In this paper, a black-box model, suitable for being used as a Soft Sensor for an Ionic Polymer-Metal Composite, is introduced. Applications of such materials, in many fields, such as bio-inspired robotics, medicine, and aerospace, have been reported. The proposed Soft Sensor is a data-driven Nonlinear Finite Impulse Response model, implemented by using a Deep Neural Network, where the output is estimated only on the basis of input past samples. The model has been selected with the aim of eliminating any hardware position sensor and, therefore, reducing the complexity of the positioning system.

A soft sensor for the estimation of ionic electroactive actuator motion based on deep learning

Andò, B.;Graziani, S.;
2018-01-01

Abstract

In this paper, a black-box model, suitable for being used as a Soft Sensor for an Ionic Polymer-Metal Composite, is introduced. Applications of such materials, in many fields, such as bio-inspired robotics, medicine, and aerospace, have been reported. The proposed Soft Sensor is a data-driven Nonlinear Finite Impulse Response model, implemented by using a Deep Neural Network, where the output is estimated only on the basis of input past samples. The model has been selected with the aim of eliminating any hardware position sensor and, therefore, reducing the complexity of the positioning system.
9781538622223
Deep Learning; Ionic Polymer-Metal Composites; Model Identification; Nonlinear Finite Impulse Response Models; Safety, Risk, Reliability and Quality; Instrumentation
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.11769/361896
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