Skip to main content
SpringerLink
Log in

The Application of Connectionist Structures to Learning Impedance Control in Robotic Contact Tasks

  • Published:
Applied Intelligence Aims and scope Submit manuscript

Abstract

The goal of this paper is to consider the synthesis of learning impedance control using recurrent connectionist structures for on-line learning of robot dynamic uncertainties in the case of robot contact tasks. The connectionist structures are integrated in non-learning impedance control laws that are intended to improve the transient dynamic response immediately after the contact. The recurrent neural network as a part of hybrid learning control algorithms uses fast learning rules and available sensor information in order to improve the robotic performance progressively for a minimum possible number of learning epochs. Some simulation results of deburring process with the MANUTEC r3 robot are presented here in order to verify the effectiveness of the proposed control learning algorithms.

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. D.E. Whitney, “Historical perspective and state of the art in robot force control,” International Journal of Robotics Research, vol. 6,no. 1, pp. 3–14, 1987.

    Google Scholar 

  2. M. Vukobratović and D. Šurdilović, “Control of robotic systems in contact tasks, an overview,” in Force and Contact Control in Robotic Systems: A Historical Perspective and Current Technologies, Tutorial Proceedings of the IEEE International Conference on Robotics and Automation, Atlanta, May 1993, pp. 13–32.

  3. N. Hogan, “Impedance control: An approach to manipulation, Part 1.—Theory, Part 2.—Implementation, Part 3.—Application,” ASME Journal of Dynamic Systems, Measurement and Control, vol. 107, pp. 1–24, 1985.

    Google Scholar 

  4. L. Daneshmend, V. Hayward, and M. Pelletier, “Adaptation to environment stiffness in the control of manipulators,” in Experimental Robotics I, edited by Hayward and Khatib, Springer-Verlag: Berlin, pp. 150–156, 1989.

    Google Scholar 

  5. L. Ziren and A.A. Goldenberg, “Implementation of robust impedance and force control using vss theory,” Robotics and Manufacturing—Recent Trends in Research, Education and Application, ASME Press Series, vol. 3, pp. 279–285, 1991.

  6. D.M. Dawson, F.L. Lewis, and J.F. Dorsey, “Robust force control of a robot manipulator,” The Internal Journal of Robotic Research, vol. 11,no. 4, pp. 312–319, 1992.

    Google Scholar 

  7. S. Chen, W. Harwin, and T. Rahman, “The application of discrete-time adaptive impedance control to rehabilitation robot manipulators,” in Proceedings of the IEEE International Conference on Robotics and Automation, San Diego, May 1994, pp. 636–642.

  8. G.J. Liu and A.A. Goldenberg, “Robust hybrid impedance control of robot manipulators,” in Proceedings of the IEEE International Conference on Robotics and Automation, Sacramento, April 1991, pp. 287–292.

  9. W.-S. Lu and Q.-H. Meng, “Impedance control with adaptation for robotic manipulators,” IEEE Transactions on Robotics and Automation, vol. 7,no. 3, pp. 408–415, June 1991.

    Google Scholar 

  10. R. Colbaugh, H. Seraji, and K. Glass, “Direct adaptive impedance control of manipulators,” Journal of Robotic Systems, vol. 10,no. 2, pp. 217–248, 1992.

    Google Scholar 

  11. D. Katić and M. Vukobratović, “Connectionist approaches to the control of manipulation robots at the executive hierarchical level: An overview,” Journal of Intelligent and Robotic Systems, vol. 10, pp. 1–36, 1994.

    Google Scholar 

  12. S.-Y. Kung and J.-N. Hwang, “Neural network architectures for robotic applications,” IEEE Transactions on Robotics and Automation, vol. 5,no. 5, pp. 641–657, Oct. 1989.

    Google Scholar 

  13. S.H. Huang and H.-C. Zhang, “Artificial neural networks in manufacturing: Concepts, applications, and perspectives,” IEEE Transactions on Components, Packaging, and Manufacturing Technology—Part A, vol. 17,no. 2, pp. 212–228, June 1994.

    Google Scholar 

  14. B. Horne, M. Jamshidi, and N. Vadie, “Neural networks in robotics: A survey,” Journal of Intelligent and Robotic Systems, vol. 3,no. 1, pp. 51–66, 1990.

    Google Scholar 

  15. T. Fukuda, T. Shibata, M. Tokita, and T. Mitsuoka, “Adaptation and learning by neural network for robotic manipulator,” in Proceedings of the IMACS International Symposium on Mathematical and Intelligent Models in System Simulation, Brussels, Sept. 1990.

  16. H.-L. Pei, Q.-J. Zhou, and T.P. Leung, “A neural network robot force controller,” in Proceedings of the 1992 IEEE/RSJ International Conference on Intelligent Robots and Systems, Raleigh, July 1992, pp. 1974–1979.

  17. J.M. Tao and J.Y.S. Luh, “Application of neural network with real-time training to robust position/force control of multiple robots,” in Proceedings of the IEEE International Conference on Robotics and Automation, Atlanta, May 1993, pp. 142–148.

  18. D. Jeon and M. Tomizuka, “Learning hybrid force and position control of robot manipulators,” in Proceedings of the IEEE International Conference on Robotics and Automation, Nice, May 1992, pp. 1455–1460.

  19. S. Arimoto and T. Naniwa, “Learning control for robot tasks under geometric endpoint constraints,” in Proceedings of the IEEE International Conference and Robotics and Automation, Nice, May 1992, pp. 1914–1919.

  20. V. Gullapali, R. Grupen, and A. Barto, “Learning reactive admittance control,” in Proceedings of the IEEE International Conference on Robotics and Automation, Nice, May 1992, pp. 1475–1481.

  21. V. Gullapali, A. Barto, and R. Grupen, “Learning admittance mappings for force-guided assembly,” in Proceedings of the IEEE International Conference on Robotics and Automation, San Diego, May 1994, pp. 2633–2638.

  22. M. Cohen and T. Flash, “Learning impedance parameters for robot control using an associative search network,” IEEE Transactions on Robotics and Automation, vol. 7,no. 3, pp. 382–390, June 1991.

    Google Scholar 

  23. H. Asada and Y. Asari, “The direct teaching of tool manipulation skills via the impedance identification of human motions,” in Proceedings of the IEEE International Conference on Robotics and Automation, Philadelphia, April 1988, pp. 1269–1274.

  24. H. Asada and S. Liu, “Transfer of human skills to neural net robot controllers,” in Proceedings of the IEEE International Conference on Robotics and Automation, Sacramento, April 1991, pp. 2442–2448.

  25. D. Katić and M. Vukobratović, “Decomposed connectionist architecture for fast and robust learning of robot dynamics,” in Proceedings of the IEEE International Conference on Robotics and Automation, Nice, May 1992, pp. 2064–2069.

  26. M. Vukobratović and D. Katić, “Connectionist control structures for high-efficiency learning in robotics,” in Applied Control, edited by S. Tzafestas, Marcel Dekker Inc.: New York, pp. 705–753, 1993.

    Google Scholar 

  27. M. Vukobratović and D. Katić, “Connectionist learning control algorithms for contact tasks in industrial robotics,” in Proceedings of the 24th International Symposium on Industrial Robots, Tokyo, Nov. 1993.

  28. D. Katić and M. Vukobratović, “Highly efficient robot dynamics learning by decomposed connectionist feedforward control structure,” IEEE Transactions on Systems, Man and Cybernetics, vol. 25,no. 1, pp. 145–158, 1995.

    Google Scholar 

  29. F. Arai, T. Tanaka, and T. Fukuda, “Recurrent neural network modeling and learning control of flexible plates by nonlinear handling system,” in Proceedings of the 1994 IEEE International Conference on Robotics and Automation, San Diego, May 1994, pp. 2070–2075.

  30. M. Vukobratović and Y. Ekalo, “New approach to the control of manipulation robots interacting with dynamic environment,” Robotica, vol. 14, pp. 31–39, 1996.

    Google Scholar 

  31. D.A. Lawrence, “Impedance control stability properties in common implementations,” in Proceedings of the IEEE International Conference on Robotics and Automation, Philadelphia, April 1988, pp. 1185–1190.

  32. M. Vukobratović, Y. Ekalo, and A. Rodić, “Impedance control as a particular case of the unified approach to the control of robots interacting with dynamic known environment” (submitted for publication in International Journal on Robotics and Intelligent Systems).

  33. R. Kelly, R. Carelli, M. Amestegui, and R. Ortega, “On adaptive impedance control of robot manipulators,” in Proceedings of the IEEE International Conference on Robotics and Automation, Scottsdale, May 1989, pp. 572–577.

  34. T. Ozaki, T. Suzuki, T. Furuhashi, S. Okuma, and Y. Uchikawa, “Trajectory control of robotic manipulators using neural networks,” IEEE Transactions on Industrial Electronics, vol. 38,no. 3, pp. 195–202, 1991.

    Google Scholar 

  35. D. Katić and M. Vukobratović, “Learning impedance control of manipulation robots by feedforward connectionist structures,” in Proceedings of the IEEE International Conference on Robotics and Automation, San Diego, May 1994, pp. 45–50.

  36. E.D. Sontag, “Neural nets as system models and controllers,” in Proceedings of the 7th Yale Workshop on Adaptive and Learning systems, Yale University, 1992, pp. 73–79.

  37. E.D. Sontag, “Neural networks for control,” in Essays on Control: Perspectived in the Theory and its Applications, edited by H.L. Trentelman and J.C. Williems, Birkhauser: Boston, pp. 339–380, 1993.

    Google Scholar 

  38. S. Turk and M. Otter, “The DFVLR Models 1 and 2 of the Manutec r3 robot,” Robotersysteme, vol. 3, pp. 753–768, 1987 (in German).

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Robotics Department, Mihailo Pupin Institute, 11000, Belgrade, Yugoslavia

    Dusko Katić & Miomir Vukobratović

Authors
  1. Dusko Katić
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Miomir Vukobratović
    View author publications

    You can also search for this author in PubMed Google Scholar

Rights and permissions

Reprints and permissions

About this article

Cite this article

Katić, D., Vukobratović, M. The Application of Connectionist Structures to Learning Impedance Control in Robotic Contact Tasks. Applied Intelligence 7, 315–326 (1997). https://doi.org/10.1023/A:1008213504051

Download citation

  • Issue Date:

  • DOI: https://doi.org/10.1023/A:1008213504051

Search

Navigation