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
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.
This work is supported by National Natural Science Foundation of China (60375017).
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Wickelgren, I.: The cerebellum: The Brain’s Engine of Agility. Science 281, 1588–1590 (1998)
Wolpert, D.M., Miall, R.C., Kawato, M.: Internal Models in the Cerebellum. Trends Cognitive Sciences 2, 338–347 (1998)
Marr, D.: A Theory of Cerebellar Cortex. Journal of physiology 202, 437–470 (1969)
Ito, M.: Cerebellum and Neural Control. Raven Press, New York (1984)
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)
Kawato, M., Gomi, H.: A Computational Model of Four Regions of the Cerebellum Based on Feedback Error Learning. Biological Cybernetics 69, 95–103 (1992)
Doya, K., Kimura, H., Kawato, M.: Neural Mechanisms of Learning and Control. Control Systems Magazine. IEEE 4, 42–54 (2001)
Paulin, M.G.: The Role of the Cerebellum in Motor Control and Perception. Brain Behavior and Evolution 41(1), 39–50 (1993)
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)
Davis, M.H.A., Vinter, R.B.: Stochastic Modelling and Control. Chapman and Hall, London (1985)
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)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Liu, L., Yu, N., Ding, M., Ruan, X. (2006). A Cerebellar Feedback Error Learning Scheme Based on Kalman Estimator for Tracing in Dynamic System. In: Wang, J., Yi, Z., Zurada, J.M., Lu, BL., Yin, H. (eds) Advances in Neural Networks - ISNN 2006. ISNN 2006. Lecture Notes in Computer Science, vol 3971. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11759966_73
Download citation
DOI: https://doi.org/10.1007/11759966_73
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-34439-1
Online ISBN: 978-3-540-34440-7
eBook Packages: Computer ScienceComputer Science (R0)