Data_Cerebellar_Inpsired_Adaptive_Control_for_Nonlinear_Artificial_Muscle from Cerebellar inspired algorithm for adaptive control of nonlinear dielectric elastomer based artificial muscle
datasetposted on 13.09.2016 by Emma D. Wilson, Tareq Assaf, Martin J. Pearson, Jonathan M. Rossiter, Sean R. Anderson, John Porrill, Paul Dean
Datasets usually provide raw data for analysis. This raw data often comes in spreadsheet form, but can be any collection of data, on which analysis can be performed.
Electroactive polymer actuators are important for soft robotics, but can be difficult to control because of compliance, creep, and nonlinearities. Since 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 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.