This paper addresses the issue of agile attitude maneuver control for low-Earth-orbit satellites during short arc segments for multi-target observations. Specifically, a configuration design for Control Moment Gyroscopes (CMGs) and a hybrid control law are provided. The control law is adept at avoiding singularities and escaping singular planes. Subsequently, an optimal time-based attitude maneuver path-planning method is presented, rooted in the relationship between Euler angles/axis and quaternions. Furthermore, a novel satellite attitude maneuver controller is developed based on a piecewise power-reaching law for variable structure sliding mode control. The paper theoretically demonstrates that the proposed piecewise power reaching law possesses two favorable properties regarding convergence time. On the other hand, the designed reaching law maintains continuity at all stages, theoretically eliminating buffeting. The simulation results demonstrate that the proposed controller achieves an Euler angle control precision of ±0.03° and angular velocity accuracy of ±0.15°/s, fulfilling the demands of multi-objective observational tasks. Compared to conventional power reaching law controllers, the convergence time is reduced by 3 s, and Euler angle accuracy is improved by 70%. This underscores the effectiveness of the proposed algorithm.

Aiming at the classification identification problem of aeroengines, this paper adopts telemetry Fourier Transform Infrared Spectrometer to collect aeroengine hot jet infrared spectrum data, and proposes an aeroengine classification identification method based on spectral feature vectors. First, aeroengine hot jet infrared spectrum data are acquired and measured, meanwhile, the spectral feature vectors based on CO2 are constructed. Then, the feature vectors are combined with the seven mainstream classification algorithms to complete the training and prediction of the classification model. In the experiment, two Fourier transform infrared spectrometers, EM27 developed by Bruker and a self-developed telemetry FTIR, were used to telemeter the hot jet of three aeroengines to obtain infrared spectral data. The training data set and test data set were randomly divided in a ratio of 3:1. The model training of the training data set and the label prediction of the test data set were carried out by combining spectral feature vectors and classification algorithms. The classification evaluation indicators were accuracy, precision, recall, confusion matrix, and F1-score. The classification recognition accuracy of the algorithm was approximately 98%. This paper has great significance for the fault diagnosis of aeroengines and classification recognition of aircrafts.