###### Abstract  
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. holographic optical tweezers; self-organizing structure; fuzzy cerebellar model neural network; cell manipulation; cooperative control +
Footnote †: journal: Applied Sciences  
In the manipulation of optical tweezers, some scholars use PI control and PID control [16; 17]; however, it is still possible to improve control performance further. With the research and development of neural networks, more and more neural networks have been applied to various projects as controllers in recent years [18; 19; 20]. On the other hand, the use of fuzzy theory can effectively reflect the inaccurate data in reality [21]. Many papers have carried out further research on fuzzy mathematics [22], fuzzy inference [23] and other related issues [24; 25; 26]. Regarding the fuzzy cerebellar model neural network (FCMNN), because fuzzy rules are added to the cerebellum model, the neural network has the advantages of local fast approximation of cerebellar neural network and strong generalization of fuzzy rules to data. In various engineering experiments, such as multi-dimensional classification, signal processing, approximation and other control issues [27; 28; 29; 30; 31; 32], after many studies of verification by predecessors, it currently possesses a good approximation ability to uncertain linear systems. Therefore, this paper considers the application of FCMNN in cell manipulation to improve the accuracy of cell manipulation. In most cases, it is not possible for FCMNN to obtain the appropriate structure directly. The size of the storage space is difficult to determine, which will affect the learning effect of the network. Therefore, some FCMNNs with self-organizing characteristics are proposed [33; 34; 35]. However, these FCMNNs have problems such as complex methods, a weak ability to adapt to online real-time, and no analysis of structure deletion. Aiming at mitigating these deficiencies, a NSOFCMNN is proposed in this paper, which can change the number of layers and blocks of the network with clearer self-organizing rules, and adjust it online in real time. Due to the inefficiency of single-cell manipulation, cell manipulation experiments require high control accuracy, and it is difficult for the control performance of traditional controllers to meet the current experimental needs. The purpose of this paper is to study an efficient and precise control method for manipulating cells in a holographic optical tweezers system to improve the above problems.