Tendon friction compensation and slack avoidance for trajectory tracking control of the tendon-driven medical continuum manipulator

Tendon-driven continuum manipulators can perform tasks in confined environments due to their flexibility and curvilinearity, especially in minimally invasive surgeries. However, the friction along tendons and tendon slack present challenges to their motion control. This work proposes a trajectory tracking controller based on adaptive fuzzy sliding mode control (AFSMC) for the tendon-driven continuum manipulators. It consists of a sliding mode control (SMC) law with two groups of adaptive fuzzy subcontrollers. The first one is utilized to estimate and compensate for friction forces along tendons. The second one adapts the switching terms of SMC to alleviate the chattering phenomenon and enhance control robustness. To prevent tendon slack, an antagonistic strategy along with the AFSMC controller is adopted to allocate driving forces. Simulation and experiment studies have been conducted to investigate the efficacy of the proposed controller. In free space experiments, the AFSMC controller generates an average root-mean-square error (RMSE) of 0.42% compared with 0.90% of the SMC controller. In the case of a 50 g load, the proposed controller reduces the average RMSE to 1.47% compared with 4.29% of the SMC controller. These experimental results demonstrate that the proposed AFSMC controller has high control accuracy, robustness, and reduced chattering.

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Publication Details

Journal
Biomimetic Intelligence and Robotics
Published
2025-04-23
DOI
https://doi.org/10.1016/j.birob.2025.100234
Citations
1
Primary Topic
Soft Robotics and Applications
Type
article
Field-Weighted Citation Impact
0.67
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article

Tendon friction compensation and slack avoidance for trajectory tracking control of the tendon-driven medical continuum manipulator

Chaoyang Shi, Kun Qian, Jianxiong Hao, Yue Zhang et al.
1 citations
Biomimetic Intelligence and Robotics
Soft Robotics and Applications
0.67
article

Tendon friction compensation and slack avoidance for trajectory tracking control of the tendon-driven medical continuum manipulator

Chaoyang Shi, Kun Qian, Jianxiong Hao, Yue Zhang, Zhiqiang Zhang, P.F. Du
article en
1 citations

Abstract

Tendon-driven continuum manipulators can perform tasks in confined environments due to their flexibility and curvilinearity, especially in minimally invasive surgeries. However, the friction along tendons and tendon slack present challenges to their motion control. This work proposes a trajectory tracking controller based on adaptive fuzzy sliding mode control (AFSMC) for the tendon-driven continuum manipulators. It consists of a sliding mode control (SMC) law with two groups of adaptive fuzzy subcontrollers. The first one is utilized to estimate and compensate for friction forces along tendons. The second one adapts the switching terms of SMC to alleviate the chattering phenomenon and enhance control robustness. To prevent tendon slack, an antagonistic strategy along with the AFSMC controller is adopted to allocate driving forces. Simulation and experiment studies have been conducted to investigate the efficacy of the proposed controller. In free space experiments, the AFSMC controller generates an average root-mean-square error (RMSE) of 0.42% compared with 0.90% of the SMC controller. In the case of a 50 g load, the proposed controller reduces the average RMSE to 1.47% compared with 4.29% of the SMC controller. These experimental results demonstrate that the proposed AFSMC controller has high control accuracy, robustness, and reduced chattering.

Biomimetic Intelligence and RoboticsVol. 5(4)
University of Leeds (GB), Tianjin University (CN), Heriot-Watt University (GB)
Reduced inequalities
Openalex Percentile: Top 33%
Soft Robotics and Applications
0.67
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Tendon friction compensation and slack avoidance for trajectory tracking control of the tendon-driven medical continuum manipulator — Chaoyang Shi, Kun Qian, et al. · Biomimetic Intelligence and Robotics (2025) | TGRS Research Map | TGRS