Adaptive Dynamic Programming for Human Postural Balance Control

Eric Mauro, Tao Bian, Zhong Ping Jiang

Research output: Chapter in Book/Report/Conference proceedingConference contribution


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.

Original languageEnglish (US)
Title of host publicationNeural Information Processing - 24th International Conference, ICONIP 2017, Proceedings
EditorsDerong Liu, Shengli Xie, Yuanqing Li, El-Sayed M. El-Alfy, Dongbin Zhao
PublisherSpringer Verlag
Number of pages9
ISBN (Print)9783319700922
StatePublished - 2017
Event24th International Conference on Neural Information Processing, ICONIP 2017 - Guangzhou, China
Duration: Nov 14 2017Nov 18 2017

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10637 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Other24th International Conference on Neural Information Processing, ICONIP 2017


  • Adaptive dynamic programming
  • Motor learning
  • Optimal control

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science


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