The modular system can change its physical structure by self-assembly and self-disassembly between modules to dynamically adapt to task and environmental requirements. Recognizing the adaptive capability of modular systems, we introduce a modular reconfigurable flight array (MRFA) to pursue a multifunction aircraft fitting for diverse tasks and requirements, and investigate the attitude control and the control allocation problem by using the modular reconfigurable flight array as a platform. First, considering the variable and irregular topological configuration of the modular array, a center-of-mass-independent flight array dynamics model is proposed to allow control allocation under over-actuated situations. Secondly, in order to meet the stable, fast and accurate attitude tracking performance of the MRFA, a fixed-time convergent sliding mode controller with state-dependent variable exponent coefficients is proposed to ensure fast convergence rate both away from and near the system equilibrium point without encountering the singularity. It is shown that the controller also has fixed-time convergent characteristics even in the presence of external disturbances. Finally, simulation results are provided to demonstrate the effectiveness of the proposed modeling and control strategies.

As the core component of the copper top-blown smelting, the service life of the injection lance critically affects production stability. To monitor the operating condition of the injection lance, a data-driven model is proposed to predict the Remaining Useful Life (RUL) or service life, namely, the DKT-LSSVM model. Firstly, to reduce noise interference, the Daubechies wavelet with four vanishing moments (DB4) denoising is used to process the raw data. Then, the Kernel Principal Component Analysis (KPCA) method is utilized to extract the principal components from the denoised data, which retains at least 90% information content (18 principal components are obtained). These principal components are used as inputs to a Least Square Support Vector Machine (LSSVM) model to predict the RUL of the injection lance, and a Tuna Swarm Optimization (TSO) algorithm is proposed to optimize the hyperparameters of the LSSVM. The results show that the proposed algorithm performs well in RUL prediction of the injection lance, with RMSE=1.2274 (day), MAE=0.6623 (day) and R$^2$=0.9308. Therefore, the proposed algorithm can provide effective RUL prediction for the injection lance, reduce its operational risks, and improve the stability and reliability of the copper top-blown smelting system.