Satellite pointing mechanisms for earth observation require ultra-low speed scanning (approximately ${70}^{″}/\mathrm{s}$) and precise variable-speed compensation. However, traditional Field-Oriented Control (FOC) suffers from significant velocity bias and instability under these conditions. To address these issues, this paper proposes a position-loop-based speed control scheme integrated with a variable structure control strategy. By substituting the speed command with a position loop, the proposed method effectively suppresses steady-state velocity bias, while the variable structure strategy mitigates fluctuations during variable-speed motion. Experimental results indicate that, compared to traditional FOC, the proposed method reduces velocity bias error by over $30\%$ during uniform tracking and decreases the amplitude of velocity fluctuations by more than $40\%$ in variable-speed scenarios. This strategy significantly enhances the control precision of satellite pointing mechanisms and improves on-orbit imaging compensation accuracy.

This paper presents a space-based limb-imaging spectrometer (LIS) for detecting atmospheric O₂ airglow; it scans the atmosphere with a vertical range of 10–100 km and has a vertical resolution of 2 km. The LIS’s detection performance needs to be examined before launch. A forward radiative transfer model (RTM) of airglow is studied to determine the airglow emission intensity. Spectral and radiation calibration is conducted to obtain the response parameters. Based on the airglow emission intensity, calibration results, and airglow spectral lines, the LIS’s simulated spectra are obtained, and then an optimal estimation inversion method for the LIS is studied. The results show that the LIS’s spectral range is 498.1 nm–802.3 nm, with a spectral resolution of 1.38 nm. Simulation results show that the LIS can detect airglow emission spectral lines, which characterize their dependence on temperature. The digital number response value is 20% to 50% of the saturation value. An inversion error analysis shows that, when the signal-to-noise ratio (SNR) of the LIS is 1000 and the prior temperature error is 10%, the inversion errors are 6.2 and 3 K at 63 and 77 km, respectively. This study shows that the LIS can achieve good SNR detection for airglow.