Design of Optical Tweezers Manipulation Control System Based on Novel Self-Organizing Fuzzy Cerebellar Model Neural Network
Holographic optical tweezers have unique non-physical contact and can manipulate and control single or multiple cells in a non-invasive way. In this paper, the dynamics model of the cells captured by the optical trap is analyzed, and a control system based on a novel self-organizing fuzzy cerebellar...
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| Format: | Article |
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MDPI AG
2022-09-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/12/19/9655 |
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| author | Jing Zhao Hui Hou Qi-Yu Huang Xun-Gao Zhong Peng-Sheng Zheng |
| author_facet | Jing Zhao Hui Hou Qi-Yu Huang Xun-Gao Zhong Peng-Sheng Zheng |
| author_sort | Jing Zhao |
| collection | DOAJ |
| description | Holographic optical tweezers have unique non-physical contact and can manipulate and control single or multiple cells in a non-invasive way. In this paper, the dynamics model of the cells captured by the optical trap is analyzed, and a control system based on a novel self-organizing fuzzy cerebellar model neural network (NSOFCMNN) is proposed and applied to the cell manipulation control of holographic optical tweezers. This control system consists of a main controller using the NSOFCMNN with a new self-organization mechanism, a robust compensation controller, and a higher order sliding mode. It can accurately move the captured cells to the expected position through the optical trap generated by the holographic optical tweezers system. Both the layers and blocks of the proposed NSOFCMNN can be adjusted online according to the new self-organization mechanism. The compensation controller is used to eliminate the approximation errors. The higher order sliding surface can enhance the performance of controllers. The distances between cells are considered in order to further realize multi-cell cooperative control. In addition, the stability and convergence of the proposed NSOFCMNN are proved by the Lyapunov function, and the learning law is updated online by the gradient descent method. The simulation results show that the control system based on the proposed NSOFCMNN can effectively complete the cell manipulation task of optical tweezers and has better control performance than other neural network controllers. |
| first_indexed | 2024-03-09T22:04:59Z |
| format | Article |
| id | doaj.art-e463aa2ae17c40e5a526815f9b36b468 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-09T22:04:59Z |
| publishDate | 2022-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-e463aa2ae17c40e5a526815f9b36b4682023-11-23T19:43:17ZengMDPI AGApplied Sciences2076-34172022-09-011219965510.3390/app12199655Design of Optical Tweezers Manipulation Control System Based on Novel Self-Organizing Fuzzy Cerebellar Model Neural NetworkJing Zhao0Hui Hou1Qi-Yu Huang2Xun-Gao Zhong3Peng-Sheng Zheng4School of Electrical Engineering & Automation, Xiamen University of Technology, Xiamen 361024, ChinaSchool of Electrical Engineering & Automation, Xiamen University of Technology, Xiamen 361024, ChinaSchool of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200030, ChinaSchool of Electrical Engineering & Automation, Xiamen University of Technology, Xiamen 361024, ChinaSchool of Electrical Engineering & Automation, Xiamen University of Technology, Xiamen 361024, ChinaHolographic optical tweezers have unique non-physical contact and can manipulate and control single or multiple cells in a non-invasive way. In this paper, the dynamics model of the cells captured by the optical trap is analyzed, and a control system based on a novel self-organizing fuzzy cerebellar model neural network (NSOFCMNN) is proposed and applied to the cell manipulation control of holographic optical tweezers. This control system consists of a main controller using the NSOFCMNN with a new self-organization mechanism, a robust compensation controller, and a higher order sliding mode. It can accurately move the captured cells to the expected position through the optical trap generated by the holographic optical tweezers system. Both the layers and blocks of the proposed NSOFCMNN can be adjusted online according to the new self-organization mechanism. The compensation controller is used to eliminate the approximation errors. The higher order sliding surface can enhance the performance of controllers. The distances between cells are considered in order to further realize multi-cell cooperative control. In addition, the stability and convergence of the proposed NSOFCMNN are proved by the Lyapunov function, and the learning law is updated online by the gradient descent method. The simulation results show that the control system based on the proposed NSOFCMNN can effectively complete the cell manipulation task of optical tweezers and has better control performance than other neural network controllers.https://www.mdpi.com/2076-3417/12/19/9655holographic optical tweezersself-organizing structurefuzzy cerebellar model neural networkcell manipulationcooperative control |
| spellingShingle | Jing Zhao Hui Hou Qi-Yu Huang Xun-Gao Zhong Peng-Sheng Zheng Design of Optical Tweezers Manipulation Control System Based on Novel Self-Organizing Fuzzy Cerebellar Model Neural Network Applied Sciences holographic optical tweezers self-organizing structure fuzzy cerebellar model neural network cell manipulation cooperative control |
| title | Design of Optical Tweezers Manipulation Control System Based on Novel Self-Organizing Fuzzy Cerebellar Model Neural Network |
| title_full | Design of Optical Tweezers Manipulation Control System Based on Novel Self-Organizing Fuzzy Cerebellar Model Neural Network |
| title_fullStr | Design of Optical Tweezers Manipulation Control System Based on Novel Self-Organizing Fuzzy Cerebellar Model Neural Network |
| title_full_unstemmed | Design of Optical Tweezers Manipulation Control System Based on Novel Self-Organizing Fuzzy Cerebellar Model Neural Network |
| title_short | Design of Optical Tweezers Manipulation Control System Based on Novel Self-Organizing Fuzzy Cerebellar Model Neural Network |
| title_sort | design of optical tweezers manipulation control system based on novel self organizing fuzzy cerebellar model neural network |
| topic | holographic optical tweezers self-organizing structure fuzzy cerebellar model neural network cell manipulation cooperative control |
| url | https://www.mdpi.com/2076-3417/12/19/9655 |
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