In this paper, an adaptive hierarchical control method based on a regulation-triggered batch identifier is proposed. This approach addresses the challenges in stability analysis and control design of fixed-wing UAVs, which are complicated by the coupling of underactuated characteristics and parametric uncertainty. In order to deal with the underactuated problem, the original fixed-wing UAV model is transformed into a virtual integrator and linear parameterized model based on the unicycle model. Backstepping is then combined with fixed-time control methods to create an equivalent flight tracking controller. By analyzing the mechanism of parameter uncertainty on the motion state of the UAV, a linear regression equation is constructed by spectral decomposition technology to quantitatively collect excitation information, which ensures the richness of the collected excitation information and the boundedness of the linear regression equation. A regulation-triggered batch identifier is created to update the parameter estimation using the gathered excitation data when the trigger function satisfies specific requirements in order to remove the impact of parametric uncertainty. Compared with the existing hierarchical control strategies for fixed-wing UAVs, the proposed framework achieves the unity of error convergence, excitement richness and parameter identification accuracy.
Filter-press electrochemical reactors have become the dominant reactor configuration for flow batteries owing to their simple structure, high modularity, and low cell voltage. They also show broad application potential in energy storage batteries, electrochemical water treatment, and electrosynthetic chemical processes. Among various flow battery systems, the all-vanadium redox flow battery represents the most commercially advanced and extensively studied technology. However, inadequate mass transfer continues to limit further performance development since it increases concentration polarization, reduces voltage efficiency, speeds up long-term performance degradation, and eventually raises system costs. This review focuses primarily on all-vanadium redox flow batteries and discusses mass-transfer enhancement from the perspectives of both key reactor components and system operation. Recent advances in the mass-transfer optimization of electrodes, flow fields, membranes, and other core components are systematically summarized. The effects of operating parameters, including flow rate, temperature, current density, electrolyte concentration, and tank-to-stack ratio, on mass-transfer behavior are analyzed. Based on this, coordinated optimization solutions including reactor structure and system operation are suggested, and the coupling relationship between component-level mass-transfer characteristics and total battery performance is elucidated. From an engineering perspective, mass-transfer optimization in all-vanadium redox flow batteries should not be limited to component modification alone. Instead, it should also account for stack scale-up effects, tank-stack matching, operating-parameter regulation, and the balance between electrochemical performance and system energy consumption. This review aims to provide theoretical guidance and technical references for the engineering design and performance improvement of filter-press flow battery reactors.
High-fidelity simulation of an aircraftu2019s dynamic reaction characteristics under challenging flight conditions is made possible by numerical virtual flight (NVF) technology, which is essential for designing control laws and assessing flight quality. This paper systematically reviews the NVF technology coupled with aerodynamics, motion, and control, with an emphasis on the application of this coupling method in complex flight problems. This paper outlines the basic framework and technical workflow of NVF, elaborating on the evolution of dynamic mesh technology from single deformation to dynamic hybrid mesh systems, as well as its critical role in ensuring relative motion of components and computational stability, while also pointing out the enabling potential of machine learning in mesh quality control. Regarding unsteady flow field solutions, this paper discusses the coupling modes of aerodynamics, motion, and control, introduces the technical evolution from loose coupling to tightly coupled closed-loop solutions, and highlights that the tightly coupled method can significantly improve the simulation accuracy for high-dynamic flight states such as high angle of attack and post-stall. This paper elaborates on the application practices of this coupling method in three major scenarios: hypersonic multi-field coupling, morphing flight, and flexible bionics. NVF will advance toward effective, unified, and full-system multi-field coupling in the future thanks to artificial intelligence technologies, hastening the shift in aircraft development paradigms toward simulation-driven, digital-intelligent testing.
Both monopropellant and bipropellant rocket engines frequently employ nitrous oxide (N2O) as a non-toxic, stable, and secure self-pressurizing propellant. It holds significant research value and application potential, especially in the field of attitude and orbit control propulsion. In addition to outlining research priorities and development recommendations for N2O propulsion technology in the field of attitude and orbit control, this paper reviews the development history and current research status of several types of nitrous oxide-based propulsion systems, summarizes their features and advantages, and identifies the key technologies facilitating their engineering application. The aim is to provide valuable references for the innovation and engineering practice of aerospace attitude and orbit control propulsion systems.
To achieve an objective assessment of driving comfort, this study developed and systematically compared multiple machine learning models based on multimodal physiological signals. 64 individuals had their electrocardiograms (ECGs), surface electromyograms (sEMGs), muscle oxygen saturation (SmO2), and arterial blood flow signals recorded while they drove. Using comfort scores as labels, six types of models, including support vector machine (SVM), random forest (RF), and adaptive Boosting (AdaBoost), were constructed and optimized with principal component analysis, synthetic minority oversampling technique (SMOTE), and grid search. Results showed that multimodal signal fusion significantly improved classification performance. The SVM model achieved optimal performance with the feature set containing arterial blood flow parameters, reaching an average area under the curve (AUC) of 0.97 and an accuracy of 0.90. RF models consistently exceeded 0.95 AUC with fused signals. Practical signal combinations for portable monitoring were identified: "sEMG + ECG" dual-signal combination (average AUC is 0.90) and "SmO2 + ECG + sEMG" triple-signal combination (average AUC is 0.92). This study offers empirical support for model selection and signal setup in objective driving comfort evaluation, providing insightful information for the development of in-car health monitoring systems.
