Modified Primal-Dual Neural Networks for Motion Control of Redundant Manipulators With Dynamic Rejection of Harmonic Noises
文献类型：期刊论文
| 作者 | Li, Shuai1; Zhou, MengChu2,3; Luo, Xin4
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| 刊名 | IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
 |
| 出版日期 | 2018-10-01 |
| 卷号 | 29期号:10页码:4791-4801 |
| 关键词 | Dual neural network kinematic control redundancy resolution robotic manipulator |
| ISSN号 | 2162-237X |
| DOI | 10.1109/TNNLS.2017.2770172 |
| 英文摘要 | In recent decades, primal-dual neural networks, as a special type of recurrent neural networks, have received great success in real-time manipulator control. However, noises are usually ignored when neural controllers are designed based on them, and thus, they may fail to perform well in the presence of intensive noises. Harmonic noises widely exist in real applications and can severely affect the control accuracy. This work proposes a novel primal-dual neural network design that directly takes noise control into account. By taking advantage of the fact that the unknown amplitude and phase information of a harmonic signal can be eliminated from its dynamics, our deliberately designed neural controller is able to reach the accurate tracking of reference trajectories in a noisy environment. Theoretical analysis and extensive simulations show that the proposed controller stabilizes the control system polluted by harmonic noises and converges the position tracking error to zero. Comparisons show that our proposed solution consistently and significantly outperforms the existing primal-dual neural solutions as well as feedforward neural one and adaptive neural one for redundancy resolution of manipulators. |
| 资助项目 | National Key Research and Development Program of China[2017YFC0804002] ; National Natural Science Foundation of China[61401385] ; National Natural Science Foundation of China[61772493] ; Hong Kong Research Grants Council Early Career Scheme[25214015] ; Departmental General Research Fund of The Hong Kong Polytechnic University[G.UA7L] ; Pioneer Hundred Talents Program of the Chinese Academy of Sciences |
| WOS研究方向 | Computer Science ; Engineering |
| 语种 | 英语 |
| WOS记录号 | WOS:000445351300020 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| 源URL | [http://119.78.100.138/handle/2HOD01W0/6724]  |
| 专题 | 大数据挖掘及应用中心 |
| 通讯作者 | Zhou, MengChu; Luo, Xin |
| 作者单位 | 1.Hong Kong Polytech Univ, Dept Comp, Hong Kong, Hong Kong, Peoples R China 2.Macau Univ Sci & Technol, Inst Syst Engn, Macau 999078, Peoples R China 3.New Jersey Inst Technol, Dept Elect & Comp Engn, Newark, NJ 07102 USA 4.Chinese Acad Sci, Chongqing Inst Green & Intelligent Technol, Chongqing 400714, Peoples R China |
| 推荐引用方式 GB/T 7714 | Li, Shuai,Zhou, MengChu,Luo, Xin. Modified Primal-Dual Neural Networks for Motion Control of Redundant Manipulators With Dynamic Rejection of Harmonic Noises[J]. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,2018,29(10):4791-4801. |
| APA | Li, Shuai,Zhou, MengChu,&Luo, Xin.(2018).Modified Primal-Dual Neural Networks for Motion Control of Redundant Manipulators With Dynamic Rejection of Harmonic Noises.IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,29(10),4791-4801. |
| MLA | Li, Shuai,et al."Modified Primal-Dual Neural Networks for Motion Control of Redundant Manipulators With Dynamic Rejection of Harmonic Noises".IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 29.10(2018):4791-4801. |
入库方式： OAI收割
来源：重庆绿色智能技术研究院
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