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Biohybrid control of general linear systems using the adaptive filter model of cerebellum

Wilson, Emma D.; Assaf, Tareq; Pearson, Martin J.; Rossiter, Jonathan M.; Dean, Paul; Anderson, Sean R.; Porrill, John

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Emma D. Wilson

Tareq Assaf

Jonathan M. Rossiter

Paul Dean

Sean R. Anderson

John Porrill


© 2015 Wilson, Assaf, Pearson, Rossiter, Dean, Anderson and Porrill. The adaptive filter model of the cerebellar microcircuit has been successfully applied to biological motor control problems, such as the vestibulo-ocular reflex (VOR), and to sensory processing problems, such as the adaptive cancelation of reafferent noise. It has also been successfully applied to problems in robotics, such as adaptive camera stabilization and sensor noise cancelation. In previous applications to inverse control problems, the algorithm was applied to the velocity control of a plant dominated by viscous and elastic elements. Naive application of the adaptive filter model to the displacement (as opposed to velocity) control of this plant results in unstable learning and control. To be more generally useful in engineering problems, it is essential to remove this restriction to enable the stable control of plants of any order. We address this problem here by developing a biohybrid model reference adaptive control (MRAC) scheme, which stabilizes the control algorithm for strictly proper plants. We evaluate the performance of this novel cerebellar-inspired algorithm with MRAC scheme in the experimental control of a dielectric electroactive polymer, a class of artificial muscle. The results show that the augmented cerebellar algorithm is able to accurately control the displacement response of the artificial muscle. The proposed solution not only greatly extends the practical applicability of the cerebellar-inspired algorithm, but may also shed light on cerebellar involvement in a wider range of biological control tasks.


Wilson, E. D., Assaf, T., Pearson, M. J., Rossiter, J. M., Dean, P., Anderson, S. R., & Porrill, J. (2015). Biohybrid control of general linear systems using the adaptive filter model of cerebellum. Frontiers in Neurorobotics, 9(JUL),

Journal Article Type Article
Acceptance Date Jun 29, 2015
Publication Date Jan 1, 2015
Deposit Date Jan 28, 2016
Publicly Available Date Apr 6, 2017
Journal Frontiers in Neurorobotics
Electronic ISSN 1662-5218
Publisher Frontiers Media
Peer Reviewed Peer Reviewed
Volume 9
Issue JUL
Keywords cerebellum, adaptive control, adaptive filter, electroactive polymer, artificial muscles, soft robotics
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