Different-layer control of robotic manipulators based on a novel direct-discretization RNN algorithm
In this paper, the development of discrete-time recurrent neural network (RNN) algorithm is different from previous studies that require the derivation processes in the continuous-time environment. Specifically, the problem of discrete time-dependent different-layer control (DTDDLC) of robotic manip...
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| Published in | Neurocomputing (Amsterdam) Vol. 620; p. 129252 |
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| Main Authors | , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Elsevier B.V
01.03.2025
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| Subjects | |
| Online Access | Get full text |
| ISSN | 0925-2312 |
| DOI | 10.1016/j.neucom.2024.129252 |
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| Abstract | In this paper, the development of discrete-time recurrent neural network (RNN) algorithm is different from previous studies that require the derivation processes in the continuous-time environment. Specifically, the problem of discrete time-dependent different-layer control (DTDDLC) of robotic manipulators is investigated. To solve the DTDDLC problem, a discrete-time RNN algorithm based on direct-discretization technique is proposed, and thus named direct-discretization RNN algorithm for convenience. For comparative purposes, four indirect-discretization RNN algorithms are also developed to solve the same problem. Theoretical analyses and results manifest the convergence performance of the proposed direct-discretization RNN algorithm. Finally, numerical experiments based on a five-link planar robotic manipulator and a UR3 robotic manipulator substantiate the effectiveness and superiority of the proposed direct-discretization RNN algorithm. |
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| AbstractList | In this paper, the development of discrete-time recurrent neural network (RNN) algorithm is different from previous studies that require the derivation processes in the continuous-time environment. Specifically, the problem of discrete time-dependent different-layer control (DTDDLC) of robotic manipulators is investigated. To solve the DTDDLC problem, a discrete-time RNN algorithm based on direct-discretization technique is proposed, and thus named direct-discretization RNN algorithm for convenience. For comparative purposes, four indirect-discretization RNN algorithms are also developed to solve the same problem. Theoretical analyses and results manifest the convergence performance of the proposed direct-discretization RNN algorithm. Finally, numerical experiments based on a five-link planar robotic manipulator and a UR3 robotic manipulator substantiate the effectiveness and superiority of the proposed direct-discretization RNN algorithm. |
| ArticleNumber | 129252 |
| Author | Qiu, Binbin Guo, Jinjin Xiao, Zhanhao Hu, Xianglei Guo, Jianhua |
| Author_xml | – sequence: 1 givenname: Jinjin orcidid: 0000-0001-5193-8822 surname: Guo fullname: Guo, Jinjin organization: School of Computer Science, Guangdong Polytechnic Normal University, Guangzhou 510665, China – sequence: 2 givenname: Zhanhao surname: Xiao fullname: Xiao, Zhanhao organization: School of Computer Science, Guangdong Polytechnic Normal University, Guangzhou 510665, China – sequence: 3 givenname: Jianhua surname: Guo fullname: Guo, Jianhua organization: School of Computer Science, Guangdong Polytechnic Normal University, Guangzhou 510665, China – sequence: 4 givenname: Xianglei surname: Hu fullname: Hu, Xianglei organization: School of Computer Science, Guangdong Polytechnic Normal University, Guangzhou 510665, China – sequence: 5 givenname: Binbin surname: Qiu fullname: Qiu, Binbin email: qiubb6@mail.sysu.edu.cn organization: School of Intelligent Systems Engineering, Sun Yat-sen University, Shenzhen 518057, China |
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| Keywords | Convergence performance Recurrent neural network Robotic manipulators Direct-discretization technique Discrete time-dependent different-layer control |
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| SubjectTerms | Convergence performance Direct-discretization technique Discrete time-dependent different-layer control Recurrent neural network Robotic manipulators |
| Title | Different-layer control of robotic manipulators based on a novel direct-discretization RNN algorithm |
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