Cerebellar-inspired algorithm for adaptive control of nonlinear dielectric elastomer-based artificial muscle
- Submitting institution
-
The University of Lancaster
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 258330825
- Type
- D - Journal article
- DOI
-
10.1098/rsif.2016.0547
- Title of journal
- Interface
- Article number
- 20160547
- First page
- -
- Volume
- 13
- Issue
- 122
- ISSN
- 1742-5689
- Open access status
- Compliant
- Month of publication
- September
- Year of publication
- 2016
- URL
-
-
- Supplementary information
-
-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- Yes
- Number of additional authors
-
6
- Research group(s)
-
C - Digital Health
- Citation count
- 18
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This paper develops and evaluates a bioinspired, adaptive algorithm for controlling electroactive polymer actuators. These actuators emulate biological muscles and have promise for soft robotic applications. The presented algorithm advances previous bioinspired algorithms by using adaptive elements to learn non-linearity’s and adapt to changes in the actuators being controlled. It has fed into further bio-inspired control contributions. The paper is multidisciplinary and spans computational neuroscience, robotics and control theory, providing both a theoretical and experimental component. The work is published in J. R. Soc. Interface - a leading journal.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
