In order to mitigate the effects of micro-vibrations due to control moment gyroscopes (CMGs) on spacecraft attitude control system, they are often mounted on isolation platforms. However, the flexible deformation of isolators may cause certain disturbances in CMG gimbal servo systems. In addition, gimbal servo systems also suffer from intrinsic disturbances due to rotor imbalance and gimbal components. Since these disturbances are distributed over a wide frequency range, they are difficult to suppress and may seriously deteriorate gimbal control performance. To suppress multiple disturbances and improve gimbal speed accuracy, a composite anti-disturbance control method is proposed. The proposed method consists of two components. The first component adopts an adaptive proportional-integral-resonant controller with phase compensation to suppress disturbance due to isolator and rotor imbalance disturbance with improved transient performance. The second component adopts an adaptive extended state observer to estimate and then compensate slowly varying disturbances with improved dynamic performance and steady-state accuracy. By integrating these two components, the proposed method can effectively suppress multiple disturbances in CMG gimbal servo systems. Simulation and experimental results demonstrate the superior performance of the proposed method.

Far-field vision-based pose measurement is a crucial technology for applications such as high-resolution Earth observation and space security early warning. However, owing to the perspective imaging model of long-range optical systems, conventional vision-based pose measurement methods are highly susceptible to image noise and pose parameter coupling, leading to significant estimation deviations. Consequently, these methods fail to meet the rigorous requirements for the accurate measurement and intelligent perception of object poses in far-field scenarios, particularly when the object distance significantly exceeds the focal length. To address these challenges, this paper presents a Perspective-n-Point (PnP) preprocessing and post-optimization method for far-field pose measurement based on weighted central normalization. First, the Robust PnP (RPnP) algorithm is employed to obtain an initial pose for the far-field object, and an objective function is formulated by minimizing the reprojection error of the image feature points. Second, central normalization is applied to the Jacobian matrix of the pose parameters, and the information matrix is weighted according to the localization uncertainty of the image feature points. Finally, a weighted nonlinear optimization is executed to obtain refined pose parameters. Under the tested conditions, this approach can reduce the sensitivity of the pose parameters to image noise, minimizes the coupling among extrinsic parameters, and reduces the tendency of noise-driven pose-update excursions. The proposed method is evaluated through simulations and scaled physical relative-comparison experiments, supporting its potential for numerically stable vision-based pose measurement of far-field objects in aerospace and related domains. Noise-and-turbulence simulations demonstrate the pose-refinement benefit of CS and improved rotation estimation with a known spatial covariance model.