Advertisement

Journal of Intelligent and Robotic Systems

, Volume 42, Issue 1, pp 95–111 | Cite as

A Proposed Hybrid Recurrent Neural Control System for Two Co-operating Robots

  • Şahin Yildirim
Article

Abstract

This paper discusses a model refernce adaptive (MRAC) position/force controller using proposed neural networks for two co-operating planar robots. The proposed neural network is a recurrent hybrid network. The recurrent networks have feedback connections and thus an inherent memory for dynamics, which makes them suitable for representing dynamic systems. A feature of the networks adopted is their hybrid hidden layer, which includes both linear and nonlinear neurons. On the other hand, the results of the case of a single robot under position control alone are presented for comparison. The results presented show the superior ability of the proposed neural network based model reference adaptive control scheme at adapting to changes in the dynamics parameters of robots.

Keywords

co-operating robots neural network hybrid control 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Fukuda, O., Tsuji, T., Kaneko, M., and Otsuka, A.: A human-assisting manipulator teleoperated by EMG signals and arm motions, IEEE Trans. Robotics Automat. 19(2) (2003), 210–222. Google Scholar
  2. 2.
    Jung, S. and Hsia, T. C.: Neural network inverse control techniques for PD controlled robot manipulator, Robotica 18 (2000), 305–314. Google Scholar
  3. 3.
    Li, Q., Poo, A. N., and Ang, M.: An enhanced computed-torque control scheme for robot manipulators with a neuro-compensator, in: IEEE Internat. Conf. on Systems, Man and Cybernetics, Vol. 1, Canada, 1995, pp. 56–60. Google Scholar
  4. 4.
    Luh, J. Y. S. and Zheng, Y. F.: Constrained relation between two co-ordinated industrial robots for motion control, Internat. J. Robotics Res. 6(3) (1987), 60–70. Google Scholar
  5. 5.
    Luo, Z. W., Ito, K., Ito, M., and Kato, A.: On co-operative manipulation of dynamic objects, Adv. Robotics 10(6) (1996), 621–636. Google Scholar
  6. 6.
    Nakamura, Y., Nagai, K. and Yoshikawa, T.: Dynamics and stability in co-ordination of multiple robotic mechanisms, Internat. J. Robotics Res. 8(2) (1989), 44–61. Google Scholar
  7. 7.
    Tao, J. M. and Luh, J. Y. S.: Robust position/force control of multiple robots using neural networks, Math. Computer Modelling 21(1/2) (1995), 119–131. Google Scholar
  8. 8.
    Tzafestas, C. S., Prokopiou, P. A., and Tzafestas, S. G.: Path planning and control of a co-operative three-robot system manipulating large objects, J. Intelligent Robotic Systems 22(2) (1998), 99–116. Google Scholar
  9. 9.
    Uchiyama, M. and Dauchez, P.: A symmetric hybrid position/force control scheme for the co-ordination of two robots, in: Proc. IEEE Internat. Conf. on Robotics and Automation, Vols 1–3, Philadelphia, PA, 1988, pp. 350–356. Google Scholar
  10. 10.
    Vemuri, A. T. and Polycarpou, M.: A methodology for fault diagnosis in robotic systems using neural networks, Robotica 22 (2004), 419–438. Google Scholar
  11. 11.
    Yıldırım, Ş.: Robot trajectory control using neural networks, IEE Electronics Lett. 38(19) (2002), 1111–1113. Google Scholar
  12. 12.
    Yıldırım, Ş: Adaptive robust neural controller for robots, Robotics Autonom. Systems 46(3) (2004), 175–184. Google Scholar
  13. 13.
    Zribi, M., Ahmad, S., and Luo, S. W.: Adaptive control of redundant multiple robots in co-operative motion, J. Intelligent Robotic Systems 17(2) (1996), 169–194. Google Scholar

Copyright information

© Kluwer Academic Publishers 2005

Authors and Affiliations

  1. 1.Engineering FacultyErciyes UniversityKayseriTurkey

Personalised recommendations