Multilayered hybrid time-varying problem solving based on integrated-enhanced zeroing neural network for robust manipulator control
Robustness is a significant research direction in manipulator control owing to their complicated and uncertain external environment, abrasion, and other factors. The ability to implement multitasking is also necessary for manipulator control because of the physical limitations and complex requiremen...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2023-10-01
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| Series: | Heliyon |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2405844023081793 |
| _version_ | 1797646450047320064 |
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| author | Yansong Zhao Jingjing Xiong |
| author_facet | Yansong Zhao Jingjing Xiong |
| author_sort | Yansong Zhao |
| collection | DOAJ |
| description | Robustness is a significant research direction in manipulator control owing to their complicated and uncertain external environment, abrasion, and other factors. The ability to implement multitasking is also necessary for manipulator control because of the physical limitations and complex requirements. However, the existing research has mainly focused on the control of a single task and robustness analysis of single-task control. Although some research on multi-task control has been conducted recently, its robustness has not yet been studied. Because of the excellent performance of the integrated-enhanced zeroing neural network in terms of robustness for time-varying problem solving, it was employed in this study to solve robust multi-task control. First, the multi-task control was formulated as a two-layered time-varying problem, including nonlinear and hybrid linear equations describing the tracking task and additional tasks, respectively. Second, an integrated-enhanced zeroing neural network was employed for the multilayered time-varying problem solving and a robust multi-task control algorithm was obtained, which can suppress different types of noises. Theoretical analyses demonstrated its effectiveness in multitasking and superior robustness compared with conventional algorithms. Finally, simulation results verified the theoretical results. |
| first_indexed | 2024-03-11T15:01:45Z |
| format | Article |
| id | doaj.art-67e17e4c134c48e3bc99b98c81528c41 |
| institution | Directory Open Access Journal |
| issn | 2405-8440 |
| language | English |
| last_indexed | 2024-03-11T15:01:45Z |
| publishDate | 2023-10-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Heliyon |
| spelling | doaj.art-67e17e4c134c48e3bc99b98c81528c412023-10-30T06:08:00ZengElsevierHeliyon2405-84402023-10-01910e20971Multilayered hybrid time-varying problem solving based on integrated-enhanced zeroing neural network for robust manipulator controlYansong Zhao0Jingjing Xiong1Department of Foreign Languages, Xinyang Normal University, Xinyang 464000, China; Corresponding author.Xinyang Central Hospital, Xinyang 464000, ChinaRobustness is a significant research direction in manipulator control owing to their complicated and uncertain external environment, abrasion, and other factors. The ability to implement multitasking is also necessary for manipulator control because of the physical limitations and complex requirements. However, the existing research has mainly focused on the control of a single task and robustness analysis of single-task control. Although some research on multi-task control has been conducted recently, its robustness has not yet been studied. Because of the excellent performance of the integrated-enhanced zeroing neural network in terms of robustness for time-varying problem solving, it was employed in this study to solve robust multi-task control. First, the multi-task control was formulated as a two-layered time-varying problem, including nonlinear and hybrid linear equations describing the tracking task and additional tasks, respectively. Second, an integrated-enhanced zeroing neural network was employed for the multilayered time-varying problem solving and a robust multi-task control algorithm was obtained, which can suppress different types of noises. Theoretical analyses demonstrated its effectiveness in multitasking and superior robustness compared with conventional algorithms. Finally, simulation results verified the theoretical results.http://www.sciencedirect.com/science/article/pii/S2405844023081793Manipulator controlMulti-task controlMultilayered hybrid time-varying problemIntegrated-enhanced zeroing neural dynamicsRobustness |
| spellingShingle | Yansong Zhao Jingjing Xiong Multilayered hybrid time-varying problem solving based on integrated-enhanced zeroing neural network for robust manipulator control Heliyon Manipulator control Multi-task control Multilayered hybrid time-varying problem Integrated-enhanced zeroing neural dynamics Robustness |
| title | Multilayered hybrid time-varying problem solving based on integrated-enhanced zeroing neural network for robust manipulator control |
| title_full | Multilayered hybrid time-varying problem solving based on integrated-enhanced zeroing neural network for robust manipulator control |
| title_fullStr | Multilayered hybrid time-varying problem solving based on integrated-enhanced zeroing neural network for robust manipulator control |
| title_full_unstemmed | Multilayered hybrid time-varying problem solving based on integrated-enhanced zeroing neural network for robust manipulator control |
| title_short | Multilayered hybrid time-varying problem solving based on integrated-enhanced zeroing neural network for robust manipulator control |
| title_sort | multilayered hybrid time varying problem solving based on integrated enhanced zeroing neural network for robust manipulator control |
| topic | Manipulator control Multi-task control Multilayered hybrid time-varying problem Integrated-enhanced zeroing neural dynamics Robustness |
| url | http://www.sciencedirect.com/science/article/pii/S2405844023081793 |
| work_keys_str_mv | AT yansongzhao multilayeredhybridtimevaryingproblemsolvingbasedonintegratedenhancedzeroingneuralnetworkforrobustmanipulatorcontrol AT jingjingxiong multilayeredhybridtimevaryingproblemsolvingbasedonintegratedenhancedzeroingneuralnetworkforrobustmanipulatorcontrol |