综述柔性驱动器建模、控制与自感知技术，助力软体机器人应用
Modeling, Control and Self-sensing of Dielectric Elastomer Soft Actuators: A Review
- 分类梳理物理模型与经验模型，预测驱动器电-机械响应
- 归纳开环、反馈、自适应等多种控制策略，提升精度与稳定性
- 对比自感知方法，无需额外传感器即可重构位移，适合集成系统
介电弹性体执行器（DEAs）因重量轻、应变大、响应快、能量密度高等优势，在过去几十年中受到广泛关注，尤其在软体机器人领域。然而，由于存在非线性弹性、固有粘弹性蠕变、滞后效应及振动动力学等问题，其建模、控制与自感知面临挑战，限制了实际应用。本文综述了多种用于预测DEA电-机械响应的物理基础模型与现象学模型；回顾了开环前馈控制、反馈控制、前馈-反馈控制及自适应前馈控制等不同控制方法；讨论了基于物理机制与数据驱动的自感知方法，实现无需额外传感器即可重构位移；最后总结现有问题与未来研究机遇。
原文摘要 · Abstract (English)
Dielectric elastomer actuators (DEAs) have garnered extensive attention especially in soft robotic applications over the past few decades owing to the advantages of lightweight, large strain, fast response and high energy density. However, because the DEAs suffer from nonlinear elasticity, inherent viscoelastic creep, hysteresis and vibrational dynamics, the modeling, control and self-sensing of DEAs are challenging, thereby hindering the practical applications of DEAs. In order to address these challenges, numerous studies have been conducted. In this review, various physics-based modeling methods and phenomenological modeling methods for predicting the electromechanical response of DEAs are presented and discussed. Different control methods for DEAs are reviewed, which are classified into open-loop feedforward control, feedback control, feedforward-feedback control and adaptive feedforward control. Physics-based self-sensing methods and data-driven self-sensing methods for reconstructing the DEA displacement without the need for additional sensors are discussed. Finally, the existing problems and new opportunities for the further studies are summarized.
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