Bioinspired Adaptive Control for Artificial Muscles

  • Emma D. Wilson
  • Tareq Assaf
  • Martin J. Pearson
  • Jonathan M. Rossiter
  • Sean R. Anderson
  • John Porrill
Conference paper

DOI: 10.1007/978-3-642-39802-5_27

Part of the Lecture Notes in Computer Science book series (LNCS, volume 8064)
Cite this paper as:
Wilson E.D., Assaf T., Pearson M.J., Rossiter J.M., Anderson S.R., Porrill J. (2013) Bioinspired Adaptive Control for Artificial Muscles. In: Lepora N.F., Mura A., Krapp H.G., Verschure P.F.M.J., Prescott T.J. (eds) Biomimetic and Biohybrid Systems. Living Machines 2013. Lecture Notes in Computer Science, vol 8064. Springer, Berlin, Heidelberg

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.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Emma D. Wilson
    • 1
  • Tareq Assaf
    • 2
    • 3
  • Martin J. Pearson
    • 2
    • 3
  • Jonathan M. Rossiter
    • 2
    • 3
  • Sean R. Anderson
    • 1
  • John Porrill
    • 1
  1. 1.Sheffield Centre for Robotics (SCentRo)University of SheffieldUK
  2. 2.Bristol Robotics Laboratory (BRL)University of the West of EnglandUK
  3. 3.University of BristolUK

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