Citation: Zhao, J.; Hou, H.; Huang,
Q.-Y.; Zhong, X.-G.; Zheng, P.-S.
Design of Optical Tweezers
Manipulation Control System Based
on Novel Self-Organizing Fuzzy
Cerebellar Model Neural Network.
Appl. Sci. 2022, 12, 9655. https://
doi.org/10.3390/app12199655
Received: 5 August 2022
Accepted: 19 September 2022
Published: 26 September 2022
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Article
Design of Optical Tweezers Manipulation Control System
Based on Novel Self-Organizing Fuzzy Cerebellar Model
Neural Network
Jing Zhao
1,
*, Hui Hou
1
, Qi-Yu Huang
2,
* , Xun-Gao Zhong
1
and Peng-Sheng Zheng
1
1
School of Electrical Engineering & Automation, Xiamen University of Technology, Xiamen 361024, China
2
School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University,
Shanghai 200030, China
* Correspondence: jzhao@xmut.edu.cn (J.Z.); qiyu@sjtu.edu.cn (Q.-Y.H.)
Abstract:
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.
Keywords:
holographic optical tweezers; self-organizing structure; fuzzy cerebellar model neural
network; cell manipulation; cooperative control
1. Introduction
In modern biomedical engineering, the micro-manipulation of biological cells has
become a hot research object. At present, atomic force microscopy (AFM) [
1
], microstraw
technology [
2
], dielectrophoretic traps [
3
], magnetic tweezers [
4
] and other micromanipu-
lation techniques have been applied to the manipulation of biological cells. Holographic
optical tweezers, because of their unique non-physical contact and flexibility, do not have
the disadvantage that micro-straw technology may damage the structure of cells, nor do
magnetic tweezers lack the flexibility needed to control biological cells alone. It can sort [
5
],
transport [
6
] and perform other complex micro-operations [
7
–
12
] for cells, and thus plays
an increasingly important role in micro-manipulation. In recent years, many seniors have
conducted in-depth research on single-cell manipulation [
13
–
15
]. However, the manipula-
tion of single cells using holographic optical tweezers has always been challenging to meet
the needs of more advanced practical applications. Multicellular manipulation can achieve
more complex and more efficient practical tasks, such as using an optical trapping force to
gather multiple red blood cells to block blood vessels, or using optical forceps to pull red
blood cells to dredge blocked capillaries [6].
Appl. Sci. 2022, 12, 9655. https://doi.org/10.3390/app12199655 https://www.mdpi.com/journal/applsci
