This paper is concerned with the stability analysis and $L_{1}$ -gain characterization for positive Takagi-Sugeno fuzzy systems subject to impulse effects. The main tool utilized is the construction of an impulse-time-dependent discretized copositive Lyapunov function, where the impulse interval partitioning idea is exploited to derive less conservative results. For the disturbance-free case, stability criteria are developed to guarantee global exponential stability of the concerned systems under ranged and constant dwell-time constraints. The analysis of the influence of the discretizing parameter on the reduction of conservativeness is carefully made. We further employ the proposed impulse-time-dependent discretized copositive Lyapunov function to analyze the $L_{1}$ -gain performance for the case where external disturbances are considered. By preforming a grid search for a scalar in an interval, all the proposed criteria can be checked via linear programming. Finally, numerical simulations are discussed to illustrate the effectiveness of the theoretical results.

Data-driven fault diagnosis, known to be simple and convenient, is more suitable for diagnosing the complicated spacecraft systems, e.g. the satellite power system. Nevertheless, it is difficult to extract the rules for diagnosing from unlabeled data. In this paper, a clustering approach based on neighborhood relationship and spatial grid partition is proposed to compensate for the above deficiency. In order to deal with the data-driven fault diagnosis issue, a diagnostic strategy is designed, which is a combination of the proposed clustering method and the entropy weight. Finally, multiple experiments, consisting of the artificial data clustering, comparison experiments on satellite data mining, and a case of fault diagnosis on satellite power system, are carried out to illustrate the versatility and superiority of the proposed method.