Neural Network Approach to Modelling the Behaviour of Ionic Polymer-Metal Composites in Dry Environments

作     者：Andrés Díaz Lantada Pilar Lafont Morgado José Luis Munoz Sanz Juan Manuel Munoz Guijosa Javier Echávarri Otero 

作者机构：Mechanical Engineering Department—E.T.S.I.I.IndustrialesUniversidad Politécnica de MadridMadridSpain 

出 版 物：《Journal of Signal and Information Processing》 (信号与信息处理（英文）)

年 卷 期：2012年第3卷第2期

页      面：137-145页

学科分类：1002[医学-临床医学] 100214[医学-肿瘤学] 10[医学] 

主　　题：Ionic Polymer-Metal Composites (IPMCs) Artificial Neural Networks (ANNs) Smart Materials Modelling and Simulation 

摘      要：Ionic polymer-metal composites (IPMCs) are especially interesting electroactive polymers because they show large a deformation in the presence of a very low driving voltage (around 1 - 2 V) and several applications have recently been proposed. Normally a humid environment is required for the best operation, although some IPMCs can operate in a dry environment, after proper encapsulation or if a solid electrolyte is used in the manufacturing process. However, such solutions usually lead to increasing mechanical stiffness and to a reduction of actuation capabilities. In this study we focus on the behaviour of non-encapsulated IPMCs as actuators in dry environments, in order to obtain relevant information for design tasks linked to the development of active devices based on this kind of smart material. The non-linear response obtained in the characterisation tests is especially well-suited to modelling these actuators with the help of artificial neural networks (ANNs). Once trained with the help of characterisation data, such neural networks prove to be a precise simulation tool for describing IPMC response in dry environments.