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

Yunsong Zhang, Tianlin Li, Mingyang Yang, Feitian Zhang

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.

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

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.
Paper Structure (12 sections, 8 equations, 11 figures, 1 table)

This paper contains 12 sections, 8 equations, 11 figures, 1 table.

Figures (11)

  • Figure 1: Conceptual overview of the proposed inductive self-sensing framework for FR-PTCAs. A helically wrapped conductive nickel wire enables intrinsic inductive sensing without external rigid sensors. While the inductance-length relationship exhibits strong hysteresis, the inductance-force mapping remains deterministic and low-hysteresis at constant pressures. Leveraging this property, a nonlinear hybrid observer processes inductance measurements to enable real-time estimation of actuator force and internal states.
  • Figure 2: Fabrication process of the self-sensing FR-PTCA. Schematics of the fabrication platform and its stages: (a) precursor preparation and drawing, (b) conductive fiber wrapping, (c) twisting, (d) coiling.
  • Figure 3: Experimental setup for the FR-PTCA. (a) Isometric Platform. (b) Isotonic Platform.
  • Figure 4: Experimental characterization of the FR-PTCA. (a) Force-length relationship at constant pressures. (b) Force-pressure relationship at fixed lengths.
  • Figure 5: Experimental characterization of the FR-PTCA inductance response. (a) Inductance versus output force at varying internal pressures. (b) Inductance versus internal pressure under isometric conditions for different lengths. (c) Inductance versus actuator length under isobaric conditions.
  • ...and 6 more figures