SSTL: Self-Sensing Tendon Loop for Hysteresis Modeling and Compensation in Tendon-Sheath Mechanisms

2026-05-16

AI Summary

This work addresses the challenge of configuration-dependent hysteresis in tendon-sheath actuation, which limits control accuracy in flexible endoscopic robots. To overcome this, the authors propose a self-sensing tendon loop (SSTL) that employs a co-located bidirectional tendon circuit to synchronously measure proximal input and output tensions, thereby constructing a real-time tension profile. By integrating a learned mapping, the system estimates hysteresis parameters on-the-fly for feedforward compensation—eliminating the need for distal force or fiber-optic sensors and significantly enhancing modeling practicality and compensation fidelity. Experimental results across three insertion tube configurations demonstrate an 88.1% reduction in average RMSE for sinusoidal and random trajectory tension tracking compared to an uncompensated baseline, achieving 97.8% of the performance attainable by direct identification methods.

Abstract

Flexible endoscopic robots enable minimally invasive access through natural orifices, but their control accuracy is limited by configuration-dependent hysteresis in the tendon-sheath mechanisms (TSMs). Tendon-sheath friction and tendon elasticity induce a systematic discrepancy between the proximal actuation input and distal output, and this discrepancy varies with the insertion tube configuration. To address this challenge, this paper proposes the Self-Sensing Tendon Loop (SSTL), a double-pass tendon loop routed through the insertion tube and wrapped around a distal pulley, and returned to the proximal end. The loop structure allows both the input and output tensions of the SSTL to be measured proximally, thereby providing an input-output tension profile without requiring distal force or fiber-optic sensors. Because the SSTL shares the same routing path as the actuation TSM, the two TSMs exhibit strongly correlated hysteresis behaviors. From the SSTL tension profile, a learning-based mapping estimates the configuration-dependent hysteresis parameters of the actuation TSM, which are then used by a feedforward controller to compensate for actuation hysteresis. We validate the proposed method by tracking actuation tendon tension under three different insertion tube configurations. Across sinusoidal and random trajectories, the proposed method reduces average RMSE by 88.1% compared with the uncompensated baseline, achieving 97.8% of the performance of direct identification, which requires direct measurement of the input and output tension profile of the actuation TSM.

Problem

• tendon-sheath mechanism
• hysteresis
• configuration-dependent
• control accuracy
• flexible endoscopic robot

Innovation

• Self-Sensing Tendon Loop
• Hysteresis Compensation
• Tendon-Sheath Mechanism
• Configuration-Dependent Hysteresis
• Learning-Based Modeling