0
Research Papers

Decentralized Trajectory Tracking Control for Modular and Reconfigurable Robots With Torque Sensor: Adaptive Terminal Sliding Control-Based Approach

[+] Author and Article Information
Yan Li

Department of Control Science and Engineering,
Changchun University of Technology,
Changchun 130012, China
e-mail: liyan_dianqi@ccut.edu.cn

Zengpeng Lu

Department of Control Science and Engineering,
Changchun University of Technology,
Changchun 130012, China
e-mail: luzengpeng@outlook.com

Fan Zhou

Department of Control Science and Engineering,
Changchun University of Technology,
Changchun 130012, China
e-mail: zhoufan@ccut.edu.cn

Bo Dong

Department of Control Science and Engineering,
Changchun University of Technology,
Changchun 130012, China
e-mail: dongbo@ccut.edu.cn

Keping Liu

Department of Control Science and Engineering,
Changchun University of Technology,
Changchun 130012, China
e-mail: liukeping@ccut.edu.cn

Yuanchun Li

Department of Control Science and Engineering,
Changchun University of Technology,
Changchun 130012, China
e-mail: liyc@ccut.edu.cn

1Corresponding author.

Contributed by the Dynamic Systems Division of ASME for publication in the JOURNAL OF DYNAMIC SYSTEMS, MEASUREMENT,AND CONTROL. Manuscript received October 9, 2018; final manuscript received January 10, 2019; published online February 18, 2019. Assoc. Editor: Xuebo Zhang.

J. Dyn. Sys., Meas., Control 141(6), 061003 (Feb 18, 2019) (9 pages) Paper No: DS-18-1457; doi: 10.1115/1.4042550 History: Received October 09, 2018; Revised January 10, 2019

The main technical challenge in decentralized control of modular and reconfigurable robots (MRRs) with torque sensor is related to the treatment of interconnection term and friction term. This paper proposed a modified adaptive sliding mode decentralized control strategy for trajectory tracking control of the MRRs. The radial basis function (RBF) neural network is used as an effective learning method to approximate the interconnection term and friction term, eliminating the effect of model uncertainty and reducing the controller gain. In addition, in order to provide faster convergence and higher precision control, the terminal sliding mode algorithm is introduced to the controller design. Based on the Lyapunov method, the stability of the MRRs is proved. Finally, experiments are performed to confirm the effectiveness of the method.

FIGURES IN THIS ARTICLE
<>
Copyright © 2019 by ASME
Your Session has timed out. Please sign back in to continue.

References

Harada, K. , Oetomo, D. , Susilo, E. , Menciassi, A. , Daney, D. , Merlet, J. , and Dario, P. , 2010, “ A Reconfigurable Modular Robotic Endoluminal Surgical System: Vision and Preliminary Results,” Robotica, 28(2), pp. 171–183. [CrossRef]
Cobos-Guzman, S. , Torres, J. , and Lozano, R. , 2013, “ Design of an Underwater Robot Manipulator for a Telerobotic System,” Robotica, 31(6), pp. 945–953. [CrossRef]
Roehr, T. M. , and Kirchner, F. , 2013, “ Reconfigurable Integrated Multirobot Exploration System (RIMRES): Heterogeneous Modular Reconfigurable Robots for Space Exploration,” J. Field Rob., 31(1), pp. 3–34. [CrossRef]
Yoo, S. S. , Rama, S. , Szewczyk, B. , Pui, J. W. Y. , Lee, W. , and Kim, L. , 2015, “ Endoscopic Capsule Robots Using Reconfigurable Modular Assembly: A Pilot Study,” Int. J. Imaging Syst. Technol., 24(4), pp. 359–365. [CrossRef]
Biglarbegian, M. , Melek, W. W. , and Mendel, J. M. , 2011, “ Design of Novel Interval Type-2 Fuzzy Controllers for Modular and Reconfigurable Robots: Theory and Experiments,” IEEE Trans. Ind. Electron., 58(4), pp. 1371–1384. [CrossRef]
Kasprzak, W. , Szynkiewicz, W. , Zlatanov, D. , and Zielinska, T. , 2014, “ A Hierarchical CSP Search for Path Planning of Cooperating Self-Reconfigurable Mobile Fixtures,” Eng. Appl. Artif. Intell., 34(9), pp. 85–98. [CrossRef]
Xu, Q. , 2013, “ Adaptive Discrete-Time Sliding Mode Impedance Control of a Piezoelectric Microgripper,” IEEE Trans. Rob., 29(3), pp. 663–673. [CrossRef]
Pham, C. V. , and Wang, Y. N. , 2015, “ Robust Adaptive Trajectory Tracking Sliding Mode Control Based on Neural Networks for Cleaning and Detecting Robot Manipulators,” J. Intell. Rob. Syst., 79(1), pp. 101–114. [CrossRef]
Angelini, F. , Santina, C. D. , Garabini, M. , Bianchi, M. , Gasparri, G. M. , Grioli, G. , Catalano, M. G. , and Bicchi, A. , 2018, “ Decentralized Trajectory Tracking Control for Soft Robots Interacting With the Environment,” IEEE Trans. Rob., 34(4), pp. 924–935.
Li, X. , and Ercan, M. F. , 2018, “ Decentralized Coordination Control for a Network of Mobile Robotic Sensors,” Wireless Pers. Commun., 102(4), pp. 2429–2442. [CrossRef]
Zhou, F. , Li, Y. , and Liu, G. , 2017, “ Robust Decentralized Force/Position Fault-Tolerant Control for Constrained Reconfigurable Manipulators Without Torque Sensing,” Nonlinear Dyn., 89(2), pp. 955–969. [CrossRef]
Dong, B. , Liu, K. , and Li, Y. , 2017, “ Decentralized Control of Harmonic Drive Based Modular Robot Manipulator Using Only Position Measurements: Theory and Experimental Verification,” J. Intell. Rob. Syst., 88(1), pp. 3–18. [CrossRef]
He, W. , Chen, Y. , and Zhao, Y. , 2016, “ Adaptive Neural Network Control of an Uncertain Robot With Full-State Constraints,” IEEE Trans. Cybern., 46(3), pp. 620–629. [CrossRef] [PubMed]
He, W. , Dong, Y. , and Sun, C. , 2016, “ Adaptive Neural Impedance Control of a Robotic Manipulator With Input Saturation,” IEEE Trans. Syst. Man Cybern. Syst., 46(3), pp. 334–344. [CrossRef]
Lu, H. , Zhang, X. , and Huang, X. , 2017, “ Robust Adaptive Control of Antagonistic Tendon-Driven Joint in the Presence of Parameter Uncertainties and External Disturbances,” ASME. J. Dyn. Sys., Meas., Control, 139(10), p. 101003.
Wang, Y. , Xu, L. , and Wu, H. , 2018, “ Adaptive Robust Backstepping Output Tracking Control for a Class of Uncertain Nonlinear Systems Using Neural Network,” ASME. J. Dyn. Sys., Meas., Control, 140(7), p. 071014.
Sun, C. , He, W. , Ge, W. , and Chang, C. , 2017, “ Adaptive Neural Network Control of Biped Robots,” IEEE Trans. Syst. Man Cybern. Syst., 47(2), pp. 1–12. [CrossRef]
Zhong-Qiang, W. U. , Zhuang, S. Y. , Bao-Ming, M. A. , and Xiao, C. J. , 2011, “ Research on Adaptive Fuzzy Sliding Mode Control for Grid-Connected Inverter Based on Inverse System,” Power Syst. Prot. Control, 39(24), pp. 1–7.
Wu, D. , Chen, M. , and Gong, H. , 2018, “ Adaptive Neural Flight Control for an Aircraft With Time-Varying Distributed Delays,” Neurocomputing, 307, pp. 130–145. [CrossRef]
Wei, H. , Huang, H. , and Ge, S. S. , 2017, “ Adaptive Neural Network Control of a Robotic Manipulator With Time-Varying Output Constraints,” Neurocomputing, 47(10), pp. 3136–3147.
Yang, H. , and Liu, J. , 2018, “ An Adaptive RBF Neural Network Control Method for a Class of Nonlinear Systems,” IEEE/CAA J. Autom. Sin., 5(2), pp. 457–462. [CrossRef]
Kali, Y. , Saad, M. , Benjelloun, K. , and Benbrahim, M. , 2017, “ Sliding Mode With Time Delay Control for Robot Manipulators,” IEEE International Multidisciplinary Conference on Engineering Technology (IMCET), Beirut, Lebanon, Nov. 2–4, pp. 135–156.
Van Tran, T. , Wang, Y. , Ao, H. , and Khac Truong, T. , 2015, “ Sliding Mode Control Based on Chemical Reaction Optimization and Radial Basis Functional Link Net for De-Icing Robot Manipulator,” ASME. J. Dyn. Sys., Meas., Control, 137(5), p. 051009.
Jung, S. , 2018, “ Improvement of Tracking Control of a Sliding Mode Controller for Robot Manipulators by a Neural Network,” Int. J. Control Autom. Syst., 16(2), pp. 937–943. [CrossRef]
Wang, Z. , Su, Y. , and Zhang, L. , 2017, “ A New Nonsingular Terminal Sliding Mode Control for Rigid Spacecraft Attitude Tracking,” ASME. J. Dyn. Sys., Meas., Control, 140(5), p. 051006.
Zhang, F. , 2017, “ High-Speed Nonsingular Terminal Switched Sliding Mode Control of Robot Manipulators,” IEEE/CAA J. Autom. Sin., 4(4), pp. 775–781. [CrossRef]
Ma, Z. , and Sun, G. , 2017, “ Dual Terminal Sliding Mode Control Design for Rigid Robotic Manipulator,” J. Franklin Inst., 355(18), pp. 9127–9149. [CrossRef]
Yang, Y. , 2018, “ A Time-Specified Nonsingular Terminal Sliding Mode Control Approach for Trajectory Tracking of Robotic Airships,” Nonlinear Dyn., 92(3), pp. 1359–1367. [CrossRef]
Liu, G. , Abdul, S. , and Goldenberg, A. , 2008, “ Distributed Control of Modular and Reconfigurable Robot With Torque Sensing,” Robotica, 26(1), pp. 75–84. [CrossRef]
Imura, J. , Sugie, T. , Yokokohji, Y. , Jun-ichi, I. , Toshiharu, S. , Yasuyoshi, Y. , and Tsuneo, Y. , 1991, “ Robust Control of Robot Manipulators Based on Joint Torque Sensor Information,” Int. J. Robot. Res., 13(5), pp. 434–442.
Liu, G. , 2002, “ Decomposition-Based Friction Compensation of Mechanical Systems,” Mechatronics, 12(5), pp. 755–769. [CrossRef]
Liu, G. , and Goldenberg, A. A. , 1997, “ Robust Control of Robot Manipulators Based on Dynamics Decomposition,” IEEE Trans. Rob. Autom., 1(5), pp. 783–789.
Liu, G. , and Goldenberg, A. A. , 1996, “ Uncertainty Decomposition-Based Robust Control of Robot Manipulators,” IEEE Trans. Control Syst. Technol., 4(4), pp. 384–93. [CrossRef]
Liu, G. , and Goldenberg, A. A. , 1996, “ Comparative Study of Robust Saturation Control of Robot Manipulators: Analysis and Experiments,” Int. J. Rob. Res., 15(5), pp. 473–491. [CrossRef]

Figures

Grahic Jump Location
Fig. 1

Schematic of joint module

Grahic Jump Location
Fig. 2

System architecture diagram

Grahic Jump Location
Fig. 4

Experimental platform and related test components

Grahic Jump Location
Fig. 5

Tracking performance curves: (a) robust control and (b) adaptive terminal sliding mode control

Grahic Jump Location
Fig. 6

Tracking error curves under different controllers: (a) robust control and (b) adaptive terminal sliding mode control

Grahic Jump Location
Fig. 7

Control torque curves: (a) joint 1 control torque and (b) joint 2 control torque

Tables

Errata

Some tools below are only available to our subscribers or users with an online account.

Related Content

Customize your page view by dragging and repositioning the boxes below.

Related Journal Articles
Related eBook Content
Topic Collections

Sorry! You do not have access to this content. For assistance or to subscribe, please contact us:

  • TELEPHONE: 1-800-843-2763 (Toll-free in the USA)
  • EMAIL: asmedigitalcollection@asme.org
Sign In