@inproceedings{6b238e2d568b457b82a9848fe3cb8d7d,
title = "Adaptive Dynamic Programming for Human Postural Balance Control",
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.",
keywords = "Adaptive dynamic programming, Motor learning, Optimal control",
author = "Eric Mauro and Tao Bian and Jiang, {Zhong Ping}",
note = "Publisher Copyright: {\textcopyright} 2017, Springer International Publishing AG.; 24th International Conference on Neural Information Processing, ICONIP 2017 ; Conference date: 14-11-2017 Through 18-11-2017",
year = "2017",
doi = "10.1007/978-3-319-70093-9_26",
language = "English (US)",
isbn = "9783319700922",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "249--257",
editor = "Derong Liu and Shengli Xie and Yuanqing Li and El-Alfy, {El-Sayed M.} and Dongbin Zhao",
booktitle = "Neural Information Processing - 24th International Conference, ICONIP 2017, Proceedings",
}