An adaptive RBF neural network control strategy for lower limb rehabilitation robot

Authors:
Feng Zhang;Pengfeng Li;Zengguang Hou;Xiaoliang Xie;Yixiong Chen;Qingling Li;Min Tan
Affiliations:
Laboratory of Complex Systems and Intelligence Science, Institute of Automation, the Chinese Academy of Sciences, Beijing, China;Laboratory of Complex Systems and Intelligence Science, Institute of Automation, the Chinese Academy of Sciences, Beijing, China;Laboratory of Complex Systems and Intelligence Science, Institute of Automation, the Chinese Academy of Sciences, Beijing, China;Laboratory of Complex Systems and Intelligence Science, Institute of Automation, the Chinese Academy of Sciences, Beijing, China;Laboratory of Complex Systems and Intelligence Science, Institute of Automation, the Chinese Academy of Sciences, Beijing, China;Laboratory of Complex Systems and Intelligence Science, Institute of Automation, the Chinese Academy of Sciences, Beijing, China;Laboratory of Complex Systems and Intelligence Science, Institute of Automation, the Chinese Academy of Sciences, Beijing, China
Venue:
ICIRA'10 Proceedings of the Third international conference on Intelligent robotics and applications - Volume Part II
Year:
2010

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Abstract

This paper proposed an adaptive control strategy based on RBF (radial basis function) neural network and PD Computed-Torque algorithmfor precise tracking of a predefined trajectory. This control strategy can not only give a small tracking error, but also have a good robustness to themodeling errors of the robot dynamics equation and also to the system friction. With this control algorithm, the robot can work in assist-as-needed mode by detecting the human active joint torque. At last, a simulation result using matlab simulink is given to illustrate the effectiveness of our control strategy.