# Design of Multi-Cell Cooperative Control Algorithm Based on Fuzzy Brain Emotional Learning
###### Abstract
Compared with other traditional neural networks, the FBEL neural network makes full use of prior knowledge in the fuzzy control and expresses knowledge with a small number of fuzzy rules. The received external data information can be learned more effectively, the fitting ability of nonlinear functions is enhanced, and the learning ability of the network is improved. Therefore, it has been widely used in various application domains, such as classification, signal processing, nonlinear system approximation, etc. [30; 31; 32]. Due to its excellent nonlinear function approximation ability, applying it to cell manipulation will effectively improve the manipulation accuracy of experiments. In view of the low efficiency of manual manipulation in the current cell manipulation, there is an application limitation of single-cell manipulation and an inability of cell control precision to meet the increasing demand for high-precision cell manipulation. This paper aims to study a new efficient and accurate cooperative control method for cell manipulation in a holographic optical tweezers system, in order to improve the manipulation efficiency and control precision of single cells and multi-cells. Holographic optical tweezers have the advantage of creating multiple optical traps to simultaneously capture cells and avoid contact damage. In this paper, the dynamic model of the cells captured by the optical trap generated by holographic optical tweezers is firstly established, and the constraints on the distance between the optical trap and the cells are added to prevent the abnormal damage of the cells caused by the high-speed movement of the optical trap, ensuring the control cells can be stably captured by the optical trap. Secondly, a FBEL neural network-based cell manipulation controller for holographic optical tweezers is designed and proposed, and the Lyapunov theory is used to verify the convergence of the controller's core FBEL neural network. In order to realize the coordinated movement of multi-cells, the multi-cell dynamics model is extended. A virtual reference point and the corresponding virtual cells at the reference point are added to the multi-cell, and a matrix of expected spacing of each cell is introduced. By designing the multi-cell cooperative control algorithm and combining the cell manipulation controller, a multi-cell cooperative controller is proposed to realize the multi-cell cooperative control. Finally, take yeast cells as an example. Through simulation experiments, it is confirmed that the cell cooperative controller based on the FBEL neural network can successfully realize the control of a single yeast cell and the cooperative control of multiple yeast cells. Compared with other traditional cell manipulation controllers and other neural networks as controllers, the proposed FBEL neural network cooperative controller has smaller evaluation index values and better control performance. It provides strong technical support for the high-precision automatic manipulation of cells in modern biomedical engineering. The rest of this paper is organized as follows: Section 2 discusses the principle of the optical tweezers system and the dynamic model of holographic optical tweezers to manipulate cell motion. Section 3 is the design of the cell manipulation controller, including the network structure and convergence analysis of the FBEL neural network. Section 4 describes the multicellular dynamics model and the design method of the cooperative controller. Section 5 is the simulation experiment verification. Section 6 is the conclusion.