Iterative Learning Embedded Composite Model Reference Adaptive Control for Off-Axis In-Situ Rotation in Nanorobotic Manipulation

doi:10.1109/LCSYS.2023.3336545

	Iterative Learning Embedded Composite Model Reference Adaptive Control for Off-Axis In-Situ Rotation in Nanorobotic Manipulation
	Zhang, Heng; Fu, Xiang; Liu, Song; Wang, Yang
	2024
发表期刊	IEEE CONTROL SYSTEMS LETTERS (IF:2.4[JCR-2023],2.4[5-Year])
ISSN	2475-1456
EISSN	2475-1456
卷号	8 页码:291-296
发表状态	已发表
DOI	10.1109/LCSYS.2023.3336545
摘要	Realizing dexterous rotation under the microscope has been a great and yet not-well-addressed challenge in micro/nano-robotics. The limited field of view (FOV) of a microscope necessitates point-wise in-situ rotation, which refers to manipulating objects within their existing or natural environment. Though on-axis in-situ rotation has been investigated, off-axis in-situ rotation, which is more suitable to classical nano-robot setups, remains unexplored. The main difficulty facing here is the model uncertainties and visual disturbances that fail traditional control methods. In this letter, we propose a novel control scheme, namely the iterative learning embedded composite model reference adaptive control (IL-CMRAC), for efficient visual servo to solve the problem of in-situ off-axis rotation, by fully leveraging the repetitiveness nature of rotational motion. IL-CMRAC takes advantage of both offline learning and online adaptation to tackle the difficulties brought by uncertainties. Experiments demonstrated that the proposed method is capable of realizing high-precision off-axis in-situ rotation by average error within several microns. © 2017 IEEE.
关键词	Nanorobotics indirect iterative learning control composite model reference adaptive control Iterative methods Rotation Two term control systems Uncertainty analysis Visual servoing Adaptation models Adaptive Control Composite model reference adaptive control Composite Modelling Convergence Indirect iterative learning control Iterative learning control Model-reference adaptive controls Nano-positioning Uncertainty
URL	查看原文
收录类别	EI
语种	英语
出版者	Institute of Electrical and Electronics Engineers Inc.
EI入藏号	20234815123932
EI主题词	Model reference adaptive control
EI分类号	461.1 Biomedical Engineering ; 731.1 Control Systems ; 731.5 Robotics ; 761 Nanotechnology ; 921.6 Numerical Methods ; 922.1 Probability Theory ; 931.1 Mechanics
原始文献类型	Journal article (JA)
来源库	IEEE
文献类型	期刊论文
条目标识符	https://kms.shanghaitech.edu.cn/handle/2MSLDSTB/348666
专题	信息科学与技术学院信息科学与技术学院_硕士生信息科学与技术学院_博士生信息科学与技术学院_PI研究组_汪阳组信息科学与技术学院_PI研究组_刘松组
通讯作者	Liu, Song; Wang, Yang
作者单位	School of Information Science and Technology, ShanghaiTech University, Shanghai, China
第一作者单位	信息科学与技术学院
通讯作者单位	信息科学与技术学院
第一作者的第一单位	信息科学与技术学院
推荐引用方式 GB/T 7714	Zhang, Heng,Fu, Xiang,Liu, Song,et al. Iterative Learning Embedded Composite Model Reference Adaptive Control for Off-Axis In-Situ Rotation in Nanorobotic Manipulation[J]. IEEE CONTROL SYSTEMS LETTERS,2024,8:291-296.
APA	Zhang, Heng,Fu, Xiang,Liu, Song,&Wang, Yang.(2024).Iterative Learning Embedded Composite Model Reference Adaptive Control for Off-Axis In-Situ Rotation in Nanorobotic Manipulation.IEEE CONTROL SYSTEMS LETTERS,8,291-296.
MLA	Zhang, Heng,et al."Iterative Learning Embedded Composite Model Reference Adaptive Control for Off-Axis In-Situ Rotation in Nanorobotic Manipulation".IEEE CONTROL SYSTEMS LETTERS 8(2024):291-296.