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A Cerebellar Feedback Error Learning Scheme Based on Kalman Estimator for Tracing in Dynamic System

  • Liang Liu
  • Naigong Yu
  • Mingxiao Ding
  • Xiaogang Ruan
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3971)

Abstract

Motivated by recent physiological and anatomical evidence, a new feedback error learning scheme is proposed for tracing in motor control system. In the scheme, the model of cerebellar cortex is regarded as the feedforward controller. Specifically, a neural network and an estimator are adopted in the cerebellar cortex model which can predict the future state and eliminate faults caused by time delay. Then the new scheme was used to control inverted pendulum. The simulation experimental results show that the new scheme can learn to control the inverted pendulum for tracing successfully.

Keywords

Purkinje Cell Cerebellar Cortex Inverted Pendulum Parallel Fiber Climb Fiber 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Wickelgren, I.: The cerebellum: The Brain’s Engine of Agility. Science 281, 1588–1590 (1998)CrossRefGoogle Scholar
  2. 2.
    Wolpert, D.M., Miall, R.C., Kawato, M.: Internal Models in the Cerebellum. Trends Cognitive Sciences 2, 338–347 (1998)CrossRefGoogle Scholar
  3. 3.
    Marr, D.: A Theory of Cerebellar Cortex. Journal of physiology 202, 437–470 (1969)Google Scholar
  4. 4.
    Ito, M.: Cerebellum and Neural Control. Raven Press, New York (1984)Google Scholar
  5. 5.
    Tamada, T., Miyauchi, S., Imamizu, H., Yoshioka, T., Kawato, M.: Activation of the Cerebellum in Grip Force and Load Force Coordination: an fMRI Study. Neuroimage 6, S492 (1999)Google Scholar
  6. 6.
    Kawato, M., Gomi, H.: A Computational Model of Four Regions of the Cerebellum Based on Feedback Error Learning. Biological Cybernetics 69, 95–103 (1992)CrossRefGoogle Scholar
  7. 7.
    Doya, K., Kimura, H., Kawato, M.: Neural Mechanisms of Learning and Control. Control Systems Magazine. IEEE 4, 42–54 (2001)CrossRefGoogle Scholar
  8. 8.
    Paulin, M.G.: The Role of the Cerebellum in Motor Control and Perception. Brain Behavior and Evolution 41(1), 39–50 (1993)CrossRefGoogle Scholar
  9. 9.
    Schweighofer, N., Arib, M.A., Dominey, P.F.: A Model of Cerebellum in Adaptive Control of Saccadic Gain. I. The Model and Its Biological Substrate. Biological Cybernetics 75, 19–28 (1996)MATHCrossRefGoogle Scholar
  10. 10.
    Davis, M.H.A., Vinter, R.B.: Stochastic Modelling and Control. Chapman and Hall, London (1985)MATHGoogle Scholar
  11. 11.
    Luo, Z.W., Fujii, S., Saitoh, Y., Muramatsu, E., Watanabe, K.: Feedback-error Learning for Explicit Force Control of a Robot Manipulator Interacting with Unknown Dynamic Environment. In: Proceedings of the IEEE International Conference on Robotics and Biomimetics 2004, August 2004, p. 448 (2004)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Liang Liu
    • 1
  • Naigong Yu
    • 1
  • Mingxiao Ding
    • 1
  • Xiaogang Ruan
    • 1
  1. 1.School of Electronic Information and Control EngineeringBeijing University of TechnologyBeijingP.R. China

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