Bioinspired Adaptive Control for Artificial Muscles

Emma Wilson, Tareq Assaf, Martin Pearson, Jonathan M Rossiter, Sean Anderson, John Porril

Research output: Chapter in Book/Report/Conference proceedingConference Contribution (Conference Proceeding)

14 Citations (Scopus)

Abstract

The new field of soft robotics offers the prospect of replacing existing hard actuator technologies by artificial muscles more suited to human-centred robotics. It is natural to apply biomimetic control strategies to the control of these actuators. In this paper a cerebellar-inspired controller is successfully applied to the real-time control of a dielectric electroactive actuator. To analyse the performance of the algorithm in detail we identified a time-varying plant model which accurately described actuator properties over the length of the experiment. Using synthetic data generated by this model we compared the performance of the cerebellar-inspired controller with that of a conventional adaptive control scheme (filtered-x LMS). Both the cerebellar and conventional algorithms were able to control displacement for short periods, however the cerebellar-inspired algorithm significantly outperformed the conventional algorithm over longer duration runs where actuator characteristics changed significantly. This work confirms the promise of biomimetic control strategies for soft-robotics applications.
Original languageEnglish
Title of host publicationLecture Notes in Computer Science
Pages311-322
Number of pages12
Volume8064
DOIs
Publication statusPublished - 29 Jul 2013

Structured keywords

  • Tactile Action Perception

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