Cerebellar-Inspired Adaptive Control of a Robot Eye Actuated by Pneumatic Artificial Muscles

Alexander Lenz*, Sean R. Anderson, A. G. Pipe, Christopher R Melhuish, Paul Dean, John Porrill

*Corresponding author for this work

Research output: Contribution to journalArticle (Academic Journal)

39 Citations (Scopus)

Abstract

In this paper, a model of cerebellar function is implemented and evaluated in the control of a robot eye actuated by pneumatic artificial muscles. The investigated control problem is stabilization of the visual image in response to disturbances. This is analogous to the vestibuloocular reflex (VOR) in humans. The cerebellar model is structurally based on the adaptive filter, and the learning rule is computationally analogous to least-mean squares, where parameter adaptation at the parallel fiber/Purkinje cell synapse is driven by the correlation of the sensory error signal (carried by the climbing fiber) and the motor command signal. Convergence of the algorithm is first analyzed in simulation on a model of the robot and then tested online in both one and two degrees of freedom. The results show that this model of neural function successfully works on a real-world problem, providing empirical evidence for validating: 1) the generic cerebellar learning algorithm; 2) the function of the cerebellum in the VOR; and 3) the signal transmission between functional neural components of the VOR.

Original languageEnglish
Pages (from-to)1420-1433
Number of pages14
JournalIEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Volume39
Issue number6
DOIs
Publication statusPublished - Dec 2009

Keywords

  • Biomimetic model
  • cerebellum
  • neurorobotics
  • pneumatic artificial muscle (PAM)
  • vestibuloocular reflex (VOR)
  • VESTIBULOOCULAR REFLEX
  • MODEL
  • ARM
  • SYSTEMS
  • STABILIZATION
  • NEUROSCIENCE
  • PLASTICITY
  • MOVEMENTS
  • FLOCCULUS
  • PATHWAYS

Cite this