Cerebellar-inspired algorithm for adaptive control of nonlinear dielectric elastomer-based artificial muscle
Electroactive polymer actuators are important for soft robotics, but can be difficult to control because of compliance, creep and nonlinearities. Because biological control mechanisms have evolved to deal with such problems, we investigated whether a control scheme based on the cerebellum would be u...
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| Published in | Journal of the Royal Society interface Vol. 13; no. 122; p. 20160547 |
|---|---|
| Main Authors | , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
England
The Royal Society
01.09.2016
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| Subjects | |
| Online Access | Get full text |
| ISSN | 1742-5689 1742-5662 1742-5662 |
| DOI | 10.1098/rsif.2016.0547 |
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| Abstract | Electroactive polymer actuators are important for soft robotics, but can be difficult to control because of compliance, creep and nonlinearities. Because biological control mechanisms have evolved to deal with such problems, we investigated whether a control scheme based on the cerebellum would be useful for controlling a nonlinear dielectric elastomer actuator, a class of artificial muscle. The cerebellum was represented by the adaptive filter model, and acted in parallel with a brainstem, an approximate inverse plant model. The recurrent connections between the two allowed for direct use of sensory error to adjust motor commands. Accurate tracking of a displacement command in the actuator's nonlinear range was achieved by either semi-linear basis functions in the cerebellar model or semi-linear functions in the brainstem corresponding to recruitment in biological muscle. In addition, allowing transfer of training between cerebellum and brainstem as has been observed in the vestibulo-ocular reflex prevented the steady increase in cerebellar output otherwise required to deal with creep. The extensibility and relative simplicity of the cerebellar-based adaptive-inverse control scheme suggests that it is a plausible candidate for controlling this type of actuator. Moreover, its performance highlights important features of biological control, particularly nonlinear basis functions, recruitment and transfer of training. |
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| AbstractList | Electroactive polymer actuators are important for soft robotics, but can be difficult to control because of compliance, creep and nonlinearities. Because biological control mechanisms have evolved to deal with such problems, we investigated whether a control scheme based on the cerebellum would be useful for controlling a nonlinear dielectric elastomer actuator, a class of artificial muscle. The cerebellum was represented by the adaptive filter model, and acted in parallel with a brainstem, an approximate inverse plant model. The recurrent connections between the two allowed for direct use of sensory error to adjust motor commands. Accurate tracking of a displacement command in the actuator's nonlinear range was achieved by either semi-linear basis functions in the cerebellar model or semi-linear functions in the brainstem corresponding to recruitment in biological muscle. In addition, allowing transfer of training between cerebellum and brainstem as has been observed in the vestibulo-ocular reflex prevented the steady increase in cerebellar output otherwise required to deal with creep. The extensibility and relative simplicity of the cerebellar-based adaptive-inverse control scheme suggests that it is a plausible candidate for controlling this type of actuator. Moreover, its performance highlights important features of biological control, particularly nonlinear basis functions, recruitment and transfer of training. Electroactive polymer actuators are important for soft robotics, but can be difficult to control because of compliance, creep and nonlinearities. Because biological control mechanisms have evolved to deal with such problems, we investigated whether a control scheme based on the cerebellum would be useful for controlling a nonlinear dielectric elastomer actuator, a class of artificial muscle. The cerebellum was represented by the adaptive filter model, and acted in parallel with a brainstem, an approximate inverse plant model. The recurrent connections between the two allowed for direct use of sensory error to adjust motor commands. Accurate tracking of a displacement command in the actuator's nonlinear range was achieved by either semi-linear basis functions in the cerebellar model or semi-linear functions in the brainstem corresponding to recruitment in biological muscle. In addition, allowing transfer of training between cerebellum and brainstem as has been observed in the vestibulo-ocular reflex prevented the steady increase in cerebellar output otherwise required to deal with creep. The extensibility and relative simplicity of the cerebellar-based adaptive-inverse control scheme suggests that it is a plausible candidate for controlling this type of actuator. Moreover, its performance highlights important features of biological control, particularly nonlinear basis functions, recruitment and transfer of training.Electroactive polymer actuators are important for soft robotics, but can be difficult to control because of compliance, creep and nonlinearities. Because biological control mechanisms have evolved to deal with such problems, we investigated whether a control scheme based on the cerebellum would be useful for controlling a nonlinear dielectric elastomer actuator, a class of artificial muscle. The cerebellum was represented by the adaptive filter model, and acted in parallel with a brainstem, an approximate inverse plant model. The recurrent connections between the two allowed for direct use of sensory error to adjust motor commands. Accurate tracking of a displacement command in the actuator's nonlinear range was achieved by either semi-linear basis functions in the cerebellar model or semi-linear functions in the brainstem corresponding to recruitment in biological muscle. In addition, allowing transfer of training between cerebellum and brainstem as has been observed in the vestibulo-ocular reflex prevented the steady increase in cerebellar output otherwise required to deal with creep. The extensibility and relative simplicity of the cerebellar-based adaptive-inverse control scheme suggests that it is a plausible candidate for controlling this type of actuator. Moreover, its performance highlights important features of biological control, particularly nonlinear basis functions, recruitment and transfer of training. |
| Author | Assaf, Tareq Dean, Paul Anderson, Sean R. Porrill, John Wilson, Emma D. Pearson, Martin J. Rossiter, Jonathan M. |
| AuthorAffiliation | 3 Bristol Robotics Laboratory , University of the West of England and University of Bristol , UK 2 Department of Psychology , University of Sheffield , Sheffield , UK 4 Department of Engineering Mathematics , University of Bristol , Bristol , UK 5 Department of Automatic Control and Systems Engineering , University of Sheffield , Sheffield , UK 1 Sheffield Robotics , University of Sheffield , Sheffield , UK |
| AuthorAffiliation_xml | – name: 1 Sheffield Robotics , University of Sheffield , Sheffield , UK – name: 3 Bristol Robotics Laboratory , University of the West of England and University of Bristol , UK – name: 2 Department of Psychology , University of Sheffield , Sheffield , UK – name: 4 Department of Engineering Mathematics , University of Bristol , Bristol , UK – name: 5 Department of Automatic Control and Systems Engineering , University of Sheffield , Sheffield , UK |
| Author_xml | – sequence: 1 givenname: Emma D. orcidid: 0000-0001-9445-8220 surname: Wilson fullname: Wilson, Emma D. email: e.wilson@sheffield.ac.uk organization: Sheffield Robotics, University of Sheffield, Sheffield, UK; Department of Psychology, University of Sheffield, Sheffield, UK – sequence: 2 givenname: Tareq surname: Assaf fullname: Assaf, Tareq organization: Bristol Robotics Laboratory, University of the West of England and University of Bristol, UK – sequence: 3 givenname: Martin J. orcidid: 0000-0002-8642-4845 surname: Pearson fullname: Pearson, Martin J. organization: Bristol Robotics Laboratory, University of the West of England and University of Bristol, UK – sequence: 4 givenname: Jonathan M. surname: Rossiter fullname: Rossiter, Jonathan M. organization: Bristol Robotics Laboratory, University of the West of England and University of Bristol, UK; Department of Engineering Mathematics, University of Bristol, Bristol, UK – sequence: 5 givenname: Sean R. orcidid: 0000-0002-7452-5681 surname: Anderson fullname: Anderson, Sean R. organization: Sheffield Robotics, University of Sheffield, Sheffield, UK; Department of Automatic Control and Systems Engineering, University of Sheffield, Sheffield, UK – sequence: 6 givenname: John surname: Porrill fullname: Porrill, John organization: Sheffield Robotics, University of Sheffield, Sheffield, UK; Department of Psychology, University of Sheffield, Sheffield, UK – sequence: 7 givenname: Paul orcidid: 0000-0003-3257-620X surname: Dean fullname: Dean, Paul organization: Sheffield Robotics, University of Sheffield, Sheffield, UK; Department of Psychology, University of Sheffield, Sheffield, UK |
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| Keywords | adaptive-inverse control transfer of training nonlinear control soft robotics artificial muscle cerebellum |
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| SubjectTerms | Adaptive-Inverse Control Artificial Muscle Cerebellum Life Sciences–Engineering interface Nonlinear Control Soft Robotics Transfer Of Training |
| Title | Cerebellar-inspired algorithm for adaptive control of nonlinear dielectric elastomer-based artificial muscle |
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