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Technical Briefs

Position Control of a Rehabilitation Robotic Joint Based on Neuron Proportion-Integral and Feedforward Control

[+] Author and Article Information
Xian-Zhi Jiang, Xin-Han Huang

Postdoc Department of Control Science and Engineering, State Key Lab of Digital Manufacturing, Equipment and Technology,  Huazhong University of Science & Technology, Wuhan 430074, Chinae-mail: jiang_sider@163.comDepartment of Control Science and Engineering,  Huazhong University of Science & Technology, Wuhan 430074, Chinae-mail: xhhuang@mail.hust.edu.cnState Key Lab of Digital Manufacturing Equipment and Technology,  Huazhong University of Science & Technology, Wuhan 430074, China

Cai-Hua Xiong

Postdoc Department of Control Science and Engineering, State Key Lab of Digital Manufacturing, Equipment and Technology,  Huazhong University of Science & Technology, Wuhan 430074, Chinae-mail: jiang_sider@163.comchxiong@mail.hust.edu.cnDepartment of Control Science and Engineering,  Huazhong University of Science & Technology, Wuhan 430074, Chinae-mail: xhhuang@mail.hust.edu.cnchxiong@mail.hust.edu.cnState Key Lab of Digital Manufacturing Equipment and Technology,  Huazhong University of Science & Technology, Wuhan 430074, Chinachxiong@mail.hust.edu.cn

Rong-Lei Sun

Postdoc Department of Control Science and Engineering, State Key Lab of Digital Manufacturing, Equipment and Technology,  Huazhong University of Science & Technology, Wuhan 430074, Chinae-mail: jiang_sider@163.comronglei@mail.hust.edu.cnDepartment of Control Science and Engineering,  Huazhong University of Science & Technology, Wuhan 430074, Chinae-mail: xhhuang@mail.hust.edu.cnronglei@mail.hust.edu.cnState Key Lab of Digital Manufacturing Equipment and Technology,  Huazhong University of Science & Technology, Wuhan 430074, Chinaronglei@mail.hust.edu.cn

You-Lun Xiong

Postdoc Department of Control Science and Engineering, State Key Lab of Digital Manufacturing, Equipment and Technology,  Huazhong University of Science & Technology, Wuhan 430074, Chinae-mail: jiang_sider@163.comfamt@mail.hust.edu.cnDepartment of Control Science and Engineering,  Huazhong University of Science & Technology, Wuhan 430074, Chinae-mail: xhhuang@mail.hust.edu.cnfamt@mail.hust.edu.cnState Key Lab of Digital Manufacturing Equipment and Technology,  Huazhong University of Science & Technology, Wuhan 430074, Chinafamt@mail.hust.edu.cn

J. Comput. Nonlinear Dynam 7(2), 024502 (Jan 09, 2012) (6 pages) doi:10.1115/1.4005436 History: Received January 23, 2011; Revised November 03, 2011; Published January 09, 2012; Online January 09, 2012

The joint of the upper limb rehabilitation robot, which is designed and built in our lab, is driven by pneumatic muscles (PMs) in an opposing pair configuration. Each PM drives the robotic joint through a steel wire with a flexible sleeve and a tension device, which causes delay and various frictions as disturbances to the robotic joint system. These factors make the rehabilitation robotic joint very complex to model and control. Especially in position control, the overshoot is difficult to deal with when the directions of the friction forces are changing. The main purpose of this paper is to enhance the position control performance of the robotic joint by neuron PI and feedforward. Neuron PI control has a powerful capability of learning, adaptation, and tackling nonlinearity, and feedforward control demonstrates good performance in dealing with frictions, which cause overshoot. The results of the experiments indicate that the proposed controller, which combines neuron PI and feedforward, can enhance the performance in position control of the robotic joint, especially on dealing with overshoot.

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Copyright © 2012 by American Society of Mechanical Engineers
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Figures

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Figure 1

The nine-Dof upper limb rehabilitation robot

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Figure 2

Schematic diagram of robotic joint

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Figure 3

Angle of robotic joint with the pressure of master-side PM set to 0.15 MPa

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Figure 4

Structure of the neuron PI and feedforward controller in position tracking control

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Figure 5

Bock diagram of neuron PI control

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Figure 6

Work region of feedforward control

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Figure 7

Comparison between classical PI and neuron PI controller

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Figure 8

Updating of Kp and error of neuron PI control

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Figure 9

Updating of Ki and error of neuron PI control

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Figure 10

Comparison of position tracking between controller with feedforward and without feedforward

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