This brief investigates the adaptive sliding mode control based on the radial basis function (RBF) neural network for T-S fuzzy fractional order systems. The RBF neural network is used to approximate the nonlinearities and external disturbances. Then, the switching control term is represented as a proportional integral control format to reduce the chattering phenomenon. The conditions of the sliding mode controller are given to ensure the stability of the control system. Finally, the efficiency of the conditions is demonstrated by a permanent magnet synchronous motor (PMSM) example, i.e., it shows that the designed controller can stabilize T-S fuzzy fractional order systems.

Single-cell transportation is one of the most common cell operations. Transporting cells with micropipettes is convenient for a wide range of biomedical applications. For high-efficiency cell transportation, the cells must be aspirated into the orifice of a micropipette. However, this is very difficult to achieve, as there is relative movement between the cell and the culture medium when the fluid drives the cell in the culture medium. It is crucial to use cell dynamics rather than fluid dynamics as the control objects to improve control performance and stop the cell immediately when it approaches the micropipette. In this study, the cell dynamics were modeled using a second-order model by integrating the dynamic model between the fluid and the cell into a first-order fluid dynamic model. A backstepping controller-based extended state observer was proposed to control the cell movement inside the micropipette. Experiments demonstrated that the proposed controller could aspirate cells into the orifice of the micropipette with high accuracy and no overshoot. Furthermore, the proposed controller was applied to automated somatic cell nuclear transfer, and it significantly boosted operational efficiency. Note to Practitioners —The need to apply advanced automation methods to transfer cells in life sciences has increased at a steady pace. The key feature of such systems is the ability to select and transfer cells at a predetermined position in space and time for biological applications. We propose a cell positioning control method based on vision-guided robotics that can directly aspirate cells to specified positions near the orifice of a micropipette. In somatic cell nuclear transfer, the proposed method of transferring somatic cells into oocytes occurs at a faster pace than manual operation. This provides essential functionality for single-cell transfer and is an appropriate technology for practitioners with this functional requirement.

In this paper, robust $H_{\infty }$ control for fractional-order switched systems (FOSSs) with uncertainty is studied. Firstly, the fractional-order switching law for FOSSs is proposed. Then, $H_{\infty }$ control for FOSSs is proven based on the switching law and linear matrix inequalities (LMIs). Moreover, $H_{\infty }$ control for FOSSs with a state feedback controller is extended. Furthermore, the LMI-based condition of robust $H_{\infty }$ control for FOSSs with uncertainty is proven. Furthermore, the condition of robust $H_{\infty }$ control is proposed to design the state feedback controller. Finally, four simulation examples verified the effectiveness of the proposed methods.

In the paper, $H_{\infty }$ and robust $H_{\infty }$ control for fractional order systems (FOS) with order $0<\alpha <1$ are studied. Firstly, necessary and sufficient conditions of $H_{\infty }$ control and state feedback controller design are proposed. Then, robust $H_{\infty }$ control for FOS with uncertainty is studied, and state feedback controller is designed. These conditions are based on linear matrix inequalities (LMI) and can be easily solved by the LMI toolbox. Finally, the effectiveness of these conditions is verified by two numerical examples.