This paper presents a novel fault diagnosis method based on data fusion for a reaction flywheel of the satellite attitude system. Different from most traditional fault diagnosis techniques, the proposed solution simultaneously accomplishes fault detection and identification within parallel fusion blocks. The core of this method is independent fusion block, which uses a generalized ordered weighted average (GOWA) operator to complement the characteristics of output data from long short-term memory (LSTM) neural network and discrete wavelet transform (DWT) so as to enhance the reliability and rapidity of decision-making. Moreover, minibatch normalization is selected to address the problem of covariate shift, realize the adaptive processing of the dynamic information in the original data, and improve the convergence speed of the network. With the high-fidelity model of the reaction flywheel, three common faults are, respectively, injected to collect experimental data. Extensive experiment results show the efficacy of the proposed method and the excellent performance achieved by LSTM and DWT.

The attitude tracking synchronization control of an orbit-predetermined leader–follower spacecraft swarm for the space moving target is discussed in this paper. The information exchange between all spacecraft is assumed to be discrete in time and on the undirected connected graph. Moreover, due to the demand for saving communication resources, wireless interference has been utilized, which allows all the neighbors of a spacecraft to access the same channel frequency spectrum simultaneously. Then the backstepping control algorithm is designed to let the spacecraft $\left(\beta ,\mathit{A}\right)$-practically stably synchronize their states and track a time-varying trajectory in the presence of unknown fading channels. Finally, simulation is provided to verify that using the proposed control scheme, the attitude tracking synchronization can be achieved with high precision.