This paper mainly studies the trajectory tracking control problem of the manipulator of TianGong-2 when capturing floating objects in the experimental cabin. Microgravity environment will lead to the existing control system of space robot can not produce good control effect. Different gravity environments change the design requirements of space robots, and the ground test cannot meet these space requirements. In this paper, a fuzzy control strategy based on a spatial extended state observer(FC-SESO) is proposed to solve these inconsistent problems. First, a spatial extended state observer is designed to estimate the influence of disturbance on the system. In order to estimate the disturbance quickly and accurately, a disturbance model including gust is established. Second, in order to ensure the rapid convergence of the desired trajectory, the fuzzy control law is derived. Based on Lyapunov stability theory, the stability of the control system based on the spatial extended state observer is proven. Finally, the proposed system is simulated and captured on the Tiangong-2 manipulator platform to verify the effectiveness of the control algorithm.

In this paper, an algorithm based on model learning to calibrate gravity compensation parameters is proposed. This method can be used to solve the gravity term of the mechanical arm by linear regression method based only on the structure of the arm and the sampling data. The position of the gravity center and the geometric center of each mechanical arm link is not needed. It solves the problem that the model derived from the traditional dynamic modeling method produces a large deviation from the actual model. Theoretical analysis shows that the joint angle and joint driving torque can accurately regress the relationship between the barycentric coordinates of the link and the mass of the connecting rod in the Cartesian coordinate system, thus realizing the gravity compensation of the manipulator. The optimal solution on the optimization trade-off curve is presented and discussed.

The measurement accuracy of ultra-precision instruments such as large-scale scanning electron microscopes (SEM) is significantly affected by ground micro-vibrations (typically at the VC-D or VC-E level). The ultra-low micro-vibration levels and low signal-to-noise ratios impede the accuracy of practical models, posing significant challenges for control system implementation. This paper presents a data-based hybrid control strategy for ultra-low frequency vibration isolation to address the sensor noise and model dependency issues that affect the precision of control signals. To reduce the noise proportion in the measurement reference signals, a system state observer is developed to recursively update state estimates using sensor measurement information. A closed loop feedback controller based on model predictive control is designed that incorporates the system’s relative displacement, balancing resonance suppression and stability. To further suppress in-band vibrations, a data-driven feedforward control strategy was constructed, which incorporates a concise and stable generalized FIR filter for online parameter identification and leverages the rapid convergence of the Gauss-Newton iterative algorithm, thereby effectively over-coming the reliance on accurate prior parameters. Simulation and experimental results demonstrate that the proposed hybrid control strategy effectively suppresses vibrations at the system’s natural frequency, constrains relative displacement within 5 μm, and significantly reduces the vibration transmissibility in the 0.8-20 Hz frequency range. This research shows tremendous potential for engineering applications in micro-vibration isolation for ultra-precision microscope measurement equipment.