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Study on the design and control method of a wire-driven waist rehabilitation training parallel robot

Published online by Cambridge University Press:  28 March 2022

Wang Yuqi Open the ORCID record for Wang Yuqi [Opens in a new window] ,
Cao Jinjiang ,
Geng Ranran ,
Zhou Lei  and
Wang Lei
Wang Yuqi*
Affiliation:
School of Automation, Nanjing Institute of Technology, Nanjing, China
Cao Jinjiang
Affiliation:
School of Automation, Nanjing Institute of Technology, Nanjing, China
Geng Ranran
Affiliation:
Industrial Center, Nanjing Institute of Technology, Nanjing, China
Zhou Lei
Affiliation:
School of Automation, Nanjing Institute of Technology, Nanjing, China
Wang Lei
Affiliation:
School of Automation, Nanjing Institute of Technology, Nanjing, China
*
Corresponding author. E-mail: 940898628@qq.com
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Abstract

With the increasing demand for rehabilitation and the lack of professional rehabilitation personnel, robot-assisted rehabilitation technology plays an increasingly important role in neurological rehabilitation. In order to recover the exercise ability of patients with waist injury, a new type of wire-driven waist rehabilitation training parallel robot (WWRTPR) is designed. According to the motion trajectory planning of waist rehabilitation training, two coordinate systems are established: moving coordinate system and static coordinate system. The inverse kinematics modeling analysis is carried out, and the dynamic model of the robot is established by using Newton–Euler method. An intelligent control method of force/position hybrid control based on radial basis function neural network is proposed. The stability of the closed-loop system is analyzed, and the results show that WWRTPR tends to be stable. The simulation analysis of rehabilitation training on WWRTPR is carried out, and the simulation results show that the proposed intelligent control method can effectively control the robot system, which provides a reference for the development of a flexible intelligent rehabilitation training robot.

Keywords

wire-driven rehabilitation training robotprototype designmodeling analysisintelligent controlsimulation analysis
Type
Research Article
Information
Robotica , Volume 40 , Issue 10 , October 2022 , pp. 3499 - 3513
Copyright
© The Author(s), 2022. Published by Cambridge University Press

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