# Design of Multi-Cell Cooperative Control Algorithm Based on Fuzzy Brain Emotional Learning
## 5 Simulation Results
### Control Multiple Cells
The farther the initial position of the cells is from the expected trajectory, the slower the trajectory error converges. At the same time, large errors tend to increase the collision risk of multi-cell cooperative control and reduce the control efficiency. When BP, RBFNN, and FCMNN are used as controllers, the errors of the three cells in the \(X\)-axis direction and the \(Y\)-axis direction can quickly converge to near 0, but as the trajectory changes, the error will still fluctuate near 0. The cell cooperative controller based on the  
\(7.8\times 10^{-6}\)], [\(1.4\times 10^{-6},3.6\times 10^{-6}\)], [\(1.4\times 10^{-6},3.7\times 10^{-6}\)], and [\(1.2\times 10^{-6},3.4\times 10^{-6}\)], respectively. Among these controllers, FCMNN has a smaller range of RMSE values and relatively better performance. Then, the FBELNN-based cell cooperative controller proposed in this paper has a large control accuracy advantage compared with the former, and the RMSE value range is between [\(0.6\times 10^{-6},2.7\times 10^{-6}\)]. Compared with the FCMNN controller, the minimum RMSE value and the maximum RMSE value of the cell cooperative controller proposed in this paper are reduced by 51% and 22%, respectively. It greatly improves the control accuracy in multi-cell cooperative control, and is a cell cooperative controller with excellent control performance and stability. Figure 12: The trajectory error on the \(X\)-axis (multicellular manipulation): (**a**) Use PI as a controller. (**b**) Use PID as a controller. (**c**) Use BP as a controller. (**d**) Use RBFNN as a controller. (**e**) Use FCMNN as a controller. (**f**) Use FBELNN as a controller. Figure 13: The trajectory error on the \(Y\)-axis (multicellular manipulation): (**a**) Use PI as a controller. (**b**) Use PID as a controller. (**c**) Use BP as a controller. (**d**) Use RBFNN as a controller. (**e**) Use FCMNN as a controller. (**f**) Use FBELNN as a controller. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Data Type** & **Main Controller** & **Cell 1** & **Cell 2** & **Cell 3** \\ \hline \multirow{3}{*}{RMSE of each cell in the \(X\)-axis direction} & PI & \(8.2016\times 10^{-6}\) & \(5.7012\times 10^{-6}\) & \(3.4229\times 10^{-6}\) \\  & PID & \(7.7836\times 10^{-6}\) & \(5.4168\times 10^{-6}\) & \(3.2786\times 10^{-6}\) \\ \cline{1-1}  & BP & \(3.5322\times 10^{-6}\) & \(3.4547\times 10^{-6}\) & \(2.8029\times 10^{-6}\) \\ \cline{1-1}  & RBFNN & \(3.7211\times 10^{-6}\) & \(3.1396\times 10^{-6}\) & \(2.6596\times 10^{-6}\) \\ \cline{1-1}  & FCMNN & \(3.3970\times 10^{-6}\) & \(2.9586\times 10^{-6}\) & \(2.3933\times 10^{-6}\) \\ \cline{1-1}  & This work & \(2.6599\times 10^{-6}\) & \(2.2398\times 10^{-6}\) & \(1.4882\times 10^{-6}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: RMSE of multicellular manipulation. In reality, the cell's environment might change. For example, changes in the coefficient of viscous resistance will cause changes in the stress of cells. Therefore, according to Table 1, the value of \(\beta/\text{k}\) is changed to 0.09 for the experiment. In order to further verify the robustness of the cell cooperative controller based on FBELNN proposed in this paper, the expected trajectory was changed at the same time, and the expected spacing matrix of each yeast cell in the triangle formation was set, \(\chi= \begin{bmatrix}25&50*\sin(0*pi/3)\\ -30&77*\sin(2*pi/3)\\ -30&70*\sin(4*pi/3)\\ \end{bmatrix} \). All other experimental parameters remained unchanged. The new ideal trajectory for the experiment is designed as:  
\[ \begin{cases}{X_{x}}^{d}=35+t\\ {X_{y}}^{d}=30+15\cos(0.1t)\times\sin(t)\end{cases}  \tag{37}\]  
The initial position of the captured cells is shown in Table 5.  
The cell trajectories of the vertical formation and the triangular formation are shown in Figures 14 and 15, respectively.