In long-range sequential orbital pursuit-evasion game scenarios, traditional Nash equilibrium algorithms rely on symmetric simultaneous decision-making assumptions. Such algorithms fail to fully exploit the sequential advantages of the leader-follower paradigm in practical space missions, and often yield conservative strategies with excessive fuel consumption. This paper proposes a Stackelberg equilibrium-based control algorithm for sequential pursuit-evasion games involving satellites with impulsive orbital maneuvers. A multi-stage impulsive maneuver game model incorporating orbit determination delays is established, and a bi-level nested optimization architecture is designed. The outer layer employs the Pattern Search algorithm to derive the optimal maneuver strategy of the pursuer, while the inner layer uses the Sequential Quadratic Programming (SQP) algorithm to obtain the optimal response strategy of the evader. Simulation results demonstrate that, compared with the traditional Nash-equilibrium Action-Reaction Search (ARS) algorithm and the greedy algorithm neglecting evader maneuvers, the proposed algorithm achieves a shorter terminal relative distance with lower fuel consumption.
This paper presents a G-Agent, a large language model (LLM)-driven multi-agent framework that automates the optimization workflow for an axisymmetric cavity-based scramjet combustor. The framework integrates a supersonic compressible reacting flow solver with the Qwen3-VL-2B-Instruct model. It consists of four specialized agents: Interactor, Runner, Corrector, and Optimizer. The Interactor parses user requirements and generates simulation input files. The Runner manages mesh generation, solver configuration, and simulation execution. The Corrector diagnoses failed cases by analyzing fault logs and applies corrective actions. The Optimizer coordinates the optimization process using a trust-region response surface method to maximize thrust. The framework employs Latin hypercube sampling (LHS) to explore the design space defined by three key geometric parameters: isolator length, cavity depth, and fuel injector position. Without manual intervention beyond initial specifications, the G-Agent successfully conducted multiple optimization iterations. Starting from a baseline thrust of 489.4 N, the optimized configuration achieved 523.5 N after three iterations, reaching a relative improvement of 6.97%. Our proposed agent achieves closed-loop control of the entire optimization process, significantly lowering the technical barrier and human effort in scramjet combustor design.
This review surveys control methods for stabilizing the coupled translational–rotational relative motion of a chaser spacecraft during rendezvous and proximity operations. Rendezvous and proximity operations campaigns comprise multiple phases, including launch, phasing, far-range rendezvous, close-range rendezvous, and terminal approach. This paper focuses on the terminal proximity phases, where precise relative position and attitude regulation of the chaser with respect to a target is essential for safety and mission success. The surveyed literature is organized into two controller-strategy families: concurrent attitude and position control (CAPC), in which translational and attitude subsystems operate simultaneously but remain structurally separated, and integrated attitude and position control (IAPC), in which translational–rotational coupling is modeled and exploited within a unified formulation. Using this taxonomy, we (i) synthesize representative flight missions to clarify operational concepts and recurring guidance, navigation, and control building blocks, and (ii) identify recurring research gaps and open problems across mission complexity, convergence guarantees, constraint handling, actuator configurations, and robustness to disturbances and target motion. The resulting synthesis provides a structured reference for researchers and supports the development of more reliable and flight-relevant terminal-phase rendezvous and proximity operations controllers.
Prediction of turboshaft engine performance parameters is essential for engine health management, however, traditional physics-based models and shallow neural networks struggle to effectively model the complex nonlinear characteristics of time-series data. To overcome these limitations, a hybrid model integrating a Temporal Convolutional Network (TCN), an attention mechanism, and a Bidirectional Long Short-Term Memory (BiLSTM) network is proposed for predicting turboshaft engine performance parameters. Temporal convolution is employed to extract local temporal features, while the BiLSTM network is used to capture long-term bidirectional dependencies. An attention mechanism is further incorporated to assign greater weights to critical time steps. Simulation results show that the proposed model achieves higher prediction accuracy compared to standalone LSTM and TCN models. When applied to the performance prediction of turboshaft engines under different inlet air temperature conditions, the proposed framework consistently maintained the root mean square error values for training and testing between 0.05 and 0.09 across three key performance parameters, while effectively suppressing measurement noise. These results demonstrate that the model possesses excellent generalization performance and holds significant potential for practical engineering applications.
Deboarding sits on the aircraft turnaround critical path, yet it is usually treated as boarding run in reverse, with schedules validated for boarding assumed to keep their merit when the flow direction changes. This study compares five conventional schedules in both directions under one cabin geometry, in a three-dimensional Airbus A320 economy-cabin simulation at three passenger loads with ten replications per scenario. The ordering is reversed, as the mirror intuition suggests: Back-to-Front is fastest to board at full load and slowest to deboard. Two results are less expected. First, schedule choice loses most of its leverage when the process reverses: the full-load spread is 26 s against 121 s on the boarding side, and a decisive boarding advantage collapses into a statistical tie in the deboarding direction. Second, what survives is not duration but its distribution. At full load, mean seat-to-door time is 40% longer under Back-to-Front than under Front-to-Back, and the last-served passenger waits 11.9 min against 9.0, differences that persist in proportion when compliance is only partial. Deboarding schedule choice is therefore a service-quality instrument rather than a turnaround-duration one, and schedules must be evaluated in the direction the cabin process actually runs.
Vision-based unmanned aerial vehicle (UAV) detection has become increasingly important since it is a key technology in aerial collision avoidance systems. However, the detection of small UAVs is still unsatisfactory in practical applications. To address this problem, this paper proposes EWH-YOLO, a novel deep convolutional neural network-based method for small UAV detection. First, an efficient feature extraction network is designed to exact the multi-level features of small UAVs while reducing the network parameters and computational complexity. Second, a weighted bidirectional feature fusion network is proposed to enhance the low-level and high-level features in the output feature maps. Third, a hybrid bounding box regression loss is introduced to evaluate the difference between the predicted bounding box and the ground-truth bounding box during training and improve the detection accuracy. Finally, a new dataset is created on the basis of considering small UAVs to verify the detection performance. Compared with the state-of-the-art methods, the proposed method achieves higher detection accuracy with lower model complexity. The experimental results demonstrate that the proposed detector significantly improves the detection performance of small UAVs.
Conceptual design is a critical phase in the development of propulsion plants. Its aim is to explore multidimensional design spaces to provide guidelines for the detailed design phase. This exploration is necessarily broad in scope and shallow in fidelity. Lately, developments in computing hardware have allowed the use of simplified computational fluid dynamics (CFD) models for conceptual design purposes, thereby increasing significantly the amount of data to be processed and generalised. In this context, the present work benchmarks two specific surrogate-model implementations for the conceptual design of a rocket-type pulse detonation engine: a tensor-based method based on high-order singular value decomposition (HOSVD) and a fully connected feed-forward neural network (NN). Two complementary comparisons were considered: HOSVD versus NN, using the same factorial databases to assess the effect of the surrogate method; and factorial versus low-discrepancy sampling, using the same NN architecture to assess the effect of the database distribution. The benchmark, which involved five input architecture parameters and five output operation parameters, was performed for both the direct analysis problem (outputs obtained from inputs) and the inverse design problem (inputs obtained from outputs). Three different situations were considered in the comparison—dimensionally balanced, overdetermined, and underdetermined—to simulate conditions that typically arise in these design phases. Three databases of different sizes were generated for the comparison. The results provide a case-specific assessment of the performance of the two implementations under the conditions considered and may provide useful guidance on the application of these data-analysis approaches in conceptual design.
This study presents a computational comparison of 24 lattice topologies over their geometrically feasible relative density ranges. Conjugate heat transfer simulations were performed in ANSYS Fluent 2024 R2 using 10 mm unit cells, inlet air at 300 K and 0.05 m/s, a constant base temperature of 312 K, and gravity acting in the negative z direction. The inlet Reynolds number was approximately 32.5. The prescribed temperature difference of 12 K gives a Grashof number of 1.68 × 103 and a Richardson number of 1.59, indicating that buoyancy and the imposed flow are both relevant. The operating condition was therefore classified as low-speed mixed convection with perpendicular forced flow and buoyancy directions. The hydrodynamic model was benchmarked against published pressure gradient data for a body-centered cubic lattice. Thermal performance was compared using interfacial area, the magnitude of the ANSYS Fluent surface heat transfer coefficient, interfacial heat transfer rate, and interfacial thermal resistance. At 10% relative density, Auxetic gave the lowest resistance, 112.34 K/W, compared with 327.51 K/W for Cube. At 70%, FBCC reached 97.56 K/W, whereas Cube reached 1028.12 K/W. Increasing relative density improved or degraded thermal performance depending on topology. The database provides comparative guidance for lattice selection and subsequent multiscale design optimization of lightweight aerospace and electronic heatsinks.
Tailless underactuated hypersonic glide vehicles rely on elevons alone to control both roll and yaw. The dual-loop cascade architecture, in which differential elevon deflection regulates sideslip and the resulting sideslip response drives roll, exploits the high roll-to-yaw ratio to address lateral–directional underactuation. However, because the inner loop relies on sideslip angle feedback, its performance is sensitive to composite measurement bias. This study first analyzes lateral–directional open-loop divergence, the high roll-to-yaw ratio, and adverse yaw induced by differential elevons using a six-degree-of-freedom model and linearizations at multiple trim points, and then develops a baseline cascade controller employing stability-axis yaw-rate feedback. A first-order Gauss–Markov process with a constant offset is subsequently used to represent sideslip angle measurement bias. A linear extended state observer is introduced only in the velocity–bank angle outer loop to jointly estimate and compensate for the equivalent effect propagated by the bias, aerodynamic perturbations, center-of-mass offsets, and residual inner-loop dynamics. Observer parameters are selected through input direction identification, hierarchical two-dimensional parameter sweeps, and local grid verification. Local stability and boundedness near the design operating point are analyzed under bounded-input assumptions at the levels of the observer error, velocity–bank angle tracking error, and complete attitude control closed loop. Strictly paired Monte Carlo simulations and ablation experiments show that the proposed method effectively attenuates the propagation of sideslip angle measurement bias into the roll channel, improves velocity–bank angle tracking accuracy and response consistency under random uncertainties, and does not appreciably increase the required elevon position command envelope.
Directional asymmetry and Dutch-roll are critical phenomena observed during powered parafoil vehicle (PPV) flight tests, complicating stable flight and leading to obvious lateral–directional biases in autopilot tracking. However, existing PPV models typically neglect propeller counter-torque (PCT), resulting in limited research on directional asymmetry. Moreover, conventional Newton–Euler formulations require explicit treatment of internal constraint forces, complicating analytical linearization and local stability analysis. To address these issues, first, this paper develops a nonlinear 9-degree-of-freedom (9-DOF) PPV model using Kane’s equations with quasi-velocities. This multibody dynamic model provides a compact and structurally consistent basis for linearization and stability analysis. Subsequently, the mechanism analysis shows that PCT shifts the coupled roll–yaw equilibrium and is the primary physical source of the observed directional asymmetry. In addition, the Dutch-roll mode is identified as the dominant oscillatory mode governing the lateral–directional stability of the PPV. The modal analysis further indicates that increasing thrust and directional control inputs reduce the Dutch-roll damping ratio. On this basis, a damping-ratio-based flight envelope is constructed. Furthermore, numerical simulations and flight-test comparisons demonstrate that the proposed model captures the principal PPV dynamic responses. The simulation results also support the mechanism analysis of directional asymmetry and the Dutch-roll.
Data association for space-based radar multi-target tracking is a significant challenge in dense clutter and complex scenarios, often leading to tracking errors and target loss. This paper proposes a classification-aided message-passing algorithm for space-based radars (CA-MP-SBRs), which employs a hybrid mean-field and belief-propagation (MF-BP) framework enhanced with target classification information. We leverage high-resolution range profile (HRRP) data, which are processed by a convolutional neural network (CNN) to classify targets. The classification output is then seamlessly integrated into the MF-BP framework to jointly infer target kinematic states, visibility states, data association, and class index. Simulation results demonstrate that incorporating HRRP-based classification improves data-association reliability and tracking accuracy. Specifically, compared with Classification-aided Gaussian mixture probability hypothesis density (CA-GM-PHD), the proposed CA-MP-SBRs reduces the average root mean square error (RMSE), optimal subpattern assignment (OSPA), and generalized optimal subpattern assignment (GOSPA) by 33.4%, 31.7%, and 32.1%, respectively, while the corresponding reductions relative to classification-aided labeled multi-Bernoulli tracking for space-based radars (CA-LMB-SBRs) are 23.2%, 25.6%, and 30.3%. These results confirm the effectiveness of CA-MP-SBRs for tracking diverse target types in dense-target scenarios.
Flow pattern recognition and anomaly detection in flight trajectory data constitute fundamental tasks in air traffic management. The former provides a foundation for understanding flight trajectories, while the latter seeks to pinpoint trajectories that deviate from normal patterns. Despite their interconnected nature and potential for synergy, these tasks are often addressed separately. Therefore, this paper proposes an integrated unsupervised learning framework, termed Integrated Unsupervised Pattern Identification and Anomaly Detection (IU-PIAD). The proposed framework, consisting of an encoder, a generator, and a discriminator, enables simultaneous flow patterns recognition and anomaly detection. The encoder maps trajectories to latent space representations, which are reconstructed by the generator. The discriminator’s output is integrated to compute the anomaly score of the trajectories for anomaly detection. Additionally, clustering assignment reinforcement is introduced in the latent space to strengthen cluster stability, thereby supporting flow pattern identification. Experimental analysis using real flight trajectory data demonstrates that the proposed framework achieves favorable performance in both anomaly detection and flow pattern recognition tasks.
The strong coupling among guidance laws, control loops, aerodynamics, and mission constraints poses growing challenges to the design of modern tactical missile guidance systems for autonomous flight. Conventional manual tuning and simulation-based trial-and-error result in long iteration cycles, limited reuse of design knowledge, and poor adaptability to changing scenarios. To address these limitations, we propose GSMultiAgent, a multi-agent collaborative cascade framework built atop Hermes Agent, which transforms natural-language mission requirements into optimized guidance system models through structured agent cooperation with feedback-driven iterative refinement. Three innovations are introduced: (1) a three-layer correction pipeline covering syntactic checking, deterministic mathematical verification, and semantic reasoning; (2) a bimodal experience repository supporting similarity-guided retrieval with access-count decay and best-quality retrieval for PPO warm-start initialization; and (3) a self-adjudicating optimizer that autonomously decides between PPO-based systematic parameter search and heuristic LLM-tuning guided by a reflection agent. Across four engagement scenarios, GSMultiAgent consistently attains high feasibility at a small fraction of the simulation budget required by conventional optimizers and single-agent baselines, and its design paths escalate autonomously from parameter tuning to structural law modification as task difficulty increases. Ablation studies confirm that the reflection agent, the optimization agent, and structured memory each contribute essential and complementary gains. These results establish multi-agent coordination with structured memory and self-adjudicating optimization as an effective paradigm for intelligent, reusable guidance system design.
In multi-hop UAV ad hoc networks employing the Statistical Priority-based Multiple Access (SPMA) protocol, the HELLO broadcast interval cannot be arbitrarily shortened due to the inherent upper bound on the per-slot transmission probability of each node in saturated networks, which fundamentally limits the estimation accuracy of Channel Occupancy Statistics (COS). To address this problem, this paper proposes a spatial correlation-aided multi-source asynchronous Kalman filtering method, abbreviated as SMA-KF. On the basis of conventional COS broadcasting, SMA-KF introduces two complementary observation sources: COS measurements piggybacked on data packets, and spatially correlated observations from common neighbors compensated by historical biases. These three types of observations are integrated into a unified Kalman filtering framework, and a state-space model suitable for asynchronous intermittent observations is constructed. Theoretical analysis verifies the convergence of the algorithm. Simulation results demonstrate that the proposed algorithm significantly outperforms the EWMA and TW algorithms across all test scenarios, and achieves overall lower error than BiLSTM. Under the extremely sparse observation condition with a HELLO broadcast interval of 600 slots, the Normalized Root Mean Square Error (NRMSE) of SMA-KF is 28.28%, which is 32.8% and 26.2% lower than those of EWMA (42.11%) and TW (38.33%), respectively. In the heavy-traffic scenario with an average data packet arrival interval of 20 slots, the NRMSE of SMA-KF is as low as 4.80%, whereas those of EWMA and TW are 17.49% and 15.40%, respectively, corresponding to reductions of 72.6% and 68.8%. In comparison with BiLSTM, SMA-KF achieves lower NRMSE in five out of seven traffic configurations, while BiLSTM exhibits only marginal and statistically insignificant advantages in the remaining two configurations. Link interruption experiments show that SMA-KF maintains NRMSE between 5.68% and 10.40% across the entire meaningful interruption coverage range of 0% to 53%, consistently outperforming all benchmark algorithms. Moreover, SMA-KF consistently achieves the lowest estimation error under varying node mobility speeds. Parameter sensitivity analysis confirms that SMA-KF maintains stable performance across a wide range of parameter values. These results validate the effectiveness of multi-source observation fusion and spatial cooperative estimation in improving both the accuracy and robustness of COS estimation.
Conventional validation methods for multi-output models generally assume probabilistic descriptions of all input variables. In engineering applications, however, sparse data and limited knowledge may permit some inputs to be specified only by intervals, which limits the applicability of existing methods under random and interval mixed uncertainty. This challenge is prevalent in numerical simulation of various aerospace structural systems. A reliability-metric-based validation method for multi-output models under random and interval mixed uncertainty is proposed in this paper. The Mahalanobis distance (MD) is used to account for correlations among multiple responses, and interval analysis is introduced to construct an interval-valued MD. A conservative validation metric is then defined as the probability that the upper bound of the model–experiment MD is smaller than the lower bound of the MD corresponding to the engineering-tolerance vector. The metric therefore quantifies, from a reliability perspective, the probability that model predictions satisfy prescribed engineering tolerances. Because the coupling between random and interval uncertainties prevents a straightforward analytical solution, a baseline numerical procedure based on Monte Carlo simulation (MCS) is developed. Two numerical examples and two engineering examples demonstrate the applicability of the method. The results indicate that the proposed metric accommodates random and interval mixed uncertainty, accounts for correlations among multiple outputs, and provides a probabilistic measure of agreement between model predictions and experimental measurements. The method offers an interpretable basis for assessing the credibility of complex engineering simulation models.
Reusable launch vehicles (RLVs) subject avionics electronics to repeated vibration, shock, and thermal cycling, yet no standardized method exists to evaluate reusability at the device level. We integrate physics-of-failure models—Steinberg vibration fatigue, Engelmaier/Coffin–Manson solder thermal fatigue, and Miner cumulative damage—with AHP-entropy multi-criteria decision making (MCDM). A flight-heritage 6-layer FR4 printed circuit board (PCB) from a rocket data-acquisition unit (108.25 × 108.25 × 1.62 mm, 109 components) is analyzed under Falcon 9 vibration and a DLR re-entry thermal profile (f1 = 310.2 Hz), with 11 core devices extracted from ODB++. Thermal fatigue dominates vibration damage by seven orders of magnitude. The board-level average of 62.9 flights is misleading: the ceramic PGA device D10 limits the unit to 12.3 flights (a preliminary model-based estimate, pending ALT calibration), a factor-of-five discrepancy, with three ceramic families (D10 PGA, D9 LCC, D6–D8 FIFO) forming the bottleneck. The combined weighting assigns 79.5% of the decision to the damage-derived criterion; Comprehensive Reusability Index (CRI) thresholds map flights 0–3 to direct reuse, 4–7 to refurbishment, and 8+ to retirement. The results distill into a weakest-link reuse principle, a damage-threshold service model, and an inverse-square thermal-fatigue relation.
The detection of orbital anomalies is of critical importance to the safe operation of on-orbit spacecraft. Existing methods often rely on manually engineered features and are constrained by the limited computational resources of satellite equipment. This paper proposes a lightweight unsupervised anomaly detection method based on the Peak Adversarial Autoencoder (Peak-AAE). First, the Peak-AAE model is employed to reconstruct historical semi-major axis data, and the squared differences between the reconstructed and original data are taken as the residual sequence. The Automatic Multi-Scale Peak Detection (AMPD) algorithm is then applied to the residual sequence to identify anomalous points. In comparison with traditional approaches, this method eliminates the need for manual feature design. Moreover, the improved Peak-AAE model effectively reduces the false positive rate associated with the original AAE model, thereby enhancing detection accuracy. Finally, the compressed Peak-AAE model is deployed on edge devices for ground simulation verification. Experimental validation on the BEIDOU-3 G2 and IRNSS-1A satellites demonstrates that the Peak-AAE model achieves a minimum precision of 90.00% and a minimum recall of 84.74%.
Tip leakage degrades ducted fan performance and contributes to unsteady loading noise. This study investigates a wall-penetrating blade ring (WPBR) ducted fan for unmanned electric vertical takeoff and landing (eVTOL), replacing radial clearance with axial end face gaps. Six configurations of a 381 mm four-bladed rotor were evaluated at 5000 r/min under quasi-hover conditions: a conventional ducted fan, an internal blade ring rotor, and four WPBR cases with single-sided clearances of 0.8–2.0 mm. Sliding-mesh unsteady Reynolds-averaged Navier–Stokes simulations using the shear stress transport k-ω model were coupled with the Ffowcs Williams–Hawkings formulation. The WPBR formed a U-shaped cavity recirculation and redistributed the concentrated tip-region vortical structures. The 1.2 mm case retained 28.72 N of thrust, 2.1% above baseline, while reducing torque by 6.1% relative to the 0.8 mm case; its figure of merit remained lower. Its simulations predicted a reduction of 16.4 dB in the first blade-passing frequency level in the rotor plane and a predicted reduction of up to 15 dB in overall sound pressure level at 1 m. Thus, it represents a compromise among thrust, torque, and predicted acoustic performance rather than an aerodynamic optimum. A magnetically supported prototype operated up to 2000 r/min, demonstrating low-speed operability of the architecture for unmanned eVTOL propulsion.
Aiming at the problems of strong model uncertainty, unmodeled perturbation disturbances and unknown maneuvering of space non-cooperative targets in orbital rendezvous missions, an orbit prediction and control method combining active disturbance rejection and model predictive control (ADRC-MPC) is proposed in this paper. Firstly, the relative motion dynamic equations of chaser–target are established under a geocentric inertial coordinate system, and the lumped disturbance including space perturbation and unknown evasive maneuvers of the target is expanded into extended state variable. Secondly, an extended state observer (ESO) is designed to achieve real-time accurate estimation and feedforward compensation of time-varying lumped disturbances, which mitigates adverse influences induced by J2 zonal harmonics, atmospheric drag, solar radiation pressure, and target evasive maneuvers. Thirdly, a constrained MPC strategy incorporating thrust saturation, approach corridor, and collision safety zone constraints is developed. Sufficient conditions for closed-loop input-to-state stability (ISS) are theoretically derived via the small-gain theorem under the given assumptions, which ensures the uniform ultimate boundedness (UUB) of tracking errors. Finally, numerical simulations, comparative benchmarks against conventional MPC and proportional-derivative (PD) control, and Monte Carlo robustness tests are performed. Simulation results demonstrate that the proposed ADRC-MPC framework delivers superior rendezvous precision, stronger disturbance rejection, reduced propellant consumption, and improved robustness against initial state offsets and measurement noises, laying solid theoretical and technical foundations for autonomous orbital rendezvous with space non-cooperative targets.
Surface icing poses a significant risk to unmanned aerial vehicles (UAVs) and compact aerospace platforms, where limited onboard power and space require efficient anti-/de-icing surfaces. In this study, micro/nanostructures were fabricated on TC4 titanium alloy (Ti–6Al–4V) surfaces by femtosecond laser processing at different scanning speeds. The effects of scanning speed on surface morphology, wettability, static freezing, dynamic droplet behavior, and electrothermal de-icing performance were systematically investigated. Increasing the scanning speed induced nonlinear changes in microstructure height and surface roughness, while variations in ablation intensity caused nonuniform material redistribution. The surface processed at 250 mm/s showed the best anti-icing performance, with a water contact angle of 157.5 ± 0.5° and a maximum freezing delay 21.5 times longer than untreated TC4. During electrothermal de-icing, melting initiated at discrete ice–substrate contact points, forming coalesced meltwater films, while interfacial stress concentration promoted crack propagation and rapid ice detachment. Compared with untreated surfaces, ice detachment time (250 mm/s) achieved complete ice detachment at approximately 152 s, whereas ice on the untreated surface remained adhered after 270 s of continuous heating, representing a de-icing time reduction of at least 44%. These results demonstrate that combining laser-fabricated microstructures with electrothermal heating effectively reduces real ice–substrate contact, providing an enhanced anti-/de-icing strategy for lightweight, long-endurance UAV applications under identical electrical input.
