RNN for Receding Horizon Control of Redundant Robot Manipulators
文献类型：期刊论文
| 作者 | Yan, Jingkun1,2; Jin, Long2,3; Yuan, Zhanting1; Liu, Zhiyi4 |
| 刊名 | IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
 |
| 出版日期 | 2022-02-01 |
| 卷号 | 69期号:2页码:1608-1619 |
| 关键词 | Manipulators Optimization Acceleration Recurrent neural networks Mathematical model Trajectory tracking Trajectory Joint limits receding horizon control (RHC) recurrent neural network (RNN) redundant manipulator trajectory tracking |
| ISSN号 | 0278-0046 |
| DOI | 10.1109/TIE.2021.3062257 |
| 通讯作者 | Jin, Long(jinlongsysu@foxmail.com) |
| 英文摘要 | Redundant manipulators have been studied and applied in many fields. The trajectory tracking of redundant manipulators is an important topic to explore for applications. This article aims to develop a planning scheme for achieving the trajectory tracking of redundant manipulators, from the receding horizon control (RHC) perspective. For the nonlinear model of manipulators, the linearization operation is conducted to obtain predictive outputs through the forward kinematic equation. Subsequently, an RHC scheme, which minimizes tracking error, velocity norm, and acceleration norm, and directly considers joint limits at three levels as well as the terminal equality constraint, is constructed and further simplified as a convex quadratic programming problem. Furthermore, a recurrent neural network (RNN) model is designed for the constructed RHC scheme, with the help of the technique of converting inequality constraints into equality constraints. The proposed RHC scheme solved by the RNN model is compared with other existing planning schemes and solvers through computer simulations and experiments, without and with the sudden external interference. Simulation and experiment results show that the proposed RHC scheme solved by the RNN model is able to make the redundant manipulator track the given trajectory excellently, and is superior to other existing schemes and solvers in terms of high efficiency, quick-response capacity, and strong robustness. |
| 资助项目 | National Key Research and Development Program of China[2017YFE0118900] ; research project of Huawei Mindspore Academic Award Fund of Chinese Association of Artificial Intelligence[CAAIXSJLJJ-2020-009A] ; Natural Science Foundation of Qinghai Province, China[2020-ZJ-903] ; Key Laboratory of IoT of Qinghai[2020-ZJ-Y16] ; Natural Science Foundation of Gansu Province, China[20JR10RA639] ; Natural Science Foundation of Chongqing (China)[cstc2020jcyj-zdxmX0028] ; Research and Development Foundation of Nanchong (China)[20YFZJ0018] ; CAS Light of West China Program ; Chongqing Key Laboratory of Mobile Communications Technology[cqupt-mct-202004] ; Fundamental Research Funds for the Central Universities[lzujbky-2019-89] ; Fundamental Research Funds for the Central Universities[lzuxxxy-2019-tm20] |
| WOS研究方向 | Automation & Control Systems ; Engineering ; Instruments & Instrumentation |
| 语种 | 英语 |
| WOS记录号 | WOS:000712582800050 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| 源URL | [http://119.78.100.138/handle/2HOD01W0/14416]  |
| 专题 | 中国科学院重庆绿色智能技术研究院 |
| 通讯作者 | Jin, Long |
| 作者单位 | 1.Lanzhou Univ, Sch Informat Sci & Engn, Lanzhou 730000, Peoples R China 2.Chinese Acad Sci, Chongqing Inst Green & Intelligent Technol, Chongqing 400714, Peoples R China 3.Lanzhou Univ, Dept Comp Sci, Lanzhou 730000, Peoples R China 4.Lanzhou Univ, Sch Nucl Sci & Technol, Lanzhou 730000, Peoples R China |
| 推荐引用方式 GB/T 7714 | Yan, Jingkun,Jin, Long,Yuan, Zhanting,et al. RNN for Receding Horizon Control of Redundant Robot Manipulators[J]. IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS,2022,69(2):1608-1619. |
| APA | Yan, Jingkun,Jin, Long,Yuan, Zhanting,&Liu, Zhiyi.(2022).RNN for Receding Horizon Control of Redundant Robot Manipulators.IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS,69(2),1608-1619. |
| MLA | Yan, Jingkun,et al."RNN for Receding Horizon Control of Redundant Robot Manipulators".IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS 69.2(2022):1608-1619. |
入库方式： OAI收割
来源：重庆绿色智能技术研究院
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