International Journal of Fuzzy Logic and Intelligent Systems

Korean Institute of Intelligent Systems (한국지능시스템학회)
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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)
  • Published : 2008.09.01
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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.

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