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.

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.