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

• Published in: Journal of the Royal Society interface Vol. 13; no. 122; p. 20160547
• Main Authors: Wilson, Emma D., Assaf, Tareq, Pearson, Martin J., Rossiter, Jonathan M., Anderson, Sean R., Porrill, John, Dean, Paul
• Format: Journal Article
• Language: English
• Published: England The Royal Society 01-09-2016
• Subjects: Adaptive-Inverse Control; Artificial Muscle; Cerebellum; Life Sciences–Engineering interface; Nonlinear Control; Soft Robotics; Transfer Of Training; adaptive-inverse control; transfer of training; nonlinear control; soft robotics; artificial muscle; cerebellum
• ISSN: 1742-5689; 1742-5662
• DOI: 10.1098/rsif.2016.0547

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.