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<p>In this paper, we propose a data-driven nonlinear modeling approach to describe the dynamics of smart surfaces composed of electroactive liquid crystal networks (LCNs). LCNs are among the top candidates for materials to be employed in smart surfaces such as haptic displays. To realize such applications, the ability to predict an accurate LCN surface response as a function of the input signal is crucial. In this paper, we propose a data-driven modeling approach to identify the parameters of a dynamic model based on experimental data. The resulting model is used for feedforward control to compute the appropriate excitation parameters that ensure a certain desired surface deformation. This feedforward control approach is validated in a simulation study.</p>

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Data-driven modeling for electro-active liquid crystal polymer networks

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年1月9日

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  • Anahita Amiri,
  • Mohammad Fahim Shakib,
  • Ines Lopez Arteaga,
  • Nathan van de Wouw

摘要

In this paper, we propose a data-driven nonlinear modeling approach to describe the dynamics of smart surfaces composed of electroactive liquid crystal networks (LCNs). LCNs are among the top candidates for materials to be employed in smart surfaces such as haptic displays. To realize such applications, the ability to predict an accurate LCN surface response as a function of the input signal is crucial. In this paper, we propose a data-driven modeling approach to identify the parameters of a dynamic model based on experimental data. The resulting model is used for feedforward control to compute the appropriate excitation parameters that ensure a certain desired surface deformation. This feedforward control approach is validated in a simulation study.