# Design of Multi-Cell Cooperative Control Algorithm Based on Fuzzy Brain Emotional Learning
###### Abstract  
Multi-cell cooperative control can be competent for the current increasingly complex biomedical experiments, greatly improving the efficiency of cell manipulation experiments. At present, this kind of multi-cell cooperative control algorithm is becoming more and more important. In this study, holographic optical tweezers are used to capture multiple cells, and a cell manipulation controller is designed based on the Fuzzy Brain Emotional Learning (FBEL) neural network. Firstly, the dynamic model of trapping yeast cells by optical tweezers is analyzed. The distance between the trap position and the cell position is constrained to avoid cell detachment due to the trap moving too fast. Then, the design cell manipulation controller is relied upon to realize single transport trajectory tracking control. Finally, a multi-cell cooperative control algorithm is designed, and combined with the cell manipulation controller, a multi-cell cooperative controller based on the FBEL neural network is formed. The error between the cell position and the desired trajectory is the input of the multi-cell cooperative controller. The output of the multi-cell cooperative controller is the optical trap position, which is used to realize the cooperative control of multiple cells by holographic optical tweezers. The simulation results showed that the multi-cell cooperative controller based on the FBEL neural network can effectively control multiple yeast cells and quickly converge the cell formation, while ensuring a higher control accuracy than other traditional cell manipulation controllers. It provides a new solution for the efficient and precise automatic manipulation of multiple cells. holographic optical tweezers; multi-agent system; FBEL neural network; cooperative control; fuzzy system +
Footnote †: journal: Applied Sciences  
Such experiments can often show much greater application value than single-cell manipulation in practice. During the development of optical tweezers, various optical tweezers tools emerged [10; 11; 12; 13; 14]. Among them, holographic optical tweezers can simultaneously generate multiple optical traps [15], so that multiple cells can be captured and manipulated. This technique for generating multiple optical traps offers the possibility to efficiently manipulate and control multiple cells [16]. However, most of the previous studies on cells focused on the analysis of biological characteristics and application purposes of various cell experiments, while ignoring the process of manipulating cells. Most of the existing cell manipulation controls are manual, and the operation is complex, time-consuming, and unreliable in accuracy. Some scholars have used PI control and PID control [17; 18]. Simple feedback control strategies are also used for the manipulation and control of moving particles [19]. These methods have relatively low control precision for cells. While achieving high efficiency, multicellular manipulation should also satisfy high-precision control. Therefore, the high-precision automatic operation of cells has become one of the key breakthroughs in optical cell manipulation. With the research of neural network theory and application, more and more neural networks are used in various control systems, including back propagation (BP) neural network [20], Radial Basis Function (RBF) neural network [21], and other networks [22; 23; 24; 25], which effectively improves the control performance and has been verified in many studies. Among many neural networks, the neural network of the brain emotion learning model uniquely simulates the human brain mechanism, mathematically simulates the emotional activities of the amygdala and related parts of the brain, and can improve the efficiency of the neural network controller [26]. On the other hand, using fuzzy theory can better generalize uncertain data in reality [27; 28; 29]. The fuzzy reasoning system is introduced into the neural network of the brain emotion learning model to form the FBEL neural network.