Literature DB >> 33501148

Self-Sensing Control for Soft-Material Actuators Based on Dielectric Elastomers.

Thorben Hoffstadt1, Jürgen Maas1.   

Abstract

Due to their energy density and softness that are comparable to human muscles dielectric elastomer (DE) transducers are well-suited for soft-robotic applications. This kind of transducer combines actuator and sensor functionality within one transducer so that no external senors to measure the deformation or to detect collisions are required. Within this contribution we present a novel self-sensing control for a DE stack-transducer that allows to control several different quantities of the DE transducer with the same controller. This flexibility is advantageous e.g., for the development of human machine interfaces with soft-bodied robots. After introducing the DE stack-transducer that is driven by a bidirectional flyback converter, the development of the self-sensing state and disturbance estimator based on an extended Kalman-filter is explained. Compared to known estimators designed for DE transducers supplied by bulky high-voltage amplifiers this one does not require any superimposed excitation to enable the sensor capability so that it also can be used with economic and competitive power electronics like the flyback converter. Due to the behavior of this converter a sliding mode energy controller is designed afterwards. By introducing different feed-forward controls the voltage, force or deformation can be controlled. The validation proofs that both the developed self-sensing estimator as well as the self-sensing control yield comparable results as previously published sensor-based approaches.
Copyright © 2019 Hoffstadt and Maas.

Entities:  

Keywords:  control; dielectric elastomers; extended Kalman filter; flyback-converter; self-sensing; soft material actuator; stack-actuator

Year:  2019        PMID: 33501148      PMCID: PMC7805669          DOI: 10.3389/frobt.2019.00133

Source DB:  PubMed          Journal:  Front Robot AI        ISSN: 2296-9144


  1 in total

1.  Cerebellar-inspired algorithm for adaptive control of nonlinear dielectric elastomer-based artificial muscle.

Authors:  Emma D Wilson; Tareq Assaf; Martin J Pearson; Jonathan M Rossiter; Sean R Anderson; John Porrill; Paul Dean
Journal:  J R Soc Interface       Date:  2016-09       Impact factor: 4.118

  1 in total

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