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通过单目标和多目标优化算法识别 IPMC 非线性模型。

Identification of IPMC nonlinear model via single and multi-objective optimization algorithms.

机构信息

Dipartimento di Ingegneria Elettrica Elettronica ed Informatica, Università degli Studi di Catania, V.le A. Doria 6, 95125 Catania, Italy.

Dipartimento di Ingegneria Elettrica Elettronica ed Informatica, Università degli Studi di Catania, V.le A. Doria 6, 95125 Catania, Italy.

出版信息

ISA Trans. 2014 Mar;53(2):481-8. doi: 10.1016/j.isatra.2013.11.012. Epub 2013 Dec 14.

Abstract

Ionic Polymer-Metal Composites (IPMCs) are electro-active polymers transforming mechanical forces into electric signals and vice versa. This paper proposes an improved electro-mechanical grey-box model for IPMC membrane working as actuator. In particular the IPMC nonlinearity has been characterized through experimentation and included within the electric model. Moreover identification of the model parameters has been performed via optimization algorithms using both single- and multi-objective formulation. Minimization was attained via the Nelder-Mead simplex and the Genetic Algorithms considering as cost functions the error between the experimental and modeled absorbed current and the error between experimental and modeled displacement. The obtained results for the different formulations have been then compared.

摘要

离子聚合物-金属复合材料(IPMC)是一种将机械能转化为电能、反之亦然的电活性聚合物。本文提出了一种改进的用于作为执行器的 IPMC 膜的机电灰箱模型。特别是,通过实验对 IPMC 的非线性进行了特征化,并将其包含在电模型中。此外,还通过使用单目标和多目标公式的优化算法来执行模型参数的识别。通过考虑实验和模型化吸收电流之间的误差以及实验和模型化位移之间的误差的 Nelder-Mead 单纯形法和遗传算法来实现最小化。然后比较了不同配方的结果。

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