NDLI: Robust adaptive dynamic programming for sensorimotor control with signal-dependent noise

Content Provider	IEEE Xplore Digital Library
Author	Yu Jiang Zhong-Ping Jiang
Copyright Year	2013
Description	Author affiliation: Polytech. Sch. of Eng., Dept. of Electr. & Comput. Eng., New York Univ., New York, NY, USA (Yu Jiang; Zhong-Ping Jiang)
Abstract	As human beings, we coordinate movements and interact with our environment through sensory information and motor adaptation in our daily lives. Many characteristics of these interactions can be studied using optimization-based models, which assume that the precise knowledge of both the sensorimotor system and its interacting environment is available for the central nervous system (CNS). However, when static and dynamic uncertainties are present, the previously developed optimization models may fail to explain how the CNS can adapt to the uncertainties and still coordinate the movement. In this paper, we attempt to propose a novel computational mechanism for sensorimotor control from a perspective of robust adaptive dynamic programming (RADP). It is suggested that, instead of identifying the system dynamics of both the motor system and the environment, the CNS computes iteratively a robust optimal control policy for movement, using the real-time sensory data. With the help of numerical analysis and simulations, it is observed that the proposed model can reproduce movement trajectories which are consistent with experimental data. Consequently, we conjecture that, in order to achieve successful adaptation, this RADP-type mechanism may be used by the CNS of humans to coordinate movements in the presence of static/dynamic arising in the sensorimotor system.
Starting Page	1
Ending Page	6
File Size	434071
Page Count	6
File Format	PDF
ISBN	9781479930074
DOI	10.1109/SPMB.2013.6736781
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-12-07
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Adaptation models Uncertainty Computational modeling Force Optimal control Robustness Trajectory
Content Type	Text
Resource Type	Article

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