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Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
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Identification of IPMC nonlinear model via single and multi-objective optimization algorithms.

Riccardo Caponetto1, Salvatore Graziani1, Fulvio Pappalardo1

  • 1Dipartimento di Ingegneria Elettrica Elettronica ed Informatica, Università degli Studi di Catania, V.le A. Doria 6, 95125 Catania, Italy.

ISA Transactions
|December 18, 2013
PubMed
Summary
This summary is machine-generated.

This study introduces an improved electro-mechanical model for Ionic Polymer-Metal Composites (IPMCs) actuators, accurately capturing their nonlinear behavior. The model enhances performance prediction by integrating experimental data and advanced optimization techniques.

Keywords:
Actuator modelElectro-active polymerGenetic AlgorithmsModel identificationSimplex method

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Area of Science:

  • Materials Science
  • Electrical Engineering
  • Robotics

Background:

  • Ionic Polymer-Metal Composites (IPMCs) are advanced electro-active materials capable of converting mechanical and electrical energy.
  • IPMCs are increasingly utilized as actuators in soft robotics and biomedical devices.
  • Accurate modeling is crucial for optimizing IPMC performance and control.

Purpose of the Study:

  • To develop and validate an improved electro-mechanical grey-box model for IPMC membranes functioning as actuators.
  • To characterize and incorporate the inherent nonlinearities of IPMCs into the electrical model.
  • To identify model parameters using single- and multi-objective optimization algorithms.

Main Methods:

  • Experimental characterization of IPMC nonlinear behavior.
  • Development of an electro-mechanical grey-box model integrating experimental data.
  • Parameter identification using Nelder-Mead simplex and Genetic Algorithms.
  • Comparison of single- and multi-objective optimization formulations.

Main Results:

  • The proposed grey-box model effectively captures IPMC nonlinearities.
  • Optimization algorithms successfully identified model parameters.
  • Both single- and multi-objective approaches yielded comparable results in minimizing model errors.
  • The model accurately predicts experimental absorbed current and displacement.

Conclusions:

  • The improved electro-mechanical grey-box model offers enhanced accuracy for IPMC actuator modeling.
  • The integration of experimental data and optimization techniques is effective for characterizing IPMC behavior.
  • This work provides a valuable tool for the design and control of IPMC-based systems.