NDLI: RBF neural network compensation based trajectory tracking control for rehabilitation training robot

Content Provider	IEEE Xplore Digital Library
Author	Gui Yin Xiaodong Zhang Jiangcheng Chen Qiangyong Shi
Copyright Year	2015
Description	Author affiliation: Key Lab. of Educ. Minist. for Modern Design & Rotor-Bearing Syst., Xi'an Jiaotong Univ., Xi'an, China (Gui Yin; Xiaodong Zhang; Jiangcheng Chen; Qiangyong Shi)
Abstract	For rehabilitation robot, how to effectively control the movement of training, which depends on the performance of the robot control system, is very important to improve the quality of rehabilitation. Lower limb rehabilitation robot system is a nonlinear time-varying system, so the real-time calculation and compensation for the nonlinear coupling term is always neccessary, and linear control method can be used to achieve trajectory tracking with a high precision. For this target, a radial basis function (RBF) neural network compensation control method based on computed-torque is put forward. First, the controlled object and movement features of the rehabilitation robot system are briefly introduced. Then, computed torque control method is analyzed, and for the uncertainty part of computed torque as well as the environment disturbance, the RBF neural network compensator is designed. Finally, the simulation for the proposed algorithm is conducted and analyzed. The results show that the computed torque controller with RBF neural network compensator has smaller tracking error than the PD feedback controller based on computed torque.
Starting Page	359
Ending Page	364
File Size	208599
Page Count	6
File Format	PDF
ISBN	9781479987283
e-ISBN	9781479987306
DOI	10.1109/CYBER.2015.7287963
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-06-08
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Torque Uncertainty Computational modeling Neural networks Torque control Lower limb rehabilitation robot RBF neural network compensation PD computed torque control Gait trajectory tracking control Joints Robots
Content Type	Text
Resource Type	Article

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