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

Research output: Contribution to journalJournal articlepeer-review

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  • Emma Denise Wilson
  • Tareq Assaf
  • Martin J Pearson
  • Jonathan Rossiter
  • Paul Dean
  • Sean R Anderson
  • John Porrill
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Article number5
<mark>Journal publication date</mark>2015
<mark>Journal</mark>Frontiers in neurorobotics
Issue number5
Volume9
Number of pages13
Publication StatusPublished
<mark>Original language</mark>English

Abstract

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.