Neural network based posture control of a human arm model in the sagittal plane

Authors:
Shan Liu;Yongji Wang;Jian Huang
Affiliations:
Department of Control Science and Engineering, Huazhong University of Science and Technology, Wuhan, Hubei, China;Department of Control Science and Engineering, Huazhong University of Science and Technology, Wuhan, Hubei, China;Department of Control Science and Engineering, Huazhong University of Science and Technology, Wuhan, Hubei, China
Venue:
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part III
Year:
2006

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Abstract

In this paper posture control of a human arm in the sagittal plane is investigated by means of model simulations. The arm is modeled by a nonlinear neuromusculoskeletal model with two degrees of freedom and six muscles. A multilayer perceptron network is used in this paper, and effectively adapted by Levenberg-Marquardt training algorithm. The duration of next movement is regulated according as current feedback states. Simulation Results indicate that this method can maintain two joints at different location in allowable bound. The control scheme provides novel insight into neural prosthesis control and robotic control.