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Research Papers

Adaptive Control of Teleoperation Systems With Linearly and Nonlinearly Parameterized Dynamic Uncertainties

[+] Author and Article Information
Xia Liu

 School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu, Sichuan 611731, China; Department of Electrical and Computer Engineering,  University of Alberta, Edmonton, AB, T6G 2V4, Canada e-mail: xia8@ualberta.ca

Mahdi Tavakoli

Department of Electrical and Computer Engineering,  University of Alberta, Edmonton, AB, T6G 2V4, Canada e-mail: tavakoli@ece.ualberta.ca

J. Dyn. Sys., Meas., Control 134(2), 021015 (Jan 12, 2012) (10 pages) doi:10.1115/1.4005049 History: Received September 23, 2010; Revised July 18, 2011; Published January 11, 2012; Online January 12, 2012

Existing work concerning adaptive control of uncertain teleoperation systems only deals with linearly parameterized (LP) dynamic uncertainties. Typical teleoperation system dynamics, however, also posses terms with nonlinearly parameterized (NLP) structures. An example of such terms is friction, which is ubiquitous in the joints of the master and slave robots of practical teleoperation systems. Uncertainties in the NLP dynamic terms may lead to significant position and force tracking errors if not compensated for in the control scheme. In this paper, adaptive controllers are designed for the master and slave robots with both LP and NLP dynamic uncertainties. Next, these controllers are incorporated into the 4-channel bilateral teleoperation control framework to achieve transparency. Then, transparency of the overall teleoperation is studied via a Lyapunov function analysis. Simulation studies demonstrate the effectiveness of the proposed adaptive scheme when exact knowledge of the LP and NLP dynamics is unavailable.

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Copyright © 2012 by American Society of Mechanical Engineers
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Figures

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Figure 1

Block diagram of a general bilateral teleoperation system

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Figure 2

Architecture of the proposed 4-channel adaptive teleoperation control approach

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Figure 3

Revolute-prismatic robot

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Figure 4

(a) Position tracking, (b) Position tracking error, (c) Force tracking, and (d) Force tracking error. Fixed control scheme, which is not designed to deal with any uncertainties.

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Figure 5

(a) Position tracking, (b) Position tracking error, (c) Force tracking, and (d) Force tracking error. Conventional adaptive control, which merely deals with LP dynamic uncertainties.

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Figure 6

(a) Position tracking, (b) Position tracking error, (c) Force tracking, and (d) Force tracking error. Proposed adaptive control, which deals with both LP and NLP dynamic uncertainties.

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