Browse > Article
http://dx.doi.org/10.5391/IJFIS.2008.8.3.185

Experimental Studies of Real- Time Decentralized Neural Network Control for an X-Y Table Robot  

Cho, Hyun-Taek (Intelligent Systems and Emotional Engineering(ISEE) Lab, BK21 Mechatronics Group Chungnam National University)
Kim, Sung-Su (Intelligent Systems and Emotional Engineering(ISEE) Lab, BK21 Mechatronics Group Chungnam National University)
Jung, Seul (Intelligent Systems and Emotional Engineering(ISEE) Lab, BK21 Mechatronics Group Chungnam National University)
Publication Information
International Journal of Fuzzy Logic and Intelligent Systems / v.8, no.3, 2008 , pp. 185-191 More about this Journal
Abstract
In this paper, experimental studies of a neural network (NN) control technique for non-model based position control of the x-y table robot are presented. Decentralized neural networks are used to control each axis of the x-y table robot separately. For an each neural network compensator, an inverse control technique is used. The neural network control technique called the reference compensation technique (RCT) is conceptually different from the existing neural controllers in that the NN controller compensates for uncertainties in the dynamical system by modifying desired trajectories. The back-propagation learning algorithm is developed in a real time DSP board for on-line learning. Practical real time position control experiments are conducted on the x-y table robot. Experimental results of using neural networks show more excellent position tracking than that of when PD controllers are used only.
Keywords
x-y table; neural network controller; RCT;
Citations & Related Records
연도 인용수 순위
  • Reference
1 S. Omatu and Y. Kishida and M. Yoshioka, 'Neuro-control for single-input multi-output systems' IEEE Conf. on Knowledge Based Intelligent Electronics Systems, pp. 202-205, 1998
2 S. S. Ge and C. Wang, 'Direct adaptive nn control of a class of nonlinear systems,' IEEE Trans. on Neural Networks, vol. 13, no. 1, pp. 214-221, 2002   DOI   ScienceOn
3 S. Jung and T. C. Hsia, 'On reference trajectory modification approach for Cartesian space neural network control of robot manipulators', Proc. of IEEE Conf. on Robotics and Automations, pp. 575-580, 1995
4 S. Jung and T. C. Hsia, 'A study of neural network control of robot manipulators', Robotica, Vol. 14, pp. 7-15, 1996   DOI
5 Y. C. Chang, 'Neural network based h tracking control for robotic systems,' IEE Proc. On Control Theory Applications, vol. 147, no. 3, pp. 303-311, 2000   DOI   ScienceOn
6 T. H. Lee and S. S. Ge, 'Intelligent control of mechatronics systems,' Proc. of IEEE Symposium on Intelligent Control, pp. 646-660, 2003
7 K. Narendra and K. Parthasarathy, 'Control of nonlinear dynamical systems using neural networks: controllability and stabilization,' IEEE Trans. on Neural Networks, vol. 4, no. 2, pp. 192-206, 1993   DOI   ScienceOn
8 S. Jung and T. C. Hsia, 'A new neural network control technique for robot manipulators', Robotica, Vol. 13, pp. 477-484, 1995   DOI   ScienceOn
9 H. Gomi and M. Kawato, 'Learning control for a closed loop system using feedback error learning,' Proc. of the IEEE International Conf. on Decision and Control, pp. 3289-3294, 1990
10 F. L. Lewis, S. Jagannathau, and A. Yesildirek, 'Neural network control of robot manipulators and nonlinear systems,' Taylor and Francis, 1999
11 T. C. Hsia, 'Robustness analysis of pd controller with approximate gravity compensation for robot manipulator control.' Journal of Robotic System, vol. 11, pp.517-521, 1994   DOI   ScienceOn
12 S. Jung and T. C. Hsia, 'Neural Network Inverse Control Techniques for PD Controlled Robot Manipulators' Robotica, pp. 461-455, Vol. 19, No. 3, 2000
13 J. Li and D. Wang, 'An NN controller and tracking error bound for robotic manipulators,' IEEE Proc. of Decision and Control, pp. 872-876, 2000
14 S. Omatu and T. Fujinaka and M. Yoshioka, 'Neuro-pid control for inverted single and double pendulums' IEEE Conference on Systems, Man, and Cybernetics, pp. 2685-2690, 2000
15 W. T. Miller, R. S. Sutton, and P. J. Werbos, 'Neural networks for Control', The MIT Press, 1991
16 F. L. Lewis, K. Liu, and A. Yesildirek, 'Neural net robot controller with guaranteed tracking performance', IEEE Symposium on Intelligent Control, pp. 225-231, 1993