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

• Published in: IEEE transactions on systems, man and cybernetics. Part B, Cybernetics Vol. 39; no. 6; pp. 1420 - 1433
• Main Authors: Lenz, A., Anderson, S.R., Pipe, A.G., Melhuish, C., Dean, P., Porrill, J.
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.12.2009
• Subjects: Adaptive control; Adaptive filters; Algorithms; Artificial muscles; Biomimetic model; Biomimetics - instrumentation; Biomimetics - methods; Brain modeling; cerebellum; Cerebellum - physiology; Concurrent computing; Convergence; Cybernetics - methods; Equipment Design; Humans; Mathematical analysis; Mathematical models; Micro-Electrical-Mechanical Systems; Models, Neurological; Muscles; Muscles - physiology; Neural Networks (Computer); Neuronal Plasticity; neurorobotics; Optical fiber sensors; Optical fiber testing; pneumatic artificial muscle (PAM); Reflex, Vestibulo-Ocular - physiology; Robot control; Robotics - instrumentation; Robotics - methods; Robots; vestibuloocular reflex (VOR)
• ISSN: 1083-4419; 1941-0492; 1941-0492
• DOI: 10.1109/TSMCB.2009.2018138

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.