Pengyu Du , Jianxiong Hao , Kun Qian , Yue Zhang , Zhiqiang Zhang , Chaoyang Shi
{"title":"肌腱驱动医疗连续体机械臂轨迹跟踪控制的肌腱摩擦补偿与松弛避免","authors":"Pengyu Du , Jianxiong Hao , Kun Qian , Yue Zhang , Zhiqiang Zhang , Chaoyang Shi","doi":"10.1016/j.birob.2025.100234","DOIUrl":null,"url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":100184,"journal":{"name":"Biomimetic Intelligence and Robotics","volume":"5 4","pages":"Article 100234"},"PeriodicalIF":5.4000,"publicationDate":"2025-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Tendon friction compensation and slack avoidance for trajectory tracking control of the tendon-driven medical continuum manipulator\",\"authors\":\"Pengyu Du , Jianxiong Hao , Kun Qian , Yue Zhang , Zhiqiang Zhang , Chaoyang Shi\",\"doi\":\"10.1016/j.birob.2025.100234\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>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.</div></div>\",\"PeriodicalId\":100184,\"journal\":{\"name\":\"Biomimetic Intelligence and Robotics\",\"volume\":\"5 4\",\"pages\":\"Article 100234\"},\"PeriodicalIF\":5.4000,\"publicationDate\":\"2025-04-23\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Biomimetic Intelligence and Robotics\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2667379725000257\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Biomimetic Intelligence and Robotics","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2667379725000257","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
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.