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Experimental study on the control of a suspended cable-driven parallel robot for object tracking purpose

Published online by Cambridge University Press:  17 May 2022

Soroush Zare ,
Mohammad Reza Hairi Yazdi Open the ORCID record for Mohammad Reza Hairi Yazdi [Opens in a new window] ,
Mehdi Tale Masouleh ,
Dan Zhang ,
Sahand Ajami  and
Amirhossein Afkhami Ardekani
Soroush Zare
Affiliation:
School of Mechanical Engineering, University of Tehran, Tehran, Iran
Mohammad Reza Hairi Yazdi*
Affiliation:
School of Mechanical Engineering, University of Tehran, Tehran, Iran Department of Mechanical Engineering, Lassonde School of Engineering, York University, Toronto, Canada
Mehdi Tale Masouleh
Affiliation:
Human and Robot Interaction Laboratory, School of Electrical and Computer Engineering, University of Tehran, Tehran, Iran
Dan Zhang
Affiliation:
Department of Mechanical Engineering, Lassonde School of Engineering, York University, Toronto, Canada
Sahand Ajami
Affiliation:
Mechanical Engineering Department, Amirkabir University of Technology, Tehran, Iran
Amirhossein Afkhami Ardekani
Affiliation:
School of Mechanical Engineering, University of Tehran, Tehran, Iran
*
*Corresponding author. E-mail: [email protected]
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Abstract

In this paper, control of a suspended cable-driven parallel robot has been experimentally investigated based on the dynamic model of the robot for object tracking purpose. In order to improve the tracking ability of the robot, three control approaches, namely kinematic PID, dynamic PD, and a kinematic sliding mode control (SMC), have been implemented, both on the Simscape and on the robot constructed at the Human and Robot Interaction Laboratory. Neural network controller and dynamic SMC have been implemented on the Simscape model. Afterward, the effectiveness of each approach has been investigated by employing the root mean square error (RMSE) index. Simulation and experimental results reveal the ability of each controller for precise and smooth control. For precise and real-time object tracking, YOLOv5-s and YOLOv4-tiny model are trained. By comparing the obtained index values, the kinematic PID demonstrates the best performance with the maximum RMSE value of 0.018 compared to other methods.

Keywords

cable-driven parallel robotdynamicsneural network controllerPIDsliding mode control
Type
Research Article
Information
Robotica , Volume 40 , Issue 11 , November 2022 , pp. 3863 - 3877
Copyright
© The Author(s), 2022. Published by Cambridge University Press

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