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Neural Network Control of a Flexible Link Manipulator in Contact With a Compliant Environment

[+] Author Affiliations
Reza Sabzehgar, Mehrdad Moallem

Simon Fraser University, Surrey, BC, Canada

Paper No. IMECE2010-37402, pp. 555-560; 6 pages
  • ASME 2010 International Mechanical Engineering Congress and Exposition
  • Volume 8: Dynamic Systems and Control, Parts A and B
  • Vancouver, British Columbia, Canada, November 12–18, 2010
  • Conference Sponsors: ASME
  • ISBN: 978-0-7918-4445-8
  • Copyright © 2010 by ASME


A neural network controller for regulating the contact force of a flexible link manipulator in contact with a compliant environment is proposed in this paper. The dynamic model of a single-link flexible (SLF) manipulator is obtained using three rigid sub-links connected by two virtual springs. It is assumed that the length of each link is short enough to be considered as a rigid link. A neural network-based control strategy is then proposed to relax the a-priori knowledge of the model parameters of the flexible link manipulator. The weights of the neural network controller are adjusted to minimize the error between the actual contact force and desired force. To overcome the non-minimum phase characteristic of the system, a weighted term of input signal is added to controller’s cost function. Simulation results are presented to evaluate performance of the proposed controller.

Copyright © 2010 by ASME



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