Search Results (174)

This paper presents a distributed closed-form control strategy for multi-unmanned aerial vehicle (multi-UAV) formation flight in cluttered environments. We propose a virtual-centroid-based architecture that reduces leader dependency while maintaining precise geometric configuration. A virtual-centroid navigation strategy generates reference trajectories for formation coordination. For collision avoidance, a generalized p-norm distance function accurately assesses collision risks for diverse obstacle geometries. An event-triggered mechanism incorporating velocity-dependent conditions produces amplitude-modulated, continuity-preserving avoidance vectors. A virtual navigation trajectory dynamically fuses avoidance information with reference tracking, enabling seamless coordination of tracking and collision avoidance. Closed-form controllers are derived for three-degree-of-freedom (3-DoF) dynamic formation control. Safety guarantees and asymptotic convergence in the nominal case are established via generalized Lyapunov analysis for non-smooth dynamical systems. Comparative simulations demonstrate that the proposed method achieves trajectory smoothness comparable to artificial potential field (APF). Full article

Antibody glycosylation is a critical quality attribute (CQA) in disease research and quality control of therapeutic antibodies. This study presents a streamlined approach for the direct analysis of N-glycans released from IgG by PNGase F using matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) without any prior purification. By utilizing a 1,5-diaminonaphthalene (DAN)/2,5-dihydroxybenzoic acid (DHB) matrix, multiple glycan-derived peaks were selectively detected directly from the protein-rich digestion mixture, demonstrating the superior glycan selectivity of the DAN/DHB system. Furthermore, systematic dilution of the digest was found to significantly enhance the signal-to-noise ratio, enabling the detection of sialylated glycans while simultaneously suppressing the formation of undesirable DAN adducts. Comparative evaluation of three matrices—DAN/DHB, DHB/0.1% TFA, and DHB—across varying IgG concentrations revealed that the DAN/DHB matrix offers the highest sensitivity and robustness for unpurified samples. Although there are some limitations for quantitative analysis, especially for minor and sialylated glycoform, this novel, purification-free methodology provides a rapid and easily automatable platform for IgG glycan profiling. Full article

This paper investigates secure self-triggered time-varying formation control for quadrotor swarm systems against sequential multi-link scaling attacks, which can be implemented in a self-triggered and fully distributed manner. Firstly, based on the outer-loop position and velocity control model of quadrotors, a fully distributed secure time-varying formation control protocol is constructed under sequential multi-link scaling attacks with three characteristics: distributed, sequential, and scalable, and the design criteria for fully distributed secure time-varying formation control are provided. Then, combining the inner and outer-loop control principles of quadrotors, by constructing Euler angle loop controllers and angular velocity controllers, the conversion of the control input of the outer-loop position and velocity to the inner-loop attitude control is achieved, and fully distributed secure time-varying formation control algorithms for quadrotor UAV swarm systems under attacks are proposed. Finally, the effectiveness and applicability of the fully distributed secure consensus method in the formation control of quadrotor UAV swarms is verified through flight experiments using a quadrotor UAV swarm flight test platform. The research results provide a useful reference for the practical application of the fully distributed secure cooperative control theory. Full article

Herbicide overuse remains a major challenge in sugar beet production because of its environmental and economic impacts. This study addresses three key gaps in UAV-based weed mapping: the lack of leakage-aware benchmarks for North African sugar beet imagery, the limited controlled comparison of one-stage and two-stage detectors under identical experimental conditions, and the limited translation of detection outputs into decision-support layers for site-specific spraying. We develop a reproducible UAV-based deep learning pipeline and present a field case study from Beni Mellal, Morocco. Fast R-CNN, YOLOR, YOLOv7, and YOLOv5 were compared under a unified protocol using identical data partitions, input resolution, augmentation strategies, and evaluation metrics, with locally acquired RGB imagery, COCO-format annotations, and leakage-aware field/flight splits. Under the tested conditions, YOLOv5 achieved the strongest performance, with 97.82% precision, 83.05% recall, 91.61% mAP@0.5, and 72.63% mAP@0.5:0.95. Error analysis indicated that missed detections were mainly associated with small weeds, partial occlusion by sugar beet leaves, and visually similar broadleaf weeds. Detector outputs were further organized into weed-intensity maps and used in a pilot scan-guided spot-treatment workflow on the surveyed parcels. This pilot implementation demonstrates the feasibility of translating UAV detections into prescription layers, but it should not be interpreted as a complete multi-season agronomic or economic validation. The main contribution is therefore a leakage-aware, unified benchmarking protocol and a reproducible end-to-end workflow from UAV detections to field-ready prescription maps. Future work should quantify herbicide savings, treatment efficacy, yield response, economic return, edge-device throughput, and transferability across regions and seasons. Full article

High Velocity Oxy-Fuel (HVOF) thermal spraying enables the deposition of dense coatings with low porosity, high hardness, and good fracture resistance. Tungsten carbide–cobalt (WC-Co) coatings are widely used in industrial and aerospace applications due to their excellent wear resistance; however, improving crack resistance and coating–substrate adhesion remains a key challenge. In this study, WC-Co+Ni composite coatings were deposited on ductile cast iron, with emphasis on the role of Ni addition in controlling microstructure development under HVOF conditions. Microstructural characterization was performed using optical, scanning, and transmission electron microscopy (OM, SEM, TEM), while phase composition and chemical analysis were determined by X-ray diffraction (XRD) and energy-dispersive spectroscopy (EDS). The coatings exhibited a dense, low-porosity microstructure composed of fine WC and W₂C carbides embedded in a Co–Ni binder, with locally nanocrystalline regions. XRD analysis confirmed WC and W₂C as the dominant phases, with weak reflections corresponding to the η-phase (Co₆W₆C), indicating local decarburization. The addition of Ni increases the fraction of the transient liquid phase during particle flight, enhancing carbide dissolution and mass transport in the binder, which accelerates decarburization kinetics and promotes η-phase formation. Simultaneously, Ni modifies the binder into a more ductile Co–Ni matrix, reducing the detrimental effect of brittle η-phase on coating integrity. Mechanical and tribological testing (instrumented indentation and scratch testing) demonstrated improved crack resistance, wear resistance, and adhesion. The results show that Ni addition enables process-driven microstructural tailoring of HVOF-sprayed WC-Co coatings, leading to enhanced performance despite the presence of η-phase. Full article

To address the problems of state estimation bias, dynamic threat response lag, and insufficient safety margin in formation coordination caused by the mismatch between the three-dimensional continuous motion model and the discrete sampling characteristics of sensors in UAV multi-aircraft collaborative inspection missions under complex dynamic environments, this paper studies a trajectory planning method that integrates model predictive control and multi-constraint optimization. By constructing a three-dimensional continuous motion model of the UAV and discretizing it using the Euler integral method, the mapping deviation between the continuous motion characteristics and the discrete working mechanism of the airborne system is solved. Based on the model predictive control method, a patrol trajectory tracking planning model is designed, and state increment and integral augmentation strategies are introduced to transform global reference trajectory tracking into a constrained quadratic programming problem in the rolling time domain, achieving high-precision closed-loop tracking. Furthermore, a dynamic environment model coupling static terrain height field and sudden spherical threat is constructed to systematically characterize the static obstacles and random dynamic threats faced by the UAV in complex scenarios such as mountains and hills. On this basis, multiple constraints such as flight altitude, pitch angle, horizontal turning angle, terrain safety margin, and multi-aircraft collision avoidance are integrated to establish a comprehensive objective function that includes range cost, attitude penalty, and safety cost. Through a collaborative mechanism of global optimization and local online correction, a reference trajectory that meets the requirements of formation safety and flight efficiency is generated and used as the input command for the tracking planning model, forming a closed-loop architecture of global optimization generation, local closed-loop tracking, and dynamic real-time correction for trajectory planning. Experimental results show that the success rate of dynamic obstacle avoidance in complex dynamic environments is always higher than 99.9%, and the mean square error of trajectory tracking is stable in the range of 0.02–0.04 km, which verifies its significant advantages in dynamic adaptability, tracking accuracy and formation safety. Full article

In modern battlefields, the rapid advancement of Counter-UAV (C-UAV) technologies has made single-UAV missions increasingly difficult. This highlights the need for distributed swarm systems that can operate reliably under such threats. Among various swarm coordination methods, hierarchical leader–follower structures have been actively studied for battlefield environments with high risk of agent loss and limited communication. The Virtual Leader-based Formation System (VLFS), which follows this structure, enables formation through a virtual leader. It also introduces a novel collision avoidance approach that allows followers to avoid obstacles during formation flight. However, the conventional VLFS suffers from long convergence time with severe oscillations. In addition, it does not consider inter-UAV collisions and has demonstrated avoidance only in simple obstacle environments. To address these limitations, this paper proposes the VLFS-RF method, which directly integrates a repulsive function into the VLFS. The proposed method consists of four control modes that perform formation tracking, inter-UAV collision avoidance, and obstacle avoidance simultaneously according to the situation. Software-In-The-Loop (SITL) simulations were conducted in a ROS-Gazebo environment using V-shaped and hexagonal formations. The results show that the formation tracking error is reduced by approximately 59% compared to the conventional VLFS. In addition, inter-UAV collisions are prevented during initial convergence, and obstacles are successfully avoided in narrow passages and gaps between two obstacles. These results demonstrate that VLFS-RF is a practical formation control method for UAV swarms in complex environments. Full article

Fixed-wing unmanned aerial vehicle formation control confronts the dual challenges of achieving optimal performance amidst complex nonlinear dynamics and ensuring flight safety by constraining tracking errors. Existing reinforcement learning methods, though effective for optimal control, often overlook these critical safety constraints, which constitutes a serious shortcoming in safety-critical swarm operations where unmanned aerial vehicles confront highly nonlinear dynamics, unknown disturbances, and limited model knowledge, and these practical necessity drives us to synergistically integrate two previously separated techniques. To address this issue, this paper proposes a safe optimal control framework that synergistically integrates prescribed performance control with an actor–critic RL scheme. Specifically, a simplified actor–critic architecture is developed to derive a near-optimal controller for the coupled position–attitude dynamics without requiring an accurate model, thereby enhancing energy efficiency. Concurrently, the prescribed performance control is employed to transform the constrained formation error dynamics into an unconstrained system, guaranteeing that safety distances are strictly maintained. Lyapunov-based analysis proves that all signals in the closed-loop system are semi-globally uniformly ultimately bounded and that formation errors never violate the predefined performance boundaries. Full article

To address the challenges of dynamic obstacles, limited perception, and multi-UAV coordination constraints in complex urban environments, a hierarchical control framework based on a virtual leader-follower architecture is proposed, covering global planning, local obstacle avoidance, and formation coordination. In the global planning layer, a dynamic adaptive strategy rapidly exploring random tree star (DASRRT*) algorithm is proposed. To address the low sampling efficiency and limited path extension in dense environments that affect traditional RRT*, a hybrid guided sampling strategy, inefficient node optimization strategy, and perception-based adaptive step size strategy are designed. Additionally, a multi-objective cost function is introduced to provide smoother trajectories that better comply with dynamic constraints for trajectory tracking. In the local obstacle-avoidance layer, a distributed controller is constructed based on an improved artificial potential field method, integrating collision avoidance control laws derived from a spring-damper model, dynamic obstacle-avoidance laws that account for obstacle velocities, and formation coordination control laws grounded in consensus theory. In the coordination control layer, a real-time local target selection strategy is established to guide the virtual leader to precisely track the global path, and a dual-mode switching mechanism based on environmental complexity is constructed to dynamically adjust the priority between formation maintenance and autonomous obstacle-avoidance tasks. Comparative experimental results show that the proposed DASRRT* algorithm reduces path planning time by an average of 34.78% and shortens path length by 1.15%. Simulation results for formation flight demonstrate that the proposed hierarchical control framework can adaptively adjust control modes in response to changes in environmental complexity, exhibiting strong adaptability to complex environments and a good ability to generalize to various scenes. Full article

This paper presents a comprehensive UAV testbed that establishes quantitative baselines for hardware vulnerability diagnosis and cyber–physical security validation by leveraging comparative flight logs from PX4-based Software-In-The-Loop (SITL) simulations and multiple real-world quadrotor missions. The testbed utilizes a unified data pipeline centered on the uORB message bus and ULog format, enabling the extraction of high-resolution telemetry, including raw Inertial Measurement Unit (IMU) data, state-estimation, and actuator control signals. Evaluated across varying environmental conditions, side-by-side time-series and statistical analyses reveal critical sim-to-real discrepancies in sensor fidelity, GPS interference, and onboard resource behavior that are often overlooked in virtual environments. Real-world data exposes hardware-induced noise, mechanical vibrations, and electromagnetic disturbances that significantly impact flight stability and system reliability. By mathematically quantifying these discrepancies (e.g., via variance and probability distribution shifts), the proposed testbed establishes a rigorous baseline for distinguishing natural physical variability from anomalous or adversarial behavior. Ultimately, this work provides a foundational framework for developing robust anomaly detection models and validating the cyber–physical security of autonomous UAV systems in safety-critical environments. Full article

Ice accretion can significantly degrade aircraft performance and hinder its operational capacities. The ability to detect and characterize ice formation in real time is paramount for enabling timely mitigation strategies. Existing solutions for in-flight ice detection are either physically intrusive, require dedicated hardware that offers only localized readings, or are operationally impractical, depending on complex dynamic models or flight maneuvers unsuitable for standard commercial use. This context highlights a pertinent need for non-intrusive and robust methodologies for detecting actual ice accretion on aircraft. This article proposes a novel, non-intrusive Artificial Intelligence (AI)-driven methodology for real-time aircraft icing detection through the leveraging of data obtained from existing control surface sensors, namely from the aircraft’s ailerons. A supervised learning database was compiled from an Airbus aircraft flight test campaign. In this dataset, flight tests with artificial ice shapes model aircraft behavior under icing conditions, while ice-free tests performed under analogous flight domains represent the nominal scenario. A gradient boosting model was trained on the dataset and evaluated for its performance in accurately identifying the presence of ice accretion. The research shows that aileron sensor data provides sufficient discriminating capacity for in-flight ice accretion detection. Full article

Reliable tabular data correction is a prerequisite for trustworthy analytics in enterprise information systems. Tabular data in such environments frequently contain formatting errors, semantic conflicts, missing values, and cross-field inconsistencies that degrade downstream analytics and machine learning performance. Rule-based methods efficiently handle structural violations but miss context-dependent errors, whereas large language models (LLMs) offer strong semantic-correction capability at inference costs prohibitive for enterprise-scale deployment. This paper formulates data error correction as a progressive decision process and proposes a complexity-aware framework with three processing stages. The first stage applies deterministic rules for low-complexity structural errors. The second stage employs a task-specialized distilled language model for medium-complexity semantic correction. The third stage performs neural probabilistic–logical reasoning on a factor graph for high-complexity cross-field errors. A learnable routing mechanism assigns each record to the appropriate stage based on a lightweight complexity score. Layer-wise conformal prediction is further introduced to construct calibrated prediction sets with coverage guarantees at each stage, together with a rejection mechanism for low-confidence corrections. The framework is evaluated on one enterprise dataset and two public benchmarks (Hospital and Flights). It improves the record-level complete repair rate by 2.1 to 3.1 percentage points over the strongest baseline (GPT-4o-Direct) and by up to 16.8 points over purely rule-based repair, while reducing average inference latency by approximately 80% relative to direct GPT-4o invocation. Ablation studies confirm the critical role of complexity-aware routing and rule-trigger features, and reliability analyses show that hierarchical conformal calibration maintains tighter coverage than single-level alternatives across varying confidence requirements. These results indicate that complexity-aware progressive routing coupled with hierarchical conformal calibration provides a practical path toward high-throughput, auditable, and reliability-controlled data cleaning suitable for enterprise deployment. Full article

Thisarticle develops and analyzes a fractional-order Leslie–Gower prey–predator–parasite system incorporating two discrete delays and nonlocal spatial diffusion. The model’s central novelty lies in the simultaneous integration of three biologically realistic features that have not previously been combined: (i) fractional-order memory effects via a Caputo derivative of order $\alpha \in (0,1]$, (ii) two distinct biological delays—an infection transmission delay ${\tau }_1$ and a predator handling delay ${\tau }_2$—and (iii) nonlocal spatial dispersal modeled through fractional Laplacian operators ${(-\Delta )}^{\gamma /2}$. This triple integration enables the model to capture long-range temporal memory, delayed biological responses, and nonlocal spatial interactions simultaneously, offering insights into dynamics that are challenging to capture with classical integer-order or single-delay formulations. The fractional Laplacian generalizes classical diffusion by allowing long-range dispersal events (Lévy flights), where individuals can occasionally move over large distances with heavy-tailed step-size distributions—a phenomenon observed in many animal movement patterns but absent from standard diffusion models. We provide rigorous proofs of solution existence, uniqueness, non-negativity, and boundedness in both temporal and spatiotemporal settings. Local asymptotic stability conditions are derived for all feasible equilibrium states via characteristic equation analysis. The coexistence equilibrium undergoes a Hopf bifurcation when either delay crosses a critical threshold, with fractional order $\alpha$ modulating the bifurcation point and post-bifurcation oscillation frequency. A Lyapunov functional demonstrates global asymptotic stability of the infection-free equilibrium under biologically interpretable conditions. Turing instability analysis reveals conditions for spontaneous pattern formation, with the fractional exponent $\gamma$ controlling pattern wavelength and correlation length. Numerical simulations validate theoretical predictions, including spatial patterns, traveling waves, and chaos. To bridge theory with potential applications, we outline a statistical framework for parameter estimation and uncertainty quantification, suggesting that $\beta$, $\alpha$, and ${\tau }_1$ may be priority targets for parameter estimation. Full article

Understanding the unsteady aerodynamic behavior of quadrotor unmanned aerial vehicle (UAV) is critical for improving flight stability, control, and performance, particularly in complex operational environments. In closely spaced multirotor configurations, coherent tip vortices shed from each blade convect downstream and form helical vortex streets that interact with subsequent blades and neighboring rotors. These interactions induce rapid fluctuations in local inflow velocity and effective angle of attack, resulting in transient lift variations, increased vibratory loads, and elevated acoustic emissions. This study presents a comprehensive computational investigation of quadrotor rotor interactions and wake dynamics using a large-eddy simulation (LES). Detailed analyses reveal that the formation and evolution of tip vortices and blade–vortex interaction phenomena significantly influence lift fluctuations and aerodynamic loading. The simulations capture transient wake structures and their effects on neighboring rotors, highlighting unsteady aerodynamic mechanisms that are not adequately predicted by conventional RANS or URANS approaches. Parametric studies examining vortex-street offset distance demonstrate the sensitivity of wake-induced instabilities to design and operational parameters. The results provide new physical insights into multirotor wake dynamics and establish the LES as a predictive framework for quantifying unsteady aerodynamic loading in quadrotor drones. The findings provide insights into the complex flow physics of multirotor systems, offering guidance for more accurate modeling, rotorcraft design optimization, and the development of control strategies that mitigate adverse unsteady aerodynamic effects. This study provides new insights into rotor–vortex-street interactions, with applications to multirotor UAVs, by isolating multi-vortex coupling effects and quantifying the influence of horizontal vortex spacing on unsteady aerodynamic loading, complementing existing high-fidelity LES research. Full article

Communication latency is one of the main factors limiting the practical scalability of unmanned aerial vehicle (UAV) swarms operating with distributed formation control. In real-time UAV missions, such as coordinated swarm navigation, autonomous inspection, and aerial monitoring, delayed information exchange directly affects formation stability and operational safety. In practical aerial networks, inter-UAV communication latency is influenced by stochastic effects including jitter, burst delays, and multi-hop propagation, which are rarely captured by the simplified deterministic delay assumptions commonly adopted in analytical formation-control studies. This paper introduces a measurement-informed stochastic delay model and a communication–control delay-feasibility framework that jointly account for per-link latency behavior, multi-hop delay accumulation, and controller-level delay tolerance. The proposed framework is evaluated using an attractive–repulsive distance-based potential field (ARD–PF) formation controller, for which the maximum admissible end-to-end delay is quantified as a function of swarm size and inter-UAV separation. The delay model is calibrated and validated using more than 15,000 in-flight communication delay samples collected from a multi-UAV LoRa platform operating under realistic flight conditions. The results show that different mechanisms limit swarm operation under different operating scenarios. In some configurations, stochastic communication latency becomes the dominant constraint, whereas in others, formation geometry or network load determines the feasible operating region. Based on these elements, the proposed framework characterizes delay-feasible operating regions and predicts the maximum feasible swarm size under distributed formation control and realistic multi-hop communication latency. Full article