Adaptive robust neural control of a two-manipulator system holding a rigid object with inaccurate base frame parameters

  • Fan Xu
  • Jin WangEmail author
  • Guo-dong Lu


The problem of self-tuning control with a two-manipulator system holding a rigid object in the presence of inaccurate translational base frame parameters is addressed. An adaptive robust neural controller is proposed to cope with inaccurate translational base frame parameters, internal force, modeling uncertainties, joint friction, and external disturbances. A radial basis function neural network is adopted for all kinds of dynamical estimation, including undesired internal force. To validate the effectiveness of the proposed approach, together with simulation studies and analysis, the position tracking errors are shown to asymptotically converge to zero, and the internal force can be maintained in a steady range. Using an adaptive engine, this approach permits accurate online calibration of the relative translational base frame parameters of the involved manipulators. Specialized robust compensation is established for global stability. Using a Lyapunov approach, the controller is proved robust in the face of inaccurate base frame parameters and the aforementioned uncertainties.

Key words

Cooperative manipulators Neural networks Inaccurate translational base frame Adaptive control Robust control 

CLC number



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  1. Aghili F, 2011. Self-tuning cooperative control of manipulators with position/orientation uncertainties in the closedkinematic loop. IEEE/RSJ Int Conf on Intelligent Robots and Systems, p.4187–4193. Google Scholar
  2. Aghili F, 2013. Adaptive control of manipulators forming closed kinematic chain with inaccurate kinematic model. IEEE/ASME Trans Mechatron, 18(5):1544–1554. CrossRefGoogle Scholar
  3. Cheah CC, Liu C, Slotine J, 2004. Approximate Jacobian adaptive control for robot manipulators. IEEE Int Conf on Robtics and Automation, p.3075–3080. Google Scholar
  4. Cheah CC, Liu C, Slotine JJE, 2006. Adaptive Jacobian tracking control of robots with uncertainties in kinematic, dynamic and actuator models. IEEE Trans Autom Contr, 51(6):1024–1029. MathSciNetCrossRefzbMATHGoogle Scholar
  5. Cheng L, Hou ZG, Tan M, 2009. Adaptive neural network tracking control for manipulators with uncertain kinematics, dynamics and actuator model. Automatica, 45(10):2312–2318. MathSciNetCrossRefzbMATHGoogle Scholar
  6. Corke P, 1996. A robotics toolbox for Matlab. IEEE Robot Autom Mag, 3(1):24–32. CrossRefGoogle Scholar
  7. Deng H, Wu H, Yang C, et al., 2015. Base frame calibration for multi-robot coordinated systems. IEEE Int Conf on Robotics and Biomimetics, p.1489–1494. Google Scholar
  8. Erhart S, Hirche S, 2013. Adaptive force/velocity control for multi-robot cooperative manipulation under uncertain kinematic parameters. IEEE/RSJ Int Conf on Intelligent Robots and Systems, p.307–314. Google Scholar
  9. Gan Y, Dai X., 2011. Base frame calibration for coordinated industrial robots. Robot Auton Syst, 59(7-8):563–570. CrossRefGoogle Scholar
  10. Gueaieb W, Al-Sharhan S, Bolic M, 2007a. Robust computationally efficient control of cooperative closed-chain manipulators with uncertain dynamics. Automatica, 43(5): 842–851. MathSciNetCrossRefzbMATHGoogle Scholar
  11. Gueaieb W, Karray F, Al-Sharhan S, 2007b. A robust hybrid intelligent position/force control scheme for cooperative manipulators. IEEE/ASME Trans Mechatron, 12(2):109–125. CrossRefGoogle Scholar
  12. Lewis F, Jagannathan S, Yesildirak A, 1998. Neural Network Control of Robot Manipulators and Non-linear Systems. CRC Press, France, p.1–468.Google Scholar
  13. Li Z, Xiao S, Ge SS, et al., 2015. Constrained multilegged robot system modeling and fuzzy control with uncertain kinematics and dynamics incorporating foot force optimization. IEEE Trans Syst Man Cybern Syst, 46(1):1–15. CrossRefGoogle Scholar
  14. Liu JF, Abdel-Malek K, 2000. Robust control of planar dualarm cooperative manipulators. Robot Comput-Integr Manuf, 16(2):109–119. CrossRefGoogle Scholar
  15. Liu YC, 2015. Distributed synchronization for heterogeneous robots with uncertain kinematics and dynamics under switching topologies. J Franklin Instit, 352(9):3808–3826. MathSciNetCrossRefzbMATHGoogle Scholar
  16. Liu YC, Khong MH, 2015. Adaptive control for nonlinear teleoperators with uncertain kinematics and dynamics. IEEE/ASME Trans Mechatron, 20(5):2550–2562. CrossRefGoogle Scholar
  17. Lizarralde F, Leite AC, Hsu L, et al., 2013. Adaptive visual servoing scheme free of image velocity measurement for uncertain robot manipulators. Automatica, 49(5):1304–1309. MathSciNetCrossRefzbMATHGoogle Scholar
  18. Mohajerpoor R, Rezaei M, Talebi A, et al., 2011. A robust adaptive hybrid force/position control scheme of two planar manipulators handling an unknown object interacting with an environment. Proc Instit Mech Eng Part I J Syst Contr Eng, 226(4):509–522. Google Scholar
  19. Namvar M, Aghili F, 2005. Adaptive force-motion control of coordinated robots interacting with geometrically unknown environments. IEEE Trans Robot, 21(4):678–694. CrossRefGoogle Scholar
  20. Panwar V, Kumar N, Sukavanam N, et al., 2012. Adaptive neural controller for cooperative multiple robot manipulator system manipulating a single rigid object. Appl Soft Comput, 12(1):216–227. CrossRefGoogle Scholar
  21. Park IW, Lee BJ, Cho SH, et al., 2012. Laser-based kinematic calibration of robot manipulator using differential kinematics. IEEE/ASME Trans Mechatron, 17(6):1059–1067. CrossRefGoogle Scholar
  22. Park J, Sandberg IW, 1991. Universal approximation using radial-basis-function networks. Neur Comput, 3(2):246–257. CrossRefGoogle Scholar
  23. Parra-Vega V, Arimoto S, Liu YH, et al., 2003. Dynamic sliding pid control for tracking of robot manipulators: theory and experiments. IEEE Trans Robot Autom, 19(6):967–976. CrossRefGoogle Scholar
  24. Su CY, Stepanenko Y, 1995. Adaptive sliding mode coordinated control of multiple robot arms attached to a constrained object. IEEE Trans Syst Man Cybern, 25(5):871–878. CrossRefGoogle Scholar
  25. Szewczyk J, Plumet F, Bidaud P, 2002. Planning and controlling cooperating robots through distributed impedance. J Robot Syst, 19(6):283–297. CrossRefzbMATHGoogle Scholar
  26. Tavasoli A, Eghtesad M, Jafarian H, 2009. Two-time scale control and observer design for trajectory tracking of two cooperating robot manipulators moving a flexible beam. Robot Auton Syst, 57(2):212–221. CrossRefGoogle Scholar
  27. Zhang YH, Wei W, Dan YU, et al., 2011. A tracking and predicting scheme for ping pong robot. J Zhejiang Univ-Sci C (Comput & Electron), 12(2):110–115. CrossRefGoogle Scholar
  28. Zhao D, Li S, Zhu Q, 2014a. Adaptive synchronised tracking control for multiple robotic manipulators with uncertain kinematics and dynamics. Int J Syst Sci, 47(4):1–14. MathSciNetGoogle Scholar
  29. Zhao D, Ni W, Zhu Q, 2014b. A framework of neural networks based consensus control for multiple robotic manipulators. Neurocomputing, 140:8–18. CrossRefGoogle Scholar
  30. Zhao D, Zhu Q, Li N, et al., 2014c. Synchronized control with neuro-agents for leader–follower based multiple robotic manipulators. Neurocomputing, 124:149–161. CrossRefGoogle Scholar
  31. Zribi M, Karkoub M, Huang L, 2000. Modelling and control of two robotic manipulators handling a constrained object. Appl Math Model, 24(12):881–898. CrossRefzbMATHGoogle Scholar

Copyright information

© Editorial Office of Journal of Zhejiang University Science and Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.State Key Laboratory of Fluid Power and Mechatronic SystemsZhejiang UniversityHangzhouChina

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