# Design of Multi-Cell Cooperative Control Algorithm Based on Fuzzy Brain Emotional Learning
## 5 Simulation Results  
### Control Single Cell  
In this section, the superiority of the FBEL neural network-based cell manipulation controller proposed in this paper is verified by MATLAB experiments when using holographic optical tweezers. In practice, the environment in which cells live is complex and changeable. Additionally, the cell cannot move in a straight line. Although point-to-point linear control is easy to implement, it is necessary to avoid cell collision to ensure cell safety. Some researchers use circular trajectories to control cell movement. This mode of transportation is too limited [45]. The trajectory tracking control method is used to set the desired trajectory in advance to control cell transport. In this way, the collision between cells and obstacles in the process of moving can be effectively avoided [38]. In this paper, sine and cosine trajectories are chosen to realize long-distance transportation and avoid a collision. Suppose the cells move along the desired trajectory \(X^{d}=\left[{X_{x}}^{d},{X_{y}}^{d}\right]\), where  
\[\left\{{ \begin{array}{*{20}{c}}{{X_{x}}^{d}=35+t}\\ {{X_{y}}^{d}=30+35\sin(0.1t)\times\cos(0.06t)}\end{array} }\right. \tag{35}\]  
The cell environment solution coefficient and captured cell parameters were selected according to References [43,48]: captured cell radius \(R=5\)\(\mu\)m, \(\beta/K=0.1\), \(r_{0}=3.2\). According to (12)-(14) and (16)-(25), the FBEL neural network control parameters are selected: \(m=35\), the initial values of the adaptive weight matrix \(\varepsilon_{nm}\), \(\omega_{nm}\), \(D_{nm}\), and \(B_{nm}\) are randomly generated, the weight coefficient \(\alpha_{1}=100\), \(\alpha_{2}=1\), and the learning rate \(\eta_{D}=\eta_{B}=\eta_{\epsilon}=\eta_{\omega}=0.2\). Finally, set the simulation duration to 60s, the sampling time to 0.001, and the initial position of the captured cells is [2.65, 5]. It is important to better demonstrate the control effect of the cell manipulation controller based on the FBEL neural network (FBELNN) proposed in this paper. In addition, the PI controller (PI) [48] and PID controller (PID) [18] used in previous cell manipulation experiments are added, and the BP neural network [20], the RBF neural network (RBFNN) [21], and fuzzy cerebellar model neural network (FCMNN) [49] that are common in other control systems are used for comparison. The proportional coefficient and integral coefficient of the pi controller are set to 10 and 0.1, respectively, and the proportional coefficient, integral coefficient, and differential coefficient of the pi controller are set to 10,  
Figure 6: The structure of the multicellular cooperative controller based on the FBEL neural network. 0.1, and 0.2, respectively. The number of neurons of BP and RBFNN is set to 55, the number of layers of FCMNN is set to 35, and the number of blocks is set to 15. In the single-cell manipulation control experiment of the above solution environment and preset trajectory, the comparison of single-cell trajectory using each controller is shown in Figure 7.  
Figure 7 shows the effect comparison between the desired trajectory and the expected trajectory of a single cell when PI, PID, BP, RBFNN, FCMNN, and FBELNN are used as controllers, respectively. It can be seen from the figure that when the PI and PID controllers operate a single cell, they cannot control the cell to the desired trajectory in time for a long time, and can move accurately on the desired trajectory after a long time. In contrast, when BP, RBFNN, FCMNN are used as controllers to manipulate single cells, the control effect has been greatly improved, but there is still a problem that the initial manipulation fluctuates greatly and cannot move to the desired trajectory in time. The cell manipulation controller based on FBELNN can move to the desired trajectory in time, and will not produce severe jitter near the trajectory, showing a good control effect.