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Control systems are foundational elements in automation and engineering. They are broadly categorized into open-loop and closed-loop systems. These classifications hinge on the presence or absence of feedback mechanisms, significantly influencing the system's performance, complexity, and application.
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A Versatile-Designable Framework for Active and Programmable Shape-Morphing Soft Matter Systems: From Inverse Design

Kai Liu1,2,3, Peiling Xie1,2,3, Ruitong Song1,2,3

  • 1School of Materials Science and Engineering, Zhejiang University, Hangzhou, China.

Advanced Science (Weinheim, Baden-Wurttemberg, Germany)
|June 22, 2026
PubMed
Summary
This summary is machine-generated.

Researchers developed a programmable soft matter system using liquid crystal elastomers and conductive strips for shape-morphing robots. This framework enables adaptive control and real-time sensing in soft robotic systems.

Keywords:
closed‐loop controldeep learninginverse designliquid crystal elastomersprogrammable morphingsoft matter

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Area of Science:

  • Soft robotics
  • Smart materials
  • Materials science

Background:

  • Developing programmable, multifunctional, and adaptable soft matter systems is a key challenge.
  • Existing methods often require complex microstructural manipulation.

Purpose of the Study:

  • To create a versatile framework for active, programmable shape-morphing soft matter.
  • To enable addressable actuation and deterministic 2D-to-3D transformations.
  • To integrate physical and computational intelligence for adaptive soft robots.

Main Methods:

  • Utilized liquid crystal elastomers (LCEs) and conductive constraint strips for actuation and geometric control.
  • Employed an inverse design strategy with an analytical model for complex surface reconstruction.
  • Integrated shape memory polymers (SMPs) and crack-based sensors for proprioception and lockability.
  • Developed a flapping-wing robot with a 1D convolutional neural network (1D-CNN) optimized by the grey wolf optimizer (GWO) for adaptive control.

Main Results:

  • Demonstrated addressable electrothermal actuation and 2D-to-3D shape transformation without complex microstructuring.
  • Constructed a proprioceptive lockable soft robotic system (PLSRS) with zero-energy shape retention.
  • Successfully deployed the PLSRS in a flapping-wing robot for autonomous adaptive wing regulation based on wind speed estimation.

Conclusions:

  • The proposed framework offers a versatile platform for next-generation adaptive soft robots.
  • Successfully fused physical intelligence (shape-morphing, sensing) with computational intelligence (adaptive control).
  • The system exhibits programmability, multifunctional integration, and environmental adaptability.