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A PD Computed Torque Control Method with Online Self-gain Tuning for a 3UPS-PS Parallel Robot

Published online by Cambridge University Press:  20 January 2021

Xiaogang Song ,
Yongjie Zhao Open the ORCID record for Yongjie Zhao [Opens in a new window] ,
Chengwei Chen ,
Liang’an Zhang  and
Xinjian Lu
Xiaogang Song
Affiliation:
Department of Mechatronics Engineering, Shantou University, Shantou City, Guangdong515063, P. R. China, E-mails: 16xgsong@gmail.com, chengwei0101@outlook.com
Yongjie Zhao*
Affiliation:
Department of Mechatronics Engineering, Shantou University, Shantou City, Guangdong515063, P. R. China, E-mails: 16xgsong@gmail.com, chengwei0101@outlook.com
Chengwei Chen
Affiliation:
Department of Mechatronics Engineering, Shantou University, Shantou City, Guangdong515063, P. R. China, E-mails: 16xgsong@gmail.com, chengwei0101@outlook.com
Liang’an Zhang
Affiliation:
School of Mechanical Engineering, Anhui University of Technology, Maanshan243000, P. R. China, E-mail: robotlab@ahut.edu.cn
Xinjian Lu
Affiliation:
Guangdong Goldenwork Robot Technology Ltd, Foshan City, Guangdong528226, P. R. China, E-mail: tigerw813@126.com
*
*Corresponding author. E-mail: meyjzhao@stu.edu.cn
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Summary

In this paper, an online self-gain tuning method of a PD computed torque control (CTC) is used for a 3UPS-PS parallel robot. The CTC is applied to the 3UPS-PS parallel robot based on the robot dynamic model which is established via a virtual work principle. The control system of the robot comprises a nonlinear feed-forward loop and a PD control feedback loop. To implement real-time online self-gain tuning, an adjustment method based on the genetic algorithm (GA) is proposed. Compared with the traditional CTC, the simulation results indicate that the control algorithm proposed in this study can not only enhance the anti-interference ability of the system but also improve the trajectory tracking speed and the accuracy of the 3UPS-PS parallel robot.

Keywords

3UPS-PS parallel robotDynamicsComputed torque controlPD controlGenetic algorithm
Type
Article
Information
Robotica , Volume 39 , Issue 9 , September 2021 , pp. 1575 - 1587
Copyright
© The Author(s), 2021. Published by Cambridge University Press

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References

Haouas, W. and Dahmouche, R., “A new 7-dof parallel robot with a foldable platform,” J. Mech. Robot. 10(4), 1–15 (2018).CrossRefGoogle Scholar
Zhao, Y. J. and Cheng, G., “Dimensional synthesis of a 3UPS-PRU parallel robot,” Robotica 35(12), 2319–2329 (2017).CrossRefGoogle Scholar
Zhang, N., “Dynamictrajectory planning of a 3-DOF under-constrainedcable-drivenparallel robot,” Mech. Mach. Theory 98, 21–35 (2016).CrossRefGoogle Scholar
Maldonado-Echegoyen, R., Castillo-Castaneda, E. and Garcia-Murillo, M. A., “Kinematic and deformation analyses of a translational parallel robot for drilling tasks,” J. Mech. Sci. Technol. 29(10), 4437–4443 (2015).CrossRefGoogle Scholar
Meng, Q., Xie, F. and Liu, X. J., “Conceptual design and kinematic analysis of a novel parallel robot for high-speed pick-and-place operations,” Front. Mech. Eng. 13(2), 211–224 (2018).CrossRefGoogle Scholar
Zhang, L. A., Wan, J. and Tan, Y. L., “Dimensional synthesis of Ahut-Delta parallel mechanism based on improved chaotic particle swarm algorithm,” Trans. Chin. Soc. Agric. Mach. 46(8), 344–351 (2015) (in Chinese).Google Scholar
Shin, J. C. and Lee, C. W., “Rider’s net moment estimation using control force of motion system for bicycle simulator,” J. Robot. Syst. 21(11), 597–607 (2004).CrossRefGoogle Scholar
Dong, W.,Du, Z. J., Xiao, Y. Q. and Chen, X. G., “Development of a parallel kinematic motion simulator platform,” Mechatronics 23(1), 154–161 (2013).CrossRefGoogle Scholar
Shang, W. W. and Cong, S., “Nonlinear computed torque control for a high-speed planar parallel manipulator,” Mechatronics 19(6), 987–992 (2009).CrossRefGoogle Scholar
Lashin, M., Fanni, M., Mohamed, A. M. and Miyashita, T., “Dynamic modeling and inverse optimal PID with feed-forward control in H∞, framework for a novel 3D pantograph manipulator,” Int. J. Control 16(1), 1–16 (2018).Google Scholar
Shang, W. W., Cong, S. and Jiang, S. L., “Dynamic model based nonlinear tracking control of a planar parallel manipulator,” Nonlinear Dyn. 60(4), 597–606 (2010).CrossRefGoogle Scholar
Wu, J., Wang, D. and Wang, L. P., “A control strategy of a two degree-of-freedom heavy duty parallel manipulator,” J. Dyn. Syst. T ASME 6(137), 1–10 (2015).Google Scholar
Agarwal, A., Nasa, C. and Bandyopadhyay, S., “Dynamic singularity avoidance for parallel manipulators using a task-priority based control scheme,” Mech. Mach. Theory 96, 107–126 (2016).CrossRefGoogle Scholar
Peng, W., Lin, Z. and Su, J., “Computed torque control-based composite nonlinear feedback controller for robot manipulators with bounded torques,” IET Control Theory A 3(6), 701–711 (2009).CrossRefGoogle Scholar
Cheng, H., Yiu, Y. K. and Li, Z. X., “Dynamics and control of redundantly actuated parallel manipulators,” IEEE-ASME T Mech. 8(4), 483–491 (2003).CrossRefGoogle Scholar
Codourey, A., “Dynamic modeling of parallel robots for computed-torque control implementation,” Int. J. Robot. Res. 17(12), 1325–1336 (1998).CrossRefGoogle Scholar
Davliakos, I. and Papadopoulos, E., “Model-based control of a 6-dof electrohydraulic Stewart-Gough platform,” Mech. Mach. Theory 43(11), 1385–1400 (2008).CrossRefGoogle Scholar
Zubizarreta, A., Marcos, M., Cabanes, I. and Pinto, C., “A procedure to evaluate extended computed torque control configurations in the Stewart-Gough platform,” Robot. Auton. Syst. 59(10), 770–781 (2011).CrossRefGoogle Scholar
Chen, Y., Ma, G. Y., Lin, S. X. and Gao, J., “Adaptive fuzzy computed-torquecontrol for robot manipulator with uncertain dynamics,” Int. J. Adv. Robot. Syst. 9(6), 1–9 (2012).CrossRefGoogle Scholar
Zhu, X. C., Tao, G. L., Yao, B. and Cao, J., “Adaptive robust posture control of a pneumatic muscles by driven parallel manipulator with redundancy,” Automatica 13(4), 2248–2257 (2008).CrossRefGoogle Scholar
Yang, Z. Y., Wu, J. and Mei, J. P., “Motor-mechanism dynamic model based neural network optimized computed torque control of a high speed parallel manipulator,” Mechatronics 17(7), 381–390 (2007).CrossRefGoogle Scholar
Sangdani, M. H., Tavakolpour-Saleh, A. R. and Lotfavar, A., “Genetic algorithm-based optimal computed torque control of a vision-based tracker robot: Simulation and experiment,” Eng. Appl. Artif. Intel. 67, 24–38 (2018).CrossRefGoogle Scholar
Tsai, L. W., “Solving the inverse dynamics of a Stewart-Gough manipulator by the principle of virtual work,” J. Mech. Des. 122(1), 3–9 (2000).CrossRefGoogle Scholar
Zhao, Y. J. and Gao, F., “Inverse dynamics of the 6-dof out-parallel manipulator by means of the principle of virtual work,” Robotica 27(2), 259–268 (2009).CrossRefGoogle Scholar
Wu, J., Li, T. M. and Guan, L. W., “Computed-torque control for a moving flight simulator platform,” J. Tsinghua Univ. (Sci. Tech.) 46(8), 1405–1408+1413 (2006) (in Chinese).Google Scholar