Stability and convergence of neurologic model based robotic controllers

M. Kemal Ciliz, Can Isik

Research output: Chapter in Book/Report/Conference proceedingConference contribution

7 Scopus citations

Abstract

The authors investigate the local convergence properties of an artificial-neural-network (ANN)-based learning controller, using linearization techniques. The controller utilizes generic multilayer ANNs to adaptively approximate the manipulator dynamics over a specified region of the state space for a given desired trajectory. This generic neural network structure can be viewed as a nonlinear extension of a deterministic auto-regressive model which is commonly used in model matching problems for linear systems.

Original languageEnglish (US)
Title of host publicationProceedings - IEEE International Conference on Robotics and Automation
PublisherIEEE Computer Society
Pages2051-2056
Number of pages6
ISBN (Print)0818627204
StatePublished - Dec 1 1992
Externally publishedYes
EventProceedings of the 1992 IEEE International Conference on Robotics and Automation - Nice, Fr
Duration: May 12 1992May 14 1992

Publication series

NameProceedings - IEEE International Conference on Robotics and Automation
Volume3

Other

OtherProceedings of the 1992 IEEE International Conference on Robotics and Automation
CityNice, Fr
Period5/12/925/14/92

ASJC Scopus subject areas

  • Software
  • Control and Systems Engineering
  • Artificial Intelligence
  • Electrical and Electronic Engineering

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  • Cite this

    Ciliz, M. K., & Isik, C. (1992). Stability and convergence of neurologic model based robotic controllers. In Proceedings - IEEE International Conference on Robotics and Automation (pp. 2051-2056). (Proceedings - IEEE International Conference on Robotics and Automation; Vol. 3). IEEE Computer Society.