Jiangtao Xu

To address the uncertainty and the stringent requirement for trajectory planning in rocket-powered landing, this paper proposes a six-degree-of-freedom (6-DoF) quantile-constrained sequential convex programming (QC-SCP) algorithm. Considering the uncertainties in engine thrust and gimbal actuation, an empirical quantile estimation method based on random parameter samples is employed to reformulate the chance constraints as equivalent quantile constraints, enabling deterministic treatment of uncertain constraints. Subsequently, a QC-SCP solution framework is developed, in which the original nonconvex trajectory optimization problem is transformed into a sequence of convex subproblems, and the quantile constraints are directly embedded into the sequential convex optimization process to achieve efficient trajectory planning under uncertainty. To mitigate the potential ”artificial infeasibility” under extreme operating conditions, a reference trajectory generation strategy with attitude constraints is designed to improve the robustness and convergence of the algorithm. Numerical simulation results demonstrate the effectiveness and convergence of the proposed QC-SCP algorithm. Comparative results with other chance-constrained methods further verify its advantages in terms of reduced conservatism and improved computational efficiency. Finally, Monte Carlo simulation tests validate the robustness of the proposed approach.

In this paper, a high-order multiplication perturbation-based transition matrix method (TM-HOMP) is proposed to address the strongly terminal-constrained optimal control problem (OCP) in aerospace engineering. Theoretical consistency between model predictive static programming (MPSP) and the transition matrix (TM) method is established, revealing that discrete MPSP constitutes a special case of traditional linear quadratic optimal control theory. To enhance the computational accuracy of the transition matrix—and consequently improve iterative processes—the High-Order Multiplication Perturbation (HOMP) method is integrated into the optimal control framework. HOMP overcomes limitations in existing approaches by extending its applicability from linear autonomous system to general linear time-varying (LTV) systems via the State Augmentation method. A recursive symbolic computation method is proposed to address the challenge of computing high-order matrix derivatives required by HOMP, utilizing lower-order derivatives coupled with an implementation approach based on the CasADi toolbox or Maple software. Finally, three strongly terminal-constrained OCP cases—including an undamped spring problem and two aerospace guidance scenarios—are presented to validate TM-HOMP. Results confirm its efficacy in relaxing discretization requirements and reducing iteration counts, which in turn boosts convergence speed by up to 67% and improves solving efficiency by up to 81%.

This study addresses the control challenge of ventilated supercavitating vehicles during depth-change maneuvers, where variations in speed and depth induce unsteady cavity evolution and nonlinear planing forces. An unsteady cavity evolution model based on the independent cross-sectional expansion principle was developed and integrated with vehicle dynamics to form a heterogeneous coupled motion framework. A DQN-based controller was designed to maintain cavity length under unsteady conditions, while an ADRC-based pitch controller achieved decoupled attitude control, with depth tracking realized through cascaded outer-loop feedback. Numerical simulations were performed on the established heterogeneous coupled motion model under depth-change maneuvers. The results show that the proposed approach maintains the cavity length within ±10% of the commanded value and achieves rapid and stable depth tracking. The proposed modeling and control framework offers an effective approach to enhance the maneuverability and robustness of ventilated supercavitating vehicles in complex hydrodynamic environments.

To address the challenges of low diagnostic accuracy and poor interpretability in fault diagnosis under variable conditions with small samples from multiple sensors, this paper proposes a novel multi-sensor fusion attention residual network. First, multi-directional sensor signals are fused using principal component analysis, and 28 time-frequency modal features are innovatively extracted to capture subtle fault signatures across diverse operating conditions. The 1D convolutional neural network is enhanced with residual blocks and a multi-channel attention mechanism, effectively mitigating gradient vanishing and overfitting while emphasizing discriminative features. To overcome the ‘black-box’ nature of deep learning models, the SHapley Additive exPlanations method is incorporated to quantify the contribution of each feature, enabling visualization of the model’s decision logic and identification of critical fault indicators. Experimental results demonstrate that the proposed method achieves superior diagnostic accuracy and generalization performance on small-sample, variable-condition datasets, while offering improved interpretability. These advantages make the approach well-suited for practical bearing condition monitoring and maintenance decision-making.

Aiming at the traditional single sensor vibration signal cannot fully express the bearing running state, and in the high noise background, the traditional algorithm is insufficient for fault feature extraction. This paper proposes a fault diagnosis algorithm based on multi-sensor and hybrid multimodal feature fusion to achieve high-precision fault diagnosis by leveraging the operating state information of bearings in a high-noise environment to the fullest extent possible. First, the horizontal and vertical vibration signals from two sensors are fused using principal component analysis, aiming to provide a more comprehensive description of the bearing’s operating condition, followed by data set segmentation. Following fusion, time-frequency feature maps are generated using a continuous wavelet transform for global time-frequency feature extraction. A first diagnostic model is then developed utilizing a residual neural network. Meanwhile, the feature data is normalized, and 28 time-frequency feature indexes are extracted. Subsequently, a second diagnostic model is constructed using a support vector machine. Lastly, the two diagnosis models are integrated to derive the final model through an ensemble learning algorithm fused at the decision level and complemented by a genetic algorithm solution to improve the diagnosis accuracy. Experimental results demonstrate the effectiveness of the proposed algorithm in achieving superior diagnostic performance with a 97.54% accuracy rate.

We present a novel multi-modal adaptive feature extraction algorithm considering both time-domain and frequency-domain modalities (AFETF), coupled with a Bidirectional Long Short-Term Memory (Bi-LSTM) network based on the Grey Wolf Optimizer (GWO) for early-stage weak fault diagnosis in bearings. Singular Value Decomposition (SVD) was employed for noise reduction, while Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) was utilized for signal decomposition, facilitating further signal processing. AFETF algorithm proposed in this paper was employed to extract weak fault features. The adaptive diagnostic process was further enhanced using Bi-LSTM network optimized with GWO, ensuring objectivity in the hyperparameter optimization. The proposed method was validated for datasets containing weak faults with a 0.2 mm crack and strong faults with a 0.4 mm crack, demonstrating its effectiveness in early-stage fault detection.

With the application of advanced composite materials in High-Aspect-Ratio wings (HARW), the randomness of structural parameters, such as elastic modulus and Poisson's ratio, is enhanced. Hence, in order to explore the whole picture of aeroelastic problems, it is of great significance to study the role of random structural parameters in aeroelastic problems. In this paper, the dynamic response of flexible HARW considering random structural parameters is analyzed. An aeroelastic model of a one-dimensional cantilevered Euler–Bernoulli beam considering aerodynamic forces acting on the wing is established based on Hamilton's principle. Adopted the idea of simplifying calculation, the effect of random structural parameters is analyzed. Then, considering the elastic modulus and torsional stiffness as continuously one-dimensional random field functions, and discretized by local method. The first and second order recursive stochastic nonlinear finite element equations of wing are derived by using perturbation method. Based on it, statistical expression of aeroelastic effects of the wing is derived. Monte Carlo method is adopted to verify the effectiveness of the method. Numerical simulations indicate that the method proposed can well mirror the statistical characteristics of aeroelastic response.

Since the missile dynamical system in the pitch plane is nonlinear, uncertain and non-matched, its integrated guidance and control is nontrivial. In order to cope with this problem, an adaptive back-stepping based sliding mode control algorithm is proposed in this paper. Firstly, the sliding surface of the virtual control variable is defined thanks to the strong robustness of sliding mode control. Secondly, it is highlighted that the calculation scales with the number of the virtual variables in back-stepping control. To deal with this problem, an adaptive sliding mode control is used to estimate the upper bound of the uncertainties and to simplify the control system design. The closed-loop stability has been proven based on Lyapunov theory. Finally, the simulation results have been reported to witness the validity of the algorithm, which show that the proposed approach can reject the target uncertainty and have a satisfactory control performance.