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

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.