Aiming at the zero dynamic instability caused by nonminimum phase property of hypersonic vehicles, a model transformation method based on the B-I (Byrnes-Isidori) standard form was proposed to achieve decoupling of internal and external dynamics of the system. A dynamic integral sliding mode stabilization control method was proposed, an augmented closed-loop system with internal dynamics, external dynamics and dynamic parameters was formed. A sliding mode parameter tuning method was proposed to make the augmented system remain dynamic stable under different operating conditions and perturbation conditions, and the trimmed point of external output was always zero. The proposed method could accurately track the output trajectory command with zero dynamic stability, and realize the longitudinal trajectory stability tracking control of nonminimum phase hypersonic vehicle. Lyapunov stability analysis was used to prove the stability of the proposed control method, and constant dynamic pressure trajectory tracking and Monte Carlo simulations were carried out. Simulation results show that the dynamic integral sliding mode control method maintains good tracking accuracy and robustness under perturbation conditions, and stabilizes the zero dynamics of the system effectively.

Accurate prediction of remaining useful life (RUL) is critical to the stability, reliability, and safety of aircraft engines. A new deep learning model called BSVAR is suggested for RUL prediction in order to address the issue that current RUL prediction techniques are unable to properly utilize the deterioration information of sensor data. The deep degradation information of sensor data is extracted using a bidirectional long-short-term memory (Bi-LSTM) networks and self-attention based variational autoencoder (SVAE). With the utilization of variational inference, the sensor data is clustered according to the implied degradation information, meanwhile, the latent space can be generated. The combination of the Bi-LSTM, the SVAE, and the regressor is used to establish a RUL prediction model to sufficiently extract the degradation features of sensor data to improve the prediction accuracy. Results from experimental validation on the aero-engine C-MAPSS dataset demonstrate that the suggested approach outperforms the current RUL prediction approaches in terms of prediction performance and can identify the engines’ degree of degradation in the latent space.