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Research Papers

Fuzzy Sliding Mode Control of Robotic Manipulators With Kinematic and Dynamic Uncertainties

[+] Author and Article Information
Haitao Liu

 School of Mechanical & Automotive Engineering, South China University of Technology, Guangzhou 510640, Chinagdliuht@126.com

Tie Zhang

 School of Mechanical & Automotive Engineering, South China University of Technology, Guangzhou 510640, Chinamerobot@scut.edu.cn

J. Dyn. Sys., Meas., Control 134(6), 061007 (Sep 13, 2012) (9 pages) doi:10.1115/1.4006626 History: Received October 22, 2010; Revised March 10, 2012; Published September 13, 2012; Online September 13, 2012

Sliding mode control is a very attractive control scheme with strong robustness to structured and unstructured uncertainties as well as to external disturbances. In this paper, a robust fuzzy sliding mode controller, which is combined with an adaptive fuzzy logic system, is proposed to improve the control performance of the robotic manipulator with kinematic and dynamic uncertainties. In this controller, the sliding mode control is employed to improve the control accuracy and the robustness of the robotic manipulator, and the fuzzy logic control is adopted to approximate various uncertainties and to eliminate the chattering without the help of any prior knowledge of system uncertainties. The effectiveness of the proposed controller is then verified by the simulations on a 2-DOF (degrees of freedom) robotic manipulator and the experiments on an SCARA robot with four degrees of freedom. Simulated and experimental results indicate that the proposed controller is effective in the robust tracking of the robotic manipulator with kinematic and dynamic uncertainties.

Copyright © 2012 by American Society of Mechanical Engineers
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Figures

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Figure 1

Fuzzy sliding mode controller

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Figure 2

Schematic diagram of the simulated 2-DOF manipulator

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Figure 3

Desired and actual end-effector tracking trajectories in simulation 1

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Figure 4

Position tracking in x and y directions in simulation 1

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Figure 5

Tracking errors in simulation 1

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Figure 6

Input control torques in simulation 1

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Fuzzy output Kfs in simulation 1

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Trajectory tracking without disturbances

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Figure 9

End-effector tracking errors without disturbances

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Figure 10

Trajectory tracking comparison with disturbances

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End-effector tracking errors comparison with disturbances

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Figure 12

Dynamic parameter estimates with disturbances

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Figure 13

Desired and actual end-effector tracking trajectories in simulation 2

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Figure 14

Position tracking in x and y directions in simulation 2

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Figure 15

Tracking errors in simulation 2

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Figure 16

SCARA robot used for experiment

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Figure 17

Desired and actual end-effector tracking trajectories in the experiment

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