Abstract
An adaptive robust control system is considered for dual-arm manipulators (DAM) using the combination of second-order sliding mode control (SOSMC) and neural networks. The SOSMC deals with the system robustness when faced with external disturbances and parametric uncertainties. Meanwhile, the radial basis function network (RBFN) is to constitute an adaptation mechanism for approximating the unknown dynamic model of DAM. The stability of model estimator-integrated controller is analyzed using Lyapuov theory. To show the effectiveness of proposed controller, a four DOFs-DAM is applied as an illustrating example. The results reveal that the controller works well, excellently adapt to no information of robot modeling.
Article PDF
Similar content being viewed by others
Avoid common mistakes on your manuscript.
References
X. Yun and V. Kumar, “An approach to simultaneous control of trajectory and interaction forces in dual–arm configurations,” IEEE Transactions on Robotics and Automation, vol. 7, no. 5, pp. 618–625, October 1991. [click]
N. Sarkar, X. Yun, and V. Kumar, “Dynamic control of 3–d rolling contacts in two–arm manipulation,” IEEE Transactions on Robotics and Automation, vol. 13, no. 3, pp. 364–376, June 1997. [click]
Z. Doulgeri and A. Golfakis, “Nonlinear manipulation control of a compliant object by dual fingers,” ASME Journal of Dynamic Systems Measurement and Control, vol. 128, no. 3, pp. 473–481, September 2006.
N. Xi, T.-J. Tarn, and A. Bejczy, “Intelligent planning and control for multirobot coordination: an event–based approach,” IEEE Transactions on Robotics and Automation, vol. 12, no. 3, pp 439–452, June 1996. [click]
T. Yoshikawa, “Control algorithm for grasping and manipulation by multi–fingered robot hands using virtual truss model representation of internal force,” Proceedings of IEEE International Conference on Robotics and Automation, pp. 369–376, 2000. [click]
M. Yamano, J.–S. Kim, A. Konno, and M. Uchiyama, “Cooperative control of a 3d dual–flexible–arm robot,” Journal of Intelligent and Robotic Systems, vol. 39, no. 1, pp. 1–15, January 2004. [click]
S. Schneider and R. Cannon, “Object impedance control for cooperative manipulation: theory and experimental results,” IEEE Transactions on Robotics and Automation, vol. 8, no. 3, pp. 383–394, June 1992. [click]
R. Bonitz and T. Hsia, “Internal force–based impedance control for cooperating manipulators,” IEEE Transactions on Robotics and Automation, vol. 12, no. 1, pp. 78–89, February 1996. [click]
N. Yagiz, Y. Hacioglu, and Y. Z. Arslan, “Load transportation by dual arm robot using sliding mode control,” Journal of Mechanical Science and Technology, vol. 24, no. 5, pp. 1177–1184, May 2010.
S. T. Lin and A. K. Huang, “Position–based fuzzy force control for dual industrial robots,” Journal of Intelligent and Robotic Systems, vol. 4, no. 19, pp. 393–409, August 1997.
S. S. Ge, C. C. Hang, and L. C. Woon, “Adaptive neural network control of robot manipulators in task space,” IEEE Transactions on Industrial Electronics, vol. 44, no. 6, pp. 995–1003, December 1997
M. J. Lee and Y. K. Choi, “An adaptive neurocontroller using RBFN for robot manipulators,” IEEE Transactions on Industrial Electronics, vol. 51, no. 3, pp.711–717, June 2004. [click]
L. Wang and T. Chai, “Neural–network–based terminal sliding–mode control of robotic manipulators including actuator dynamics,” IEEE Transactions on Industrial Electronics, vol. 56, no. 9, pp. 3290–3304, September 2009
J. J. Slotine and W. Li, “Adaptive manipulator control: a case study,” IEEE Transactions on Automatic Control, vol. 33, no. 11, pp. 995–1003, November 1988. [click]
Y. Hacioglu, Y. Z. Arslan, and N. Yagiz, “MIMO fuzzy sliding mode controlled dual arm robot in load transportation,” Journal of the Franklin Institute, vol. 348 pp. 1886–1902, October 2011. [click]
Z. Liu, C. Chen, Y. Zhang, and C. L. P. Chen, “Adaptive neural control for dual–arm coordination of humanoid robot with unknown nonlinearities in output mechanism,” IEEE Transactions on Cybernetics, vol. 45, no. 3, pp. 521–532, March 2015. [click]
Y. Jiang, Z. Liu, C. Chen, and Y. Zhang, “Adaptive robust fuzzy control for dual arm robot with unknown input deadzone nonlinearity,” Nonlinear Dynamics, vol. 81, pp. 1301–1314, August 2015. [click]
R. J. Wai and Z. W. Yang, “Adaptive fuzzy neural network control design via a T–S fuzzy model for a robot manipulator including actuator dynamics,” IEEE Transactions on Systems, Man, And Cybernetics–Part B: Cybernetics, vol. 38, no 5, pp.1326–1346, October 2008. [click]
C. S. Chen, “Dynamic structure neural–fuzzy networks for robust adaptive control of robot manipulators,” IEEE Transactions on Industrial Electronics, vol. 55, no. 9, pp. 3402–3414, September 2008. [click]
F. Caccavale, P. Chiacchio, A. Marino, and L. Villani, “Six–DOF impedance control of dual–arm cooperative manipulators,” IEEE/ASME Transactions on Mechatronics, vol. 13, no. 5, pp. 576–586. October 2008. [click]
D. Kruse, J. T. Wen, and R. J. Radke, “A sensor–based dual–arm tele–robotic system,” IEEE Transactions on Automation Science and Engineering, vol. 12, no. 1, pp.4–18, January 2015. [click]
D. Nicolis, A. M. Zanchettin, and P. Rocco, “Constraintbased and sensorless force control with an application to a lightweight dual–arm robot,” IEEE Robotics and Automation Letters, vol. 1, no. 1, pp. 340–347, January 2016. [click]
J. J. Slotine and W. Li, Applied Nonlinear Control, Prentice Hall, 1991.
P. A. Ioannou and J. Sun, Robust Adaptive Control, Prentice Hall, 1996.
D. W. Kim, J. B. Park, and Y. H. Joo, “Theoretical justification of approximate norm minimization method for intelligent digital redesign,” Automatica, vol. 44, no. 3, pp. 851–856, 2008, 03.
M. K. Song, J. B. Park, and Y. H. Joo, “Stability and stabilization for discrete–time Markovian jump fuzzy systems with time–varying delays; partially known transition probabilities case,” International Journal of Control, Automation, and Systems, vol. 11. no. 1, pp. 136–146, Feb. 2013. [click]
Author information
Authors and Affiliations
Corresponding author
Additional information
Recommended by Associate Editor Do Wan Kim under the direction of Editor Euntai Kim. This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MEST) (NRF-2015R1A2A2A05001610) and the Human Resources Development program (No. 20144030200590) of the Korea Institute of Energy Technology Evaluation and Planning (KETEP) grant funded by the Korea Government Ministry of Knowledge Economy.
Le Anh Tuan graduated both B.Eng. and M.Eng. in Mechanical Engineering and Marine Machinery from Vietnam Maritime University in 2003 and 2007, respectively. He received the Ph.D. degree in Mechanical Engineering from Kyung Hee University, Korea in 2012. He is currently an Associate Professor at Automotive Engineering Department of Vietnam Maritime University, Hai Phong, Vietnam. His interested research composes of Applied Nonlinear Control, Dynamics and Control of Industrial Machines.
Young Hoon Joo received the B.S., M.S., and Ph.D. degrees in Electrical Engineering from Yonsei University, Seoul, Korea, in 1982, 1984, and 1995, respectively. He worked with Samsung Electronics Company, Seoul, Korea, from 1986 to 1995, as a project manager. He was with the University of Houston, Houston, TX, from 1998 to 1999, as a visiting professor in the Department of Electrical and Computer Engineering. He is currently a professor in the Department of Control and Robotics Engineering, Kunsan National University, Korea. President for Korea Institute of Intelligent Systems (KIIS) (2008–2009) and is serving as the His major interest is mainly in the field of intelligent control, intelligent robot, human-robot interaction, windfarm control, power system stabilization, and intelligent surveillance systems. He served as Editor-in-Chief for the International Journal of Control, Automation, and Systems (IJCAS) (2014-present) and the Vice-President for the Korean Institute of Electrical Engineers (KIEE) (2013-present) and for Institute of Control, Automation, and Systems (ICROS) (2016-present). Also, he is serving as Director of Research Center of Wind Energy Systems funded by Korean Government (2016-present).
Pham Xuan Duong graduated both Engineer degree in Marine Engineering and then Master of Science in Marine Engineering from Vietnam Maritime University in 1994 and 1998, respectively. He received his PhD in “Synthesis of feedback law for non-linear models of propulsive system” from the Russian Academy of Science in 2006. He is currently a Vice President of Vietnam Maritime University since September 2009 until now and in-charge of the university’s academic affairs, scientific research, international relations and maritime education and training. He was an author and co-author of number of papers, which were published in the proceedings of IMLA, ISME, ISMT, AMFUF, IAMU, TranNav, etc. With his remarkable contribution to the mentioned above organizations, he was honorary elected as a Deputy Chair of LEC IAMU’s AGA 17, an Advisory Board Member of ISME 2015, 2017, an International Executive Board’s member of ISMT 2009, 2011, 2013. His interested research composes of Automation & Control, Reduction of ship emissions, educational management and administration.
Le Quoc Tien graduated as an Engineer in Marine Navigation and M. Eng. in Maritime Safety in 1994, 2005 from Vietnam Maritime University, respectively. He received a Ph.D. degree in Marine Control Engineering from the Russian Academy of Science. Since 1994, he has been with Vietnam Maritime University, where he is currently a Vice President. His research interests include Maritime Science, Ship Propulsion, and Ship Navigation.
Rights and permissions
About this article
Cite this article
Tuan, L.A., Joo, Y.H., Tien, L.Q. et al. Adaptive neural network second-order sliding mode control of dual arm robots. Int. J. Control Autom. Syst. 15, 2883–2891 (2017). https://doi.org/10.1007/s12555-017-0026-1
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12555-017-0026-1