Wilson, E. D., Assaf, T., Pearson, M. J et al. (4 more authors) (2014) Soft Robotics: Cerebellar Inspired Control of Artificial Muscles. In: The University of Sheffield Engineering Symposium Conference Proceedings Vol. 1. USES 2014 - The University of Sheffield Engineering Symposium, 24 June 2014, The Octagon Centre, University of Sheffield.
Abstract
Soft robots have the potential to greatly improve human-robot interaction via intrinsically safe, compliant designs. However, new compliant materials used in soft robotics – artificial muscles – are fabricated with poor tolerances and have time-varying dynamics. Therefore, a key technical challenge is to develop adaptive control algorithms for these materials. Here, we take a novel bio-inspired approach to artificial muscle control using the adaptive filter model of the cerebellum. The cerebellum is a brain structure essential for fine-tuning human performance in a diverse range of sensory and motor tasks. Its ability to automatically calibrate and adapt to changes in a wide variety of systems using a homogenous, repeating structure suggests that cerebellar-inspired models are highly suited to controlling artificial muscles in a range of tasks. We investigate the performance of the cerebellar adaptive filter algorithm in the displacement control of a soft actuator. Experimental results demonstrate that the cerebellar algorithm is successful and learns to accurately control the time-varying dynamics of the soft actuator in real-time.
Metadata
Item Type: | Proceedings Paper |
---|---|
Authors/Creators: |
|
Keywords: | Artificial Muscle; Adaptive Control; Cerebellum |
Dates: |
|
Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > USES (University of Sheffield Engineering Symposium) |
Depositing User: | Repository Officer |
Date Deposited: | 15 Apr 2015 13:44 |
Last Modified: | 21 Apr 2015 10:27 |
Status: | Published |
Identification Number: | 10.15445/01012014.139 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:85063 |