Technology Beijing, Beijing, China. He is currently working toward a Ph.D. degree in mechanical engineering in University of Science and Technology Beijing, China. His research interests include hybrid systems, Markov jump systems, switched systems, and robotic systems.

Wei Wu received his B.Sc. degree in physics and an M.Sc. degree in theoretical physics from Beijing Normal University, Beijing, China, in 2001 and 2004, respectively, and a Ph.D. degree in computational neuroscience from Johann Wolfgang Goethe University, Frankfurt, Germany, in 2008. He is currently an Associate Professor with the State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing. His research interests include artificial intelligence and complex networks.

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Ma, C., Fu, H. & Wu, W. Memory-based Human Postural Regulation Control: An Asynchronous Semi-Markov Model Approach. Int. J. Control Autom. Syst. 21, 3357–3367 (2023). https://doi.org/10.1007/s12555-022-0661-z

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  • DOI: https://doi.org/10.1007/s12555-022-0661-z

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