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Neural Dynamics for Computing Perturbed Nonlinear Equations Applied to ACP-Based Lower Limb Motion Intention Recognition

文献类型：期刊论文

作者Jin, Long3; Li, Jiachang3; Sun, Zhongbo4; Lu, Jingwei1,2; Wang, Fei-Yue1,2
刊名IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
出版日期2021-10-01
页码9
关键词Mathematical models Computational modeling Numerical models Nonlinear equations Convergence Analytical models Time-varying systems Artificial systems computational experiments and parallel execution (ACP) discrete-time neural dynamics (DTND) model human lower limb physical experiment nonlinear equations
ISSN号2168-2216
DOI10.1109/TSMC.2021.3114213
通讯作者Wang, Fei-Yue(feiyue.wang@ia.ac.cn)
英文摘要Many complex nonlinear optimization or control issues can be transformed into the solving of time-varying nonlinear equations (TVNEs), playing a fundamental role in the control and management of complex systems. As a result, a robust and high-precision online solution method is significant for TVNE. However, there are three main challenges for handling TVNE via the existing methods: First, short-time invariance assumption frequently leveraged in the existing methods leads to lagging errors that are difficult to eliminate. Second, it is difficult in dealing with unknown noise disturbance during the solution process, which causes low solution accuracy or solution failure. Third, existing continuous-time methods are hard to be implemented on digital equipments. In this article, an anti-noise discrete-time neural dynamics (DTND) is designed and studied to overcome the above issues systematically. The theoretical analysis and numerical simulations demonstrate that the proposed model effectively eliminates the lagging errors and achieves the accurate solution of the TVNE in a noisy environment. Moreover, to verify the superior numerical computational property of the DTND model, the intention recognition of lower limbs is explored from the artificial systems, computational experiments, and parallel execution (ACP) framework. Specifically, a nonlinear artificial dynamic system (NADS) concerning the human surface electromyogram (sEMG) signals and joint information is established, which performs in parallel with the actual human lower limb physical experiments. Simulation results illustrate that, within the acceptable range of the digital computer, the controller designed by the DTND model can well guide the NADS to accurately recognize the motion intention of the human lower limb.
WOS关键词MUSCLE FORCES ; JOINT MOMENTS ; SYSTEMS ; NETWORK
资助项目Key-Area Research and Development Program of Guangdong Province[2020B0909050003] ; Key Research and Development Program 2020 of Guangzhou[202007050002] ; National Natural Science Foundation of China[U1811463] ; National Natural Science Foundation of China[62176109] ; National Natural Science Foundation of China[61873304] ; Team Project of Natural Science Foundation of Qinghai Province (China)[2020-ZJ-903] ; Natural Science Foundation of Gansu Province[21JR7RA531] ; Natural Science Foundation of Gansu Province[20JR10RA639] ; CAAI-Huawei MindSpore Open Fund[CAAIXSJLJJ-2020-009A] ; China Postdoctoral Science Foundation[2018M641784] ; Key Science and Technology Projects of Jilin Province[20200201291JC] ; Chongqing Key Laboratory of Mobile Communications Technology[cqupt-mct-202004] ; Fundamental Research Funds for the Central Universities[lzujbky-2021-65] ; Gansu Provincial Youth Doctoral Fund of Colleges and Universities[2021QB-003] ; Science and Technology Plan Project of Chengguan District (Lanzhou)[2021-1-2] ; Science and Technology Plan Project of Chengguan District (Lanzhou)[2021-7-1]
WOS研究方向Automation & Control Systems ; Computer Science
语种英语
WOS记录号WOS:000732395900001
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
资助机构Key-Area Research and Development Program of Guangdong Province ; Key Research and Development Program 2020 of Guangzhou ; National Natural Science Foundation of China ; Team Project of Natural Science Foundation of Qinghai Province (China) ; Natural Science Foundation of Gansu Province ; CAAI-Huawei MindSpore Open Fund ; China Postdoctoral Science Foundation ; Key Science and Technology Projects of Jilin Province ; Chongqing Key Laboratory of Mobile Communications Technology ; Fundamental Research Funds for the Central Universities ; Gansu Provincial Youth Doctoral Fund of Colleges and Universities ; Science and Technology Plan Project of Chengguan District (Lanzhou)
源URL[http://ir.ia.ac.cn/handle/173211/46995]  
专题自动化研究所_复杂系统管理与控制国家重点实验室_先进控制与自动化团队
通讯作者Wang, Fei-Yue
作者单位1.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China
2.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China
3.Lanzhou Univ, Sch Informat Sci & Engn, Lanzhou 730000, Peoples R China
4.Changchun Univ Technol, Dept Control Engn, Changchun 130012, Peoples R China
推荐引用方式
GB/T 7714		 Jin, Long,Li, Jiachang,Sun, Zhongbo,et al. Neural Dynamics for Computing Perturbed Nonlinear Equations Applied to ACP-Based Lower Limb Motion Intention Recognition[J]. IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS,2021:9.
APA Jin, Long,Li, Jiachang,Sun, Zhongbo,Lu, Jingwei,&Wang, Fei-Yue.(2021).Neural Dynamics for Computing Perturbed Nonlinear Equations Applied to ACP-Based Lower Limb Motion Intention Recognition.IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS,9.
MLA Jin, Long,et al."Neural Dynamics for Computing Perturbed Nonlinear Equations Applied to ACP-Based Lower Limb Motion Intention Recognition".IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS (2021):9.

入库方式： OAI收割

来源：自动化研究所

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