Spatial Repetitive Impedance Learning Control for Robot-Assisted Rehabilitation
文献类型：期刊论文
| 作者 | Yang, Jiantao6; Sun, Tairen6; Cheng, Long4,5 ; Hou, Zeng-Guang1,2,3,4
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| 刊名 | IEEE-ASME TRANSACTIONS ON MECHATRONICS
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| 出版日期 | 2022-12-14 |
| 页码 | 11 |
| 关键词 | Adaptive control impedance learning iterative learning control rehabilitation robot repetitive learning control robot control spatial periodicity |
| ISSN号 | 1083-4435 |
| DOI | 10.1109/TMECH.2022.3221931 |
| 通讯作者 | Cheng, Long(chenglong@compsys.ia.ac.cn) ; Hou, Zeng-Guang(zengguang.hou@ia.ac.cn) |
| 英文摘要 | In robot-assisted rehabilitation and leg exoskeletons, humans and robots are required to collaboratively complete repetitive tasks with fixed periodic paths. In such applications, impedance learning control can provide variable impedance regulation for improving the performance of physical interactions; however, designing such control is highly challenging owing to the difficulty in modeling human time-varying dynamics. By exploiting the spatial periodicity characteristics of the desired trajectory and human impedance, we propose a novel spatial repetitive impedance learning control strategy to enhance interaction performance. First, a defined spatial operator serves as the mathematics foundation for constructing the robot dynamics in the spatial domain. Then, a spatial impedance learning controller is designed. In this article, time-varying impedance profiles are estimated using spatial full-saturation iterative learning laws, while robotic parameter uncertainties are estimated using the differential adaptation law with projection modification. We validate the uniform convergence of the tracking error through a Lyapunov-like analysis and demonstrate the control effectiveness using an illustrative example. Compared with related results on temporal repetitive learning control, the proposed control approach can enable human-robot system to complete a repetitive task with unspecified speeds according to the users' strengths and motion capacity. |
| 资助项目 | National Key Research and Development Project[2019YFB1312500] ; National Natural Science Foundation of China[62025307] ; National Natural Science Foundation of China[61720106012] ; National Natural Science Foundation of China[U1913601] ; National Natural Science Foundation of China[U1913209] ; National Natural Science Foundation of China[62073156] ; National Natural Science Foundation of China[62103280] ; Beijing Sci. Tech. Program[Z211100007921021] ; Chinese Academy of Science[XDB32040000] |
| WOS研究方向 | Automation & Control Systems ; Engineering |
| 语种 | 英语 |
| WOS记录号 | WOS:000899978200001 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| 资助机构 | National Key Research and Development Project ; National Natural Science Foundation of China ; Beijing Sci. Tech. Program ; Chinese Academy of Science |
| 源URL | [http://ir.ia.ac.cn/handle/173211/51323]  |
| 专题 | 自动化研究所_复杂系统管理与控制国家重点实验室_先进机器人控制团队 |
| 通讯作者 | Cheng, Long; Hou, Zeng-Guang |
| 作者单位 | 1.Macau Univ Sci & Technol, Inst Syst Engn, Joint Lab Intelligence Sci & Technol, Macau 999078, Peoples R China 2.CAS Ctr Excellence Brain Sci & Intelligence Techno, Beijing 100190, Peoples R China 3.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China 4.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China 5.Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China 6.Univ Shanghai Sci & Technol, Sch Hlth Sci & Engn, Shanghai 200093, Peoples R China |
| 推荐引用方式 GB/T 7714 | Yang, Jiantao,Sun, Tairen,Cheng, Long,et al. Spatial Repetitive Impedance Learning Control for Robot-Assisted Rehabilitation[J]. IEEE-ASME TRANSACTIONS ON MECHATRONICS,2022:11. |
| APA | Yang, Jiantao,Sun, Tairen,Cheng, Long,&Hou, Zeng-Guang.(2022).Spatial Repetitive Impedance Learning Control for Robot-Assisted Rehabilitation.IEEE-ASME TRANSACTIONS ON MECHATRONICS,11. |
| MLA | Yang, Jiantao,et al."Spatial Repetitive Impedance Learning Control for Robot-Assisted Rehabilitation".IEEE-ASME TRANSACTIONS ON MECHATRONICS (2022):11. |
入库方式： OAI收割
来源：自动化研究所
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