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.
Similar content being viewed by others
References
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.
Jung, S. and Hsia, T. C.: Neural network inverse control techniques for PD controlled robot manipulator, Robotica 18 (2000), 305–314.
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.
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.
Luo, Z. W., Ito, K., Ito, M., and Kato, A.: On co-operative manipulation of dynamic objects, Adv. Robotics 10(6) (1996), 621–636.
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.
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.
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.
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.
Vemuri, A. T. and Polycarpou, M.: A methodology for fault diagnosis in robotic systems using neural networks, Robotica 22 (2004), 419–438.
Yıldırım, Ş.: Robot trajectory control using neural networks, IEE Electronics Lett. 38(19) (2002), 1111–1113.
Yıldırım, Ş: Adaptive robust neural controller for robots, Robotics Autonom. Systems 46(3) (2004), 175–184.
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.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Yildirim, Ş. A Proposed Hybrid Recurrent Neural Control System for Two Co-operating Robots. J Intell Robot Syst 42, 95–111 (2005). https://doi.org/10.1007/s10846-004-3027-2
Received:
Revised:
Issue Date:
DOI: https://doi.org/10.1007/s10846-004-3027-2