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On-Line Self-Learning Neural Network Control for Articulated Pneumatic Robot Position System

[+] Author Affiliations
Yang Xue, Zhongliang Jing

Shanghai Jiao Tong University

Paper No. IMECE2005-80161, pp. 957-962; 6 pages
doi:10.1115/IMECE2005-80161
From:
  • ASME 2005 International Mechanical Engineering Congress and Exposition
  • Design Engineering, Parts A and B
  • Orlando, Florida, USA, November 5 – 11, 2005
  • Conference Sponsors: Design Engineering Division
  • ISBN: 0-7918-4215-0 | eISBN: 0-7918-3769-6
  • Copyright © 2005 by ASME

abstract

In this paper, a NNI (Neural Network Identifier) is presented to learn model for an articulated multiple DOF (Degrees of Freedom) pneumatic robot position system. It can adjust the weights and biases of NNC (Neural Network Controller) on line. This controller can effectively solve the difficult problems of single rod cylinders, which are mainly caused by asymmetric structures and different friction characteristics in two directions. On these bases an articulated four DOF pneumatic robot is designed and its work space is analyzed. Experimental results prove that, the dynamic performance of the system can be much improved. The system using NN (Neural Network) has strong self-adaptability and robustness. It obtains desired percentage overshoot and repeatability in steady-state responses.

Copyright © 2005 by ASME

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