Search Results (321)

This paper presents a novel design for precise vertical landing of drones based on the detection of three phase shifts in the range of ±180°. The design has three inputs to which the signal transmitted from an oscillator located at the landing point arrives with different delays. The circuit increases the aerial tracking volume relative to that achieved by detectors with theoretical unambiguous detection ranges of ±90°. The phase shift measurement circuit uses an analog phase detector (mixer), detecting a maximum range of ±90°and a double multiplication of the input signals, in phase and phase-shifted, without the need to fulfill the quadrature condition. The calibration procedure, phase detector curve modeling, and calculation of the input signal phase shift are significantly simplified by the use of an automatic gain control on each branch, dwhich keeps input amplitudes to the analog phase detectors constant. A simple program to determine phase shifts and guidance instructions is proposed, which could be integrated into the same flight control platform, thus avoiding the need to add additional processing components. A prototype has been manufactured in C band to explain the details of the procedure design. The circuit uses commercial circuits and microstrip technology, avoiding the crossing of lines by means of switches, which allows the design topology to be extrapolated to much higher frequencies. Calibration and measurements at 5.3 GHz show a dynamic range greater than 50 dB and a non-ambiguous detection range of ±180°. These specifications would allow one to track the drone during the landing maneuver in an inverted cone formed by a surface with an 11 m radius at 10 m high and the landing point, when 4 cm between RF inputs is considered. The errors of the phase shifts used in the landing maneuver are less than ±3°, which translates into 1.7% losses over the detector theoretical range in the worst case. The circuit has a frequency bandwidth of 4.8 GHz to 5.6 GHz, considering a 3 dB variation in the input power when the AGC is limiting the output signal to 0 dBm at the circuit reference point of each branch. In addition, the evolution of phases in the landing maneuver is shown by means of a small simulation program in which the drone trajectory is inside and outside the tracking range of ±180°. Full article

This paper presents some of the most significant findings in the design of a hydraulic servomechanism for flight controls, which were primarily achieved by the first author during his activity in an aviation institute. These results are grouped into four main topics. The first one outlines a classical theory, from the 1950s–1970s, of the analysis of nonlinear automatic systems and namely the issue of absolute stability. The uninformed public may be misled by the adjective “absolute”. This is not a “maximalist” solution of stability but rather highlights in the system of equations a nonlinear function that describes, for the case of hydraulic servomechanisms, the flow-control dependence in the distributor spool. This function is odd, and it is therefore located in quadrants 1 and 3. The decision regarding stability is made within the so-called Lurie problem and is materialized by a matrix inequality, called the Lefschetz condition, which must be satisfied by the parameters of the electrohydraulic servomechanism and also by the components of the control feedback vector. Another approach starts from a classical theorem of V. M. Popov, extended in a stochastic framework by T. Morozan and I. Ursu, which ends with the description of the local and global spool valve flow-control characteristics that ensure stability in the large with respect to bounded perturbations for the mechano-hydraulic servomechanism. We add that a conjecture regarding the more pronounced flexibility of mathematical models in relation to mathematical instruments (theories) was used. Furthermore, the second topic concerns, the importance of the impedance characteristic of the mechano-hydraulic servomechanism in preventing flutter of the flight controls is emphasized. Impedance, also called dynamic stiffness, is defined as the ratio, in a dynamic regime, between the output exerted force (at the actuator rod of the servomechanism) and the displacement induced by this force under the assumption of a blocked input. It is demonstrated in the paper that there are two forms of the impedance function: one that favors the appearance of flutter and another that allows for flutter damping. It is interesting to note that these theoretical considerations were established in the institute’s reports some time before their introduction in the Aviation Regulation AvP.970. However, it was precisely the absence of the impedance criterion in the regulation at the appropriate time that ultimately led, by chance or not, to a disaster: the crash of a prototype due to tailplane flutter. A third topic shows how an important problem in the theory of automatic systems of the 1970s–1980s, namely the robust synthesis of the servomechanism, is formulated, applied and solved in the case of an electrohydraulic servomechanism. In general, the solution of a robust servomechanism problem consists of two distinct components: a servo-compensator, in fact an internal model of the exogenous dynamics, and a stabilizing compensator. These components are adapted in the case of an electrohydraulic servomechanism. In addition to the classical case mentioned above, a synthesis problem of an anti-windup (anti-saturation) compensator is formulated and solved. The fourth topic, and the last one presented in detail, is the synthesis of a fuzzy supervised neurocontrol (FSNC) for the position tracking of an electrohydraulic servomechanism, with experimental validation, in the laboratory, of this control law. The neurocontrol module is designed using a single-layered perceptron architecture. Neurocontrol is in principle optimal, but it is not free from saturation. To this end, in order to counteract saturation, a Mamdani-type fuzzy logic was developed, which takes control when neurocontrol has saturated. It returns to neurocontrol when it returns to normal, respectively, when saturation is eliminated. What distinguishes this FSNC law is its simplicity and efficiency and especially the fact that against quite a few opponents in the field, it still works very well on quite complicated physical systems. Finally, a brief section reviews some recent works by the authors, in which current approaches to hydraulic servomechanisms are presented: the backstepping control synthesis technique, input delay treated with Lyapunov–Krasovskii functionals, and critical stability treated with Lyapunov–Malkin theory. Full article

Aircraft icing and snow accumulation are significant threats to flight safety and operational efficiency, necessitating rapid and accurate detection methods. The aim of this study was to develop and comparatively evaluate artificial intelligence (AI) models for the real-time detection of ice and snow on aircraft surfaces using vision systems. A custom dataset of annotated aircraft images under various winter conditions was prepared and augmented to enhance model robustness. Two training approaches were implemented: an automatic process using the YOLOv8 framework on the Roboflow platform and a manual process in the Google Colab environment. Both models were evaluated using standard object detection metrics, including mean Average Precision (mAP) and mAP@50:95. The results demonstrate that both methods achieved comparable detection performance, with final mAP50 values of 0.25–0.3 and mAP50-95 values around 0.15. The manual approach yielded lower training losses and more stable metric progression, suggesting better generalization and a reduced risk of overfitting. The findings highlight the potential of AI-driven vision systems to support intelligent de-icing decision-making in aviation. Future research should focus on refining localization, minimizing false alarms, and adapting detection models to specific aircraft components to further enhance operational safety and reliability. Full article

Fish kills are sudden mass mortalities that occur in freshwater and marine systems worldwide. Fish kill surveys are essential for assessing the ecological and economic impacts of fish kill events, but are often labor-intensive, time-consuming, and spatially limited. This study aims to address these challenges by exploring the application of unoccupied aerial systems (or drones) and deep learning techniques for coastal fish carcass detection. Seven flights were conducted using a DJI Phantom 4 RGB quadcopter to monitor three sites with different substrates (i.e., sand, rock, shored Sargassum). Orthomosaics generated from drone imagery were useful for detecting carcasses washed ashore, but not floating or submerged carcasses. Single shot multibox detection (SSD) with a ResNet50-based model demonstrated high detection accuracy, with a mean average precision (mAP) of 0.77 and a mean average recall (mAR) of 0.81. The model had slightly higher average precision (AP) when detecting large objects (>42.24 cm long, AP = 0.90) compared to small objects (≤14.08 cm long, AP = 0.77) because smaller objects are harder to recognize and require more contextual reasoning. The results suggest a strong potential future application of these tools for rapid fish kill response and automatic enumeration and characterization of fish carcasses. Full article

An electromechanical actuator system was used on a general aviation aircraft to automatically execute programmed test inputs for system identification and parameter estimation. The flight test campaign consisted of approximately 10 flight hours with over 250 carefully designed dynamic test inputs, including multisteps, frequency sweeps, phase-optimized orthogonal multisines, and the optimal inputs for parameter estimation. This paper describes the actuator system retrofitted to the REMOS GX aircraft and the software developed for automatic maneuver injection. The design of the flight test maneuvers is discussed while considering the characteristics and the limits of the onboard actuator system. The initial parameter estimation results are used to evaluate the effectiveness of the applied methods, which is a first for a light sport aircraft. The lessons learned and the advantages of such a system with respect to manual (piloted) flight testing will be described, as will recommendations for future applications of electromechanical actuators to aircraft of this weight class. Full article

This study addresses the optimization of control performance for automatic voltage regulator systems by proposing a fractional-order PIDD² (FOPIDD²) controller design method based on the hybrid Black-winged Kite Algorithm (BWOA). To overcome the challenges of complex parameter tuning and adaptability to high-dimensional nonlinear optimization in PID controllers, the BWOA integrates the precise search mechanism of the Black-winged Kite Algorithm (BKA) with the spiral encircling strategy of the Whale Optimization Algorithm (WOA). By dividing high-fitness individuals into subgroups for parallel optimization, combined with an elitism preservation mechanism and Levy flight perturbation, the BWOA effectively balances global exploration and local exploitation capabilities, preventing premature convergence. Furthermore, a multi-factor objective function is adopted to optimize the six parameters of the FOPIDD² controller. Numerical simulations in MATLAB evaluate the controller’s performance across multiple dimensions, including transient response, frequency-domain stability, trajectory tracking, parameter uncertainty, and disturbance rejection, with comparisons to other recent controllers. Simulation results demonstrate that the BWOA-FOPIDD² controller achieves superior performance in most metrics. Therefore, the proposed method provides an efficient hybrid optimization framework for AVR system controller design. Full article

Large Language Models (LLMs) hold transformative potential for analyzing sequential data, offering an opportunity to enhance the aviation field’s data management and decision support systems. This study explores the capability of the LLaMA 3.1-8B model, an advanced open source LLM, for the tasks of reconstructing flight trajectories using synthetic Automatic Dependent Surveillance Broadcast (ADS-B) data characterized by noise, missing points, and data irregularities typical of real-world aviation scenarios. Comparative analyses against traditional approaches, such as the Kalman filter and the sequence to sequence (Seq2Seq) model with a Gated Recurrent Unit (GRU) architecture, revealed that the fine-tuned LLaMA model significantly outperforms these conventional methods in accurately estimating various trajectory patterns. A novel evaluation metric, containment accuracy, is proposed to simplify performance assessment and enhance interpretability by avoiding complex conversions between coordinate systems. Despite these promising outcomes, the study identifies notable limitations, particularly related to model hallucination outputs and token length constraints that restrict the model’s scalability to extended data sequences. Ultimately, this research underscores the substantial potential of LLMs to revolutionize flight trajectory reconstruction and their promising role in time series data processing, opening broader avenues for advanced applications throughout the aviation and transportation sectors. Full article

This study proposes an unmanned combat aerial vehicle (UCAV)-oriented hierarchical reinforcement learning framework to address the temporal abstraction challenge in autonomous within-visual-range air combat (WVRAC) for UCAVs. The incorporation of maximum-entropy objectives within the MEOL framework facilitates the optimization of both autonomous low-level tactical discovery and high-level option selection. At the low level, three tactical policies (angle, snapshot, and energy tactics) are designed with reward functions informed by expert knowledge, while the high-level policy dynamically terminates current tactics and selects new ones through sparse reward learning, thus overcoming the limitations of fixed-duration tactical execution. Furthermore, a novel automatic curriculum generation mechanism based on Wasserstein Generative Adversarial Networks (WGANs) is introduced to enhance training efficiency and adaptability to diverse initial combat conditions. Extensive experiments conducted in UCAV air combat simulations have shown that MEOL not only achieves significantly better win rates than other policies when training against rule-based opponents, but also that MEOC achieves superior results in tests against tactical intra-option policies as well as other option learning policies. The framework facilitates dynamic termination and switching of tactics, thereby addressing the limitations of fixed-duration hierarchical methods. Ablation studies confirm the effectiveness of WGAN-based curricula in accelerating policy convergence. Additionally, the visual analysis of UCAVs’ flight logs validates the learned hierarchical decision-making process, showcasing the interplay between tactical selection and manoeuvring execution. This research provides novel methodologies combining hierarchical reinforcement learning with tactical domain knowledge for the autonomous decision-making of UCAVs in complex air combat scenarios. Full article

Aiming to reduce energy demand and carbon footprint, minimize noise impact, and enhance flight safety and efficiency during aerotow operations, this study integrates an electric propulsion system and an automatic flight control system (AFCS) into the electric research aircraft e-Genius. An advanced propulsion system is developed using high-performance batteries and available electric drive components, while the AFCS is designed following a systematic process of developing flight control algorithms. Flight tests are then conducted to evaluate the performance of individual components and the overall system. The test results demonstrate that the upgraded propulsion system provides sufficient power to launch sailplanes, even with the maximum takeoff mass, while significantly reducing energy demand when compared to contemporary fossil fueled towplanes. Additionally, the AFCS proves to be stable and robust, successfully following specified commanded states, executing path tracking, and performing aerotow operations. Full article

With the rapid advancement of the civil aviation sector and the concurrent expansion of pilot training programs, a pressing need arises for more efficient assessment methodologies during the pilot training process. Traditional written evaluations conducted by flight instructors are often marred by subjectivity and inefficiency, rendering them inadequate to satisfy the stringent demands of Competency-Based Training and Assessment (CBTA) frameworks. To address this challenge, this study presents a novel multi-label classification model that seamlessly integrates RoBERTa, a robust language model, with Graph Convolutional Networks (GCNs). By simultaneously modeling text features and label interdependencies, this model enables the automated, multi-dimensional classification of instructor evaluations. It incorporates a dynamic weight fusion strategy, which intelligently adjusts the output weights of RoBERTa and GCNs based on label correlations. Additionally, it introduces a label co-occurrence graph convolution layer, designed to capture intricate higher-order dependencies among labels. This study is based on a real-world dataset comprising 1078 evaluations and 158 labels, covering six major dimensions, including operational capabilities and communication skills. To provide context for the improvement, the proposed RoBERTa + GCN model is compared with key baseline models, such as BERT and LSTM. The results show that the RoBERTa + GCN model achieves an F1 score of 0.9737, representing an average improvement of 4.73% over these traditional methods. This approach enhances the consistency and efficiency of flight training assessments and provides new insights into integrating natural language processing and graph neural networks, demonstrating broad application prospects. Full article

The construction of parallel runways is an effective solution to address the constraints of urban land resources and mitigate flight delays caused by the increasing volume of air traffic. To ensure the safety of parallel approach operations and further enhance operational efficiency, this study proposes a quantitative safety risk assessment method for parallel approaches based on Monte Carlo simulation (MCS) and extreme value theory (EVT). Taking a parallel runway at a major airport in Southwest China as a case study, historical Automatic Dependent Surveillance-Broadcast (ADS-B) trajectory data were processed and analyzed to derive traffic flow characteristics and the actual distribution of approach performance. Subsequently, we developed a collision probability estimation model for parallel approaches based on Monte Carlo–extreme value theory (MC-EVT). Monte Carlo simulation was employed to conduct simulation experiments on the parallel approach process, and the collision risk was quantitatively assessed by integrating experimental data with an analysis based on extreme value theory. Finally, taking the parallel runways of a major airport in southwest China as a case study, experiments were conducted under various parallel approach scenarios to quantitatively assess the collision risk between aircraft. The experimental results indicate that the MC-EVT-based safety risk assessment method for parallel approaches reduces the reliance on traffic flow assumptions. Compared to the conventional Monte Carlo method, it achieves a faster convergence rate, significantly reduces computational workload, and improves computational efficiency by a factor of ten, thus demonstrating that the proposed method is capable of accurately and effectively quantifying low-probability collision risks. Furthermore, the findings reveal a strong correlation between parallel runway width and collision risk. The approach risk under a mixed-aircraft-type configuration is higher than that of a single-aircraft-type configuration, while offset approaches can enhance approach safety. This study can provide valuable references for the construction of parallel runways and the development of regulatory frameworks for parallel approach operations in China. Full article

Thunderstorms are weather phenomena that comprise thunder and lightning. They typically result in heavy precipitation, including rain, snow, and hail. Thunderstorms have adverse effects on flight at both the ground and the upper levels of the troposphere. The characteristics of the thunderstorm of Istanbul International Airport (International Civil Aviation Organization (ICAO) code: LTFM) have been investigated because it is currently one of the busiest airports in Europe and the seventh-busiest airport in the world. Geopotential height (m), temperature (°C), dewpoint temperature (°C), relative humidity (%), mixing ratio (g kg⁻¹), wind direction (°), and wind speed (knots) data for the ground level and upper levels of the İstanbul radiosonde station were obtained from the Turkish State Meteorological Service (TSMS) for 29 October 2018 and 1 January 2023. Surface data were regularly collected by the automatic weather stations near the runway and the upper-level data were collected by the radiosonde system located in the Kartal district of İstanbul. Thunderstorm statistics, stability indices, and meteorological variables at the upper levels were evaluated for this period. Thunderstorms were observed to be more frequent during the summer, with a total of 51 events. June had the highest number of thunderstorm events with a total of 32. This averages eight events per year. A total of 72.22% occurred during trough and cold front transitions. The K index and total totals index represented the thunderstorm events better than other stability indices. In total, 75% of the thunderstorm days were represented by these two stability indices. The results are similar to the covering of this area: the convective available potential energy (CAPE) values which are commonly used for atmospheric instability are low during thunderstorm events, and the K and total totals indices are better represented for thunderstorm events. This study investigates thunderstorm events at the LTFM, providing critical insights into aviation safety and operational efficiency. The research aims to improve flight planning, reduce weather-related disruptions, and increase safety and also serves as a reference for airports with similar climatic conditions. Full article

Aircraft trajectory prediction is challenging because of the flight process with uncertain kinematic motion and varying dynamics, which is characterized by intricate temporal dependencies of the flight surveillance data. To address these challenges, this study proposes a novel hybrid prediction framework, the IMM-Informer, which integrates an interacting multiple model (IMM) approach with the deep learning-based Informer model. The IMM processes flight tracking with multiple typical motion models to produce the initial state predictions. Within the Informer framework, the encoder captures the temporal features with the ProbSparse self-attention mechanism, and the decoder generates trajectory deviation predictions. A final fusion combines the IMM estimators with Informer correction outputs and leverages their respective strengths to achieve accurate and robust predictions. The experiments are conducted using real flight surveillance data received from automatic dependent surveillance-broadcast (ADS-B) sensors to validate the effectiveness of the proposed method. The results demonstrate that the IMM-Informer framework has notable prediction error reductions and significantly outperforms the prediction accuracies of the standalone sequence prediction network models. Full article

The Global Navigation Satellite System (GNSS) is widely used in various applications of UAVs (unmanned aerial vehicles) that require precise positioning or navigation. However, GNSS signals can be blocked in specific environments and are susceptible to jamming and spoofing, which will degrade the performance of navigation systems. In this study, a deep learning-based navigation system for the automatic landing of fixed-wing UAVs in GNSS-denied environments is proposed to serve as an alternative navigation system. Most visual-based runway landing systems are typically focused on runway detection and localization while neglecting the issue of integrating the localization solution into flight control and guidance laws to become a complete real-time automatic landing system. This study addresses these problems by combining runway detection and localization methods, YOLOv8 and CNN (convolutional neural network) regression, to demonstrate the robustness of deep learning approaches. Moreover, a line detection method is employed to accurately align the UAV with the runway, effectively resolving issues related to runway contours. In the control phase, the guidance law and controller are designed to ensure the stable flight of the UAV. Based on a deep learning model framework, this study conducts experiments within the simulation environment, verifying system stability under various assumed conditions, thereby avoiding the risks associated with real-world testing. The simulation results demonstrate that the UAV can achieve automatic landing on 3-degree and 5-degree glide slopes, whether it is directly aligned with the runway or deviating from it, with trajectory tracking errors within 10 m. Full article

In this paper, a data-driven system identification method is presented based on the data fusion of a dynamic model and flight test data. The dynamic model is built by a combination of nonlinear auto-regressive networks (NARX) and the steady-state model. In such a combination, NARX can calibrate the dynamic characteristics of high-pressure and low-pressure rotor speed based on automatic control system steady-state models. As such, the calibrated engine model’s output speed is able to meet the requirements of simulation test tolerance accuracy. To enhance the robustness of the dynamic model against measurement noise, the Kalman filter is used to fuse the model prediction and the measurement data with noise. As such, the fused model can efficiently remove the influence of measurement noise and improve prediction accuracy. The proposed method supports the construction of reliable and environment-adaptive platforms for simulation application verification and provides high-fidelity simulation incentives for the realization of simulation test scenarios in the aviation industry. Full article