This dissertation deals with the modeling of electroactive polymers. More specifically IPMCs and IP2Cs, which are electroactive polymers that can be used both as sensors and as actuators, have been considered. The modeling of IPMC and IP2C transducers, in fact, is an open issue relevant to the development of effective applications. An introductions to the general framework of the proposed models of Electroactive polymers will be given in Section 1, while, in Sections 2 and 3, a multiphysics model of actuators is presented in details. It integrates the description of the electrical, mechanical, chemical and thermal coupled physics domains in a unique solution. As a novel contribution, a model optimization procedure which integrates Nelder-Mead simplex method with the multiphysics model is exploited to identify model parameters by fitting experimental data. A further nonlinear neural network model of IP2C actuators has been implemented and the results will be described in Section 4. The proposed model takes into account the humidity dependence of the device as a modifying input. Three different Neural Network models, e.g. Feed-forward neural network, Radial-basis neural network and recurrent neural network are developed and a comparison of proposed model have been reported.
Questa tesi si occupa della modellazione dei polimeri elettroattivi (gli attuatori IPMC e IP2C). Nella prima sezione è descritta un introduzione generale dei modelli proposti, mentre, nelle sezioni 2 e 3 è presentato nel dettaglio il modello multifisico degli attuatori. Tali modelli integrano i vari domini fisici, termico, elettrico, meccanico, chimico in un unica soluzione. Inoltra una procedura di ottimizzazione è sfruttata per identificare i parametri del modello a partire dai dati sperimentali. Infine, nella sezione 4, è presentato un ulteriore modello non lineare dell'attuatore IP2C, basato sulla modellizzazione con reti neurali. in particolare il modello proposto tiene conto della dipendenza dall'umidità del dispositivo.
Advanced modeling techniques for electroactive polymers transducers / DE LUCA, Viviana. - (2014 Dec 09).
Advanced modeling techniques for electroactive polymers transducers
DE LUCA, VIVIANA
2014-12-09
Abstract
This dissertation deals with the modeling of electroactive polymers. More specifically IPMCs and IP2Cs, which are electroactive polymers that can be used both as sensors and as actuators, have been considered. The modeling of IPMC and IP2C transducers, in fact, is an open issue relevant to the development of effective applications. An introductions to the general framework of the proposed models of Electroactive polymers will be given in Section 1, while, in Sections 2 and 3, a multiphysics model of actuators is presented in details. It integrates the description of the electrical, mechanical, chemical and thermal coupled physics domains in a unique solution. As a novel contribution, a model optimization procedure which integrates Nelder-Mead simplex method with the multiphysics model is exploited to identify model parameters by fitting experimental data. A further nonlinear neural network model of IP2C actuators has been implemented and the results will be described in Section 4. The proposed model takes into account the humidity dependence of the device as a modifying input. Three different Neural Network models, e.g. Feed-forward neural network, Radial-basis neural network and recurrent neural network are developed and a comparison of proposed model have been reported.File | Dimensione | Formato | |
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