Lyapunov functions underpin the stability analysis and controller synthesis for aerospace vehicles, from spacecraft attitude control to autonomous flight systems and UAV swarm coordination. This review discusses the development of computational Lyapunov function synthesis methods. Within this scope, it traces the evolution from traditional Sum-of-Squares (SOS) programming to artificial intelligence symbolic discovery technologies, prompted by the increasing complexity of nonlinear systems. Specifically, the following stages can be identified: computational relaxation methods constrained by polynomial templates; data-driven paradigms leveraging neural network-based empirical approximations; a... More >

Aiming at the interrupt storm performance bottleneck existing in high-speed gyro attitude signal acquisition of aerospace vehicles, an embedded heterogeneous multi-core data acquisition system based on Xilinx ZYNQ-7000 SoC is proposed for inertial measurement unit (IMU) gyro signal sampling. On the hardware layer, a sensor data interface IP conforming to AXI4-Stream protocol is designed in programmable logic (PL) to collect high-frequency gyro original signals, and an AXI DMA controller builds a wideband direct peripheral-DDR transmission channel to realize zero-copy data writing, greatly reducing processor resource occupation under continuous inertial attitude sampling. On the software laye... More >

As orbital swarms grow toward populations of tens to hundreds, long-term maintenance changes character. We argue that it is distinct from reconfiguration or trajectory planning, defined by two requirements: it is perpetual, and it must be low-frequency, because every active maneuver arc is stolen from the payload's working time. Against these requirements the three dominant paradigms: centralized planning, distributed reactive control, and configuration design-then-maintain, fail in qualitatively different ways. Two of these failures are structural, properties of how the problem is posed; only the third is fixable. We therefore contend that scalable swarm maintenance will be achieved primari... More >

Flapping-wing flight has long inspired bio-inspired aerial robots because of its extraordinary aerodynamic efficiency and maneuverability. Although substantial progress has been achieved in understanding unsteady aerodynamic mechanisms, most existing frameworks remain centered on idealized periodic wing motions and cycle-averaged propulsion. Recent studies increasingly suggest that transient asymmetries and multi-frequency perturbations, traditionally regarded as disturbances, can actively reorganize vortex dynamics and enhance aerodynamic performance. These findings imply that biological flight may rely not solely on stable periodic propulsion, but on continuous adaptation to evolving flow... More >

This paper investigates prescribed performance control (PPC) using an advanced barrier function (ABF) based adaptive super-twisting control scheme with non-fragility. The specialty of the proposed scheme is that it removes the error transformation process, and therefore simplifies the design of PPC. Furthermore, the ABF is combined with an online adaptation law to solve the problem of fragility inherent to the traditional PPC. The proposed method is robust to actuator faults, unknown initial states, and sudden disturbances. The stability of the system is proved via the Lyapunov framework. Finally, experiments on a two-degree-of-freedom (2-DOF) helicopter platform are conducted to verify the... More >

Remaining useful life (RUL) prediction is a core task in prognostics and health management. While Transformers excel at modeling long-range temporal dependencies, their performance is highly sensitive to hyperparameters, and improper splitting of sliding-window samples can introduce data leakage. We propose a Sparrow Search Algorithm (SSA)-optimized Transformer for CMAPSS RUL prediction, adopting an engine-wise split for leakage-aware model selection and using validation RMSE as the fitness function to guide SSA-based hyperparameter optimization. On the FD001 test set, the model achieves RMSE $13.79$, MAE $10.00$, $R^2=0.88$, and a NASA score of $356.26$. The prediction curves and residual d... More >

The operational reliability of aeroengines is vital to civil aviation safety; however, bearings and other key components are prone to failure under harsh operating conditions. In real-world monitoring data, severe class imbalance often leads conventional fault diagnosis methods to be biased toward majority classes, limiting their ability to identify critical faults. To address this issue, this paper proposes a robust anomaly detection framework that integrates the Synthetic Minority Oversampling Technique (SMOTE) with a Broad Learning System (BLS). SMOTE is first applied to generate synthetic fault samples in the feature space, thereby balancing the data distribution and reducing bias. The b... More >

This paper develops a general and implementation friendly stability framework for self-learning control (SLC) laws of the form $u(t)=k_1(t)u(t-\tau)+k_2 v(t)$. Uniform ultimate boundedness is established for a class of general linear plants under two engineering actuator assumptions: (i) smoothness and (ii) saturation with maximum value. These assumptions are reasonable for practical systems and yield an explicit bound, which converts the delay learning mechanism into a nominal (delay-free) controller plus a bounded perturbation injection. The most notable feature of SLC is its simplicity of structure coupled with excellent performance. It is compatible with traditional algorithms and can e... More >