Bioinspired adaptive control for artificial muscles

Authors:
Emma D. Wilson;Tareq Assaf;Martin J. Pearson;Jonathan M. Rossiter;Sean R. Anderson;John Porrill
Affiliations:
Sheffield Centre for Robotics (SCentRo), University of Sheffield, UK;Bristol Robotics Laboratory (BRL), University of the West of England, UK,University of Bristol, UK;Bristol Robotics Laboratory (BRL), University of the West of England, UK,University of Bristol, UK;Bristol Robotics Laboratory (BRL), University of the West of England, UK,University of Bristol, UK;Sheffield Centre for Robotics (SCentRo), University of Sheffield, UK;Sheffield Centre for Robotics (SCentRo), University of Sheffield, UK
Venue:
Living Machines'13 Proceedings of the Second international conference on Biomimetic and Biohybrid Systems
Year:
2013

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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.