用自感应技术让气动人工肌肉实时感知力与位置，无需额外传感器。
Inductance-Based Force Self-Sensing in Fiber-Reinforced Pneumatic Twisted-and-Coiled Actuators
- 在肌腱中嵌入镍丝，通过电感变化反推力和位移。
- 电感-力关系线性好，误差比外置力传感器还小。
- 适合需要高精度闭环控制的柔性机器人场景。
纤维增强型气动扭曲-缠绕执行器（FR-PTCAs）具有高功率密度和柔顺性，但强滞后性和缺乏本体感知限制了其闭环控制性能。本文提出一种集成导电镍丝的自感应FR-PTCA，可通过电感反馈实现内在力估计和间接位移推断。实验表明，在恒定压力下，电感与力呈确定性、低滞后的映射关系，而电感-长度关系则具有强滞后性。基于此特性，论文构建了参数化自感应模型，并设计了一种将扩展卡尔曼滤波（EKF）与约束优化结合的非线性混合观测器，以解决电感-力映射中的模糊性问题，准确估计执行器状态。实验结果表明，该方法的力估计算法精度与外部负载传感器相当，并在不同负载条件下保持鲁棒性能。
原文摘要 · Abstract (English)
Fiber-reinforced pneumatic twisted-and-coiled actuators (FR-PTCAs) offer high power density and compliance but their strong hysteresis and lack of intrinsic proprioception limit effective closed-loop control. This paper presents a self-sensing FR-PTCA integrated with a conductive nickel wire that enables intrinsic force estimation and indirect displacement inference via inductance feedback. Experimental characterization reveals that the inductance of the actuator exhibits a deterministic, low-hysteresis inductance-force relationship at constant pressures, in contrast to the strongly hysteretic inductance-length behavior. Leveraging this property, this paper develops a parametric self-sensing model and a nonlinear hybrid observer that integrates an Extended Kalman Filter (EKF) with constrained optimization to resolve the ambiguity in the inductance-force mapping and estimate actuator states. Experimental results demonstrate that the proposed approach achieves force estimation accuracy comparable to that of external load cells and maintains robust performance under varying load conditions.
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