Hostname: page-component-586b7cd67f-tf8b9 Total loading time: 0 Render date: 2024-11-28T01:50:49.602Z Has data issue: false hasContentIssue false
  • English
  • Français

Sliding mode nonlinear disturbance observer-based adaptive back-stepping control of a humanoid robotic dual manipulator

Published online by Cambridge University Press:  07 August 2018

Keqiang Bai ,
Xuantao Gong ,
Sihai Chen ,
Yingtong Wang  and
Zhigui Liu
Keqiang Bai*
Affiliation:
School of Information Engineering, Southwest University of Science and Technology, Mianyang 621010, P.R. China
Xuantao Gong*
Affiliation:
Information Technology Teaching Center, Tianfu College of Southwestern University of Finance and Economics, Mianyang 621000, P.R. China
Sihai Chen
Affiliation:
Science and Technology Department, Mianyang Vocational and Technical College, Mianyang 621000, P.R. China E-mail: [email protected]
Yingtong Wang
Affiliation:
School of Civil and Environmental Engineering, City College of Southwest University of Science and Technology, Mianyang 621000, P.R. China E-mail: [email protected]
Zhigui Liu*
Affiliation:
School of Information Engineering, Southwest University of Science and Technology, Mianyang 621010, P.R. China
*
*Corresponding authors. E-mail: [email protected], [email protected], [email protected]
*Corresponding authors. E-mail: [email protected], [email protected], [email protected]
*Corresponding authors. E-mail: [email protected], [email protected], [email protected]
Get access Rights & Permissions [Opens in a new window]

Summary

An adaptive back-stepping sliding mode controller (ABSMC) algorithm was developed for nonlinear uncertain systems based on a nonlinear disturbance observer (NDO). The developed ABSMC was applied to attitude control for the dual arm of a humanoid robot. Considering the system uncertainty and the unknown external disturbances, the ABSMC scheme was designed to eliminate the chattering phenomenon in the traditional sliding mode control and to reduce the tracking error closer to zero. The ABSMC algorithm solved problems related to the chattering of the system for both uncertainties and disturbances in the humanoid robotic system with an NDO in a two-dimensional environment. The algorithm was designed to work equally well with agents, with higher degrees of freedom in different applications. The method was appropriate for improving tracking performance. The ABSMC algorithm guaranteed global stability and improved the dynamic performance of the system. The algorithm inherited a low computational cost, probabilistic completeness, and asymptotic optimality from the fuzzy sliding mode control. This algorithm has a practical application in the dual arm of a humanoid robot with a circular trajectory. This paper showed the effectiveness and applicability of the proposed methods, which reduced the output of the controller and improved the control performance of the humanoid robotic system. The new combined control algorithm, ABSMC, was able to feasibly and efficiently weaken the chattering on the robot's closed-loop paths, starting and finishing at the same configuration.

Keywords

Adaptive back-stepping sliding mode controlHumanoid robotsNonlinear disturbance observerHumanoid robotic system
Type
Articles
Information
Robotica , Volume 36 , Issue 11 , November 2018 , pp. 1728 - 1742
Copyright
Copyright © Cambridge University Press 2018 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

1. Tonke, D. and Lee, T.-E., “Modeling, analysis, and scheduling of cluster tools with two independent arms,” IEEE Trans. Autom. Sci. Eng. 13 (2), 1176–1188 (2016).Google Scholar
2. Shin, S. Y. and Kim, C. H., “Human-like motion generation and control for humanoid's dual arm object manipulation,” IEEE Trans. Ind. Electron. 62 (4), 2265–2276 (2015).Google Scholar
3. Phee, S. J., Low, S. C., Dario, P. and Menciassi, A., “Tendon sheath analysis for estimation of distal end force and elongation for sensorless distal end,” Robotica 28 (7), 1073–1082 (2010).Google Scholar
4. Li, T. and Ceccarelli, M., “Design and simulated characteristics of a new biped mechanism,” Robotica 33 (1), 1568–1588 (2015).Google Scholar
5. Nicolis, D., Zanchettin, A. M. and Rocco, P., “Constraint-based and sensorless force control with an application to a lightweight dual-arm robot,” IEEE Robot. Autom. Lett. 1 (1), 340–347 (2016).Google Scholar
6. Ragaglia, M., Zanchettin, A. M., Bascetta, L. and Rocco, P., “Accurate sensorless lead-through programming for lightweight robots in structured environments,” Robot. Comput.-Integr. Manuf. 39, 9–21 (2016).Google Scholar
7. Hill, J. and Fahimi, F., “Active disturbance rejection for walking bipedal robots using the acceleration of the upper limbs,” Robotica 33 (2), 264–281 (2015).Google Scholar
8. Huang, S. J., Liu, S. and Wu, C. H., “Intelligent humanoid mobile robot with embedded control and stereo visual feedback,” J. Mech. Sci. Technol. 29 (9), 3919–3931 (2015).Google Scholar
9. Garofalo, G. and Ott, C., “Limit cycle control using energy function regulation with friction compensation,” IEEE Robot. Autom. Lett. 1 (1), 90–97 (2016).Google Scholar
10. Norton, M., Khoo, S., Kouzani, A. and Stojcevski, A., “Adaptive fuzzy multi-surface sliding control of multiple-input and multiple-output autonomous flight systems,” IET Control Theory Appl. 9 (4), 587–597 (2015).Google Scholar
11. Chang, X.-H., “Robust non-fragile H∞ filtering of fuzzy systems with linear fractional parametric uncertainties,” IEEE Trans. Fuzzy Syst. 20, 1001–1011 (2012).Google Scholar
12. Fahimi, F. and Van Kleeck, C., “Alternative trajectory-tracking control approach for marine surface vessels with experimental verification,” Robotica 31 (1), 25–33 (2013).Google Scholar
13. Liu, H., Xi, J. and Zhong, Y., “Robust optimal attitude control of a laboratory helicopter without angular velocity feedback,” Robotica 33 (2), 282–294 (2015).Google Scholar
14. Chen, W. H., Ballance, D. J., Gawthrop, P. J. and O'Reilly, J., “A nonlinear disturbance observer for robotic manipulators,” IEEE Trans. Ind. Electron. 47 (4), 932–938 (2000).Google Scholar
15. Corradini, M. L., Fossi, V., Giantomassi, A., Longhi, S. and Orlando, G., “Discrete time sliding mode control of robotic manipulators: Development and experimental validation,” Control Eng. Pract. 20 (8), 816–822 (2012).Google Scholar
16. Incremona, G. P., De Felici, G. and Ferrara, A., “A supervisory sliding mode control approach for cooperative robotic system of systems,” IEEE Syst. J. 9 (1), 263–272 (2015).Google Scholar
17. Firoozabadi, A. E., Ebrahimi, S. and Fontllagunes, J. M., “A comparative study of elastic motions in trajectory tracking of flexible RPR planar manipulators moving with high speed,” Robotica 35 (7), 1523–1540 (2017).Google Scholar
18. Khan, Q., Bhatti, A. I., Iqbal, M. and Ahmed, Q., “Dynamic integral sliding mode control for SISO uncertain nonlinear systems,” Int. J. Innovative Comput. Inf. Control 8 (7), 4621–4633 (2012).Google Scholar
19. Mohammadi, A., Tavakoli, M., Marquez, H. J. and Hashemzadeh, F., “Nonlinear disturbance observer design for robotic manipulators,” Control Eng. Pract. 21 (3), 253–267 (2013).Google Scholar
20. Eom, M. and Chwa, D., “Robust swing-up and balancing control using a nonlinear disturbance observer for the pendubot system with dynamic friction,” IEEE Trans. Robot. 31 (2), 331–343 (2015).Google Scholar
21. Ginoya, D., Shendge, P. D. and Phadke, S. B., “Disturbance observer based sliding mode control of nonlinear mismatched uncertain systems,” Commun. Nonlinear Sci. Numer. Simul. 26 (1), 98–107 (2015).Google Scholar
22. Singh, Y. and Santhakumar, M., “Inverse dynamics and robust sliding mode control of a planar parallel (2-PRP and 1-PPR) robot augmented with a nonlinear disturbance observer,” Mech. Mach. Theory 92, 29–50 (2015).Google Scholar
23. Mohammed, S., Huo, W., Huang, J., Rifaïa, H. and Amirat, Y., “Nonlinear disturbance observer based sliding mode control of a human-driven knee joint orthosis,” Robot. Auton. Syst. 75, 41–49 (2016).Google Scholar
24. Santhakumar, M., “A nonregressor nonlinear disturbance observer-based adaptive control scheme for an underwater manipulator,” Adv. Robot. 27 (16), 1273–1283 (2013).Google Scholar
25. Rigatos, G. G., “Control and disturbances compensation in underactuated robotic systems using the derivative-free nonlinear Kalman filter,” Robotica 35 (3), 687–711 (2017).Google Scholar
26. Abdelhedi, F., Bouteraa, Y. and Derbel, N., “Second order sliding mode based synchronization control for cooperative robot manipulators,” Adv. Appl. Nonlinear Control Syst. Springer, Cham, 669–683 (2016).Google Scholar
27. Zhao, D., Li, S., and , Q Z. “Adaptive synchronised tracking control for multiple robotic manipulators with uncertain kinematics and dynamics,” Int. J. Syst. Sci. 47 (4), 791–804 (2016).Google Scholar
28. Bai, K., Luo, M., Liu, M. and Jiang, G., “Fuzzy Backstepping Control for Dual-Arm Cooperative Robot Grasp,” Proceedings of International Conference on Robotics Biomimetics ROBIO2015, Zhuhai, China (Dec. 2015) pp. 2563–2568.Google Scholar
29. Mahyuddin, M. N., Khan, S. G. and Herrmann, G., “A novel robust adaptive control algorithm with finite-time online parameter estimation of a humanoid robot arm,” Robot. Auton. Syst. 62, 294–305 (2014).Google Scholar