Collaborative control of multi-manipulator systems in intelligent manufacturing based on event-triggered and adaptive strategy
Multi-manipulator systems face challenges in coordinating their movement in complex environments. To enhance the collaborative control stability of multi-manipulator systems in intelligent manufacturing, this study utilizes event-triggered (ET) mechanisms to reduce the signal transmission burden and...
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| Format: | Article |
| Language: | English |
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De Gruyter
2024-12-01
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| Series: | Nonlinear Engineering |
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| Online Access: | https://doi.org/10.1515/nleng-2024-0051 |
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| _version_ | 1841536612207427584 |
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| author | Leng Xuefeng |
| author_facet | Leng Xuefeng |
| author_sort | Leng Xuefeng |
| collection | DOAJ |
| description | Multi-manipulator systems face challenges in coordinating their movement in complex environments. To enhance the collaborative control stability of multi-manipulator systems in intelligent manufacturing, this study utilizes event-triggered (ET) mechanisms to reduce the signal transmission burden and frequency, and combines adaptive strategy to solve interference factors in complex environments. In addition, the study combines adaptive strategy with neural network structure using adaptive neural network control methods, and adopts ET mechanism to design auxiliary variables. Then, the neural network approximates the nonlinear uncertain model of the system online to cope with external disturbances and improve the robustness. From the results, the maximum fitting error of the multi-manipulator system based on ET and adaptive strategy was 0.59%, which was 2.05 and 3.99% lower than the errors of the other two advanced control systems, respectively. In summary, the research on multi-manipulator systems in intelligent manufacturing on the basis of ET and adaptive strategy effectively improved its control stability in intelligent manufacturing. |
| format | Article |
| id | doaj-art-14772fb3394b417a957d065d29c48d93 |
| institution | Kabale University |
| issn | 2192-8029 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | De Gruyter |
| record_format | Article |
| series | Nonlinear Engineering |
| spelling | doaj-art-14772fb3394b417a957d065d29c48d932025-01-14T13:23:12ZengDe GruyterNonlinear Engineering2192-80292024-12-0113112031010.1515/nleng-2024-0051Collaborative control of multi-manipulator systems in intelligent manufacturing based on event-triggered and adaptive strategyLeng Xuefeng0School of Intelligent Control, Changzhou Vocational Institute of Industry Technology, Changzhou, 213164, ChinaMulti-manipulator systems face challenges in coordinating their movement in complex environments. To enhance the collaborative control stability of multi-manipulator systems in intelligent manufacturing, this study utilizes event-triggered (ET) mechanisms to reduce the signal transmission burden and frequency, and combines adaptive strategy to solve interference factors in complex environments. In addition, the study combines adaptive strategy with neural network structure using adaptive neural network control methods, and adopts ET mechanism to design auxiliary variables. Then, the neural network approximates the nonlinear uncertain model of the system online to cope with external disturbances and improve the robustness. From the results, the maximum fitting error of the multi-manipulator system based on ET and adaptive strategy was 0.59%, which was 2.05 and 3.99% lower than the errors of the other two advanced control systems, respectively. In summary, the research on multi-manipulator systems in intelligent manufacturing on the basis of ET and adaptive strategy effectively improved its control stability in intelligent manufacturing.https://doi.org/10.1515/nleng-2024-0051adaptive strategymulti-manipulatorscollaborative controlneural networksintelligent manufacturing |
| spellingShingle | Leng Xuefeng Collaborative control of multi-manipulator systems in intelligent manufacturing based on event-triggered and adaptive strategy Nonlinear Engineering adaptive strategy multi-manipulators collaborative control neural networks intelligent manufacturing |
| title | Collaborative control of multi-manipulator systems in intelligent manufacturing based on event-triggered and adaptive strategy |
| title_full | Collaborative control of multi-manipulator systems in intelligent manufacturing based on event-triggered and adaptive strategy |
| title_fullStr | Collaborative control of multi-manipulator systems in intelligent manufacturing based on event-triggered and adaptive strategy |
| title_full_unstemmed | Collaborative control of multi-manipulator systems in intelligent manufacturing based on event-triggered and adaptive strategy |
| title_short | Collaborative control of multi-manipulator systems in intelligent manufacturing based on event-triggered and adaptive strategy |
| title_sort | collaborative control of multi manipulator systems in intelligent manufacturing based on event triggered and adaptive strategy |
| topic | adaptive strategy multi-manipulators collaborative control neural networks intelligent manufacturing |
| url | https://doi.org/10.1515/nleng-2024-0051 |
| work_keys_str_mv | AT lengxuefeng collaborativecontrolofmultimanipulatorsystemsinintelligentmanufacturingbasedoneventtriggeredandadaptivestrategy |