Skip to main content
SpringerLink
Log in

Neural network based hybrid force/position control for robot manipulators

  • Published:
International Journal of Precision Engineering and Manufacturing Aims and scope Submit manuscript

Abstract

This paper presents a neural network based adaptive control scheme for hybrid force/position control for rigid robot manipulators. Firstly the robot dynamics is decomposed into force, position and redundant joint subspaces. Based on this decomposition, a neural network based controller is proposed that achieves the stability in the sense of Lyapunov for desired interaction force between the end-effector and the environment as well as regulate robot tip position in cartesian space. A feedforward neural network is employed to learn the parametric uncertainties, existing in the dynamical model of the robot manipulator. Finally numerical simulation studies are carried out for a two link rigid robot manipulator.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Raibert, M. H. and Craig, J. J., “Hybrid Position/Force Control of Manipulators,” ASME Journal of Dynamical Systems, Measurement and Control, Vol. 103, No. 2, pp. 126–133, 1981.

    Article  Google Scholar 

  2. Lozano, R. and Brogliato, B., “Adaptive Hybrid Position/Force Control of Redundant Manipulators,” IEEE Transactions on Automatic Control, Vol. 37, No. 10, pp. 1501–1505, 1992.

    Article  MathSciNet  MATH  Google Scholar 

  3. Yoshikawa, T. and Sudou, A., “Dynamic Hybrid Position/Force Control of Robot Manipulators: Online-Estimation of Unknown Constraint,” IEEE Transactions on Robotics and Automation, Vol. 9, No. 2, pp. 220–226, 1993.

    Article  Google Scholar 

  4. Kwan, C. M., “Robust Adaptive Force/Motion Control of Constrained Robots,” IEE Proc.-Control Theory and Applications, Vol. 143, No. 1, pp. 103–109, 1996.

    Article  MATH  Google Scholar 

  5. De Queiroz, M. S., Jun, H. and Dawson, D. M., “Adaptive Position/Force Control of Robot Manipulators without Velocity Measurements: Theory and Experimentation,” IEEE Transactions on Systems, Man and Cybernetics-Part B: Cybernetics, Vol. 27, No. 5, pp. 796–809, 1997.

    Article  Google Scholar 

  6. Xiao, D., Ghosh, B. K., Ning, X. and Tarn, T. J., “Dynamic Hybrid Position/Force Control of a Robot Manipulator in an uncalibrated environment,” IEEE Transactions on Control Systems Technology, Vol. 8, No. 4, pp. 635–645, 2000.

    Article  Google Scholar 

  7. Kouya, D. N., Saad, M. and Lamarche, L., “Backstepping Adaptive Hybrid Force/Position Control for Robotic Manipulators,” Proc. of the American Control Conference, pp. 4595–4600, 2002.

  8. Roy, J. and Whitcomb, L. L., “Adaptive Force Control of Position/Velocity Controlled Robots: Theory and Experiment,” IEEE Transactions on Robotics and Automation, Vol. 18, No. 2, pp. 121–137, 2002.

    Article  Google Scholar 

  9. Cheah, C. C., Zhao, Y. and Slotine, J. J. E., “Adaptive Jacobian Motion and Force Control for Constrained Robots with Uncertainties,” Proc. of the International Conference on Robotics and Automation, pp. 2226–2231, 2006.

  10. Kang, S., Komoriya, K., Yokoi, K., Koutoku, T., Kim, B. and Park, S., “Control of Impulsive Contact Force Between Mobile Manipulator and Environment Using Effective Mass and Damping Controls,” Int. J. Precis. Eng. Manuf., Vol. 11, No. 5, pp. 697–704, 2010.

    Article  Google Scholar 

  11. Lewis, F. L., Liu, K. and Yesildirek, A., “Neural Net Robot Controller with Guaranted Tracking Performance,” IEEE Transactions on Neural Networks, Vol. 6, No. 3, pp. 703–715, 1995.

    Article  Google Scholar 

  12. Lewis, F. L., Yesildirek, A. and Liu, K., “Multilayer Neural Net Robot Controller with Guaranted Tracking Performance,” IEEE Transactions on Neural Networks, Vol. 7, No. 2, pp. 388–399, 1996.

    Article  Google Scholar 

  13. Lewis, F. L., Jagannathan, S. and Yesildirek, A., “Neural Networks Control of Robot Manipulators and Nonlinear Systems,” Taylor and Francis, 1999.

  14. Shenghai, H., Marcelo, H. A. and Krishnan, H., “Online Neural Networks Compersator for Constrained Robot Manipulators,” Proc. of the 3rd Asian Control Conference, pp. 1621–1627, 2000.

  15. Karayiannidis, Y., Rovithakis, K. and Doulgeri, Z., “Force/Position Tracking for a Robotic Manipulator in compliant contact with a surface using Neuro-adaptive Control,” Automatica, Vol. 43, No. 7, pp. 1281–1288, 2007.

    Article  MathSciNet  MATH  Google Scholar 

  16. Panwar, V. and Sukavanam, N., “Design of Optimal Hybrid Position/Force Controller for a Robot Manipulator using Neural Networks,” Mathematical Problems in Engineering, Vol. 23, Article ID. 65028, 2007.

  17. Zhao, Y. and Cheah, C. C., “Vision Based Neural Network Control for Constrained Robots with Constraint Uncertainty,” IET Control Theory and Applications, Vol. 2, No. 10, pp. 906–916, 2008.

    Article  MathSciNet  Google Scholar 

  18. Hornic, K., Stinchcombe, M. and White, H., “Multilayers Feedforward Networks are Universal Approximators,” Neural Networks, Vol. 2, No. 5, pp. 359–366, 1989.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Division of Mechanical Engineering, Ajou University, San5, Woncheon-dong, Yeongtong-gu, Suwon, South Korea, 443-749

    Naveen Kumar & Jin-Hwan Borm

  2. Department of Applied Mathematics, Defence Institute of Advanced Technology, Pune, 411025, Maharashtra, India

    Vikas Panwar

  3. Department of Mathematics, Indian Institute of Technology Roorkee, Roorkee, 247667, Uttrakhand, India

    Nagarajan Sukavanam & Shri Prakash Sharma

Authors
  1. Naveen Kumar
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Vikas Panwar
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Nagarajan Sukavanam
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Shri Prakash Sharma
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Jin-Hwan Borm
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Naveen Kumar.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Kumar, N., Panwar, V., Sukavanam, N. et al. Neural network based hybrid force/position control for robot manipulators. Int. J. Precis. Eng. Manuf. 12, 419–426 (2011). https://doi.org/10.1007/s12541-011-0054-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12541-011-0054-3

Keywords

Search

Navigation