Inductance-Based Force Self-Sensing in Fiber-Reinforced Pneumatic Twisted-and-Coiled Actuators

TL;DR

This paper introduces an inductance-based self-sensing method for fiber-reinforced pneumatic twisted-and-coiled actuators, enabling accurate force estimation and improved closed-loop control despite hysteresis and lack of proprioception.

Contribution

It presents a novel inductance-based sensing approach and a hybrid observer for intrinsic force and displacement estimation in FR-PTCAs, addressing hysteresis challenges.

Findings

Force estimation accuracy comparable to external load cells

Deterministic, low-hysteresis inductance-force relationship at constant pressure

Robust performance under varying load conditions

Abstract

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.