Search Results (90)

This paper proposes a novel hierarchical control architecture that combines Model Predictive Control (MPC) with Explicit Model-Following Control (EMFC) to enable accurate and efficient trajectory tracking for quadrotor electric Vertical Takeoff and Landing (eVTOL) aircraft operating in urban environments. The approach addresses the challenges of strong nonlinear dynamics, multi-axis coupling, and stringent safety constraints by separating the planning task from the fast-response control task. The MPC layer generates constrained velocity and yaw rate commands based on a simplified inertial prediction model, effectively reducing computational complexity while accounting for physical and operational limits. The EMFC layer then compensates for dynamic couplings and ensures the rapid execution of commands. A high-fidelity simulation model, incorporating rotor flapping dynamics, differential collective pitch control, and enhanced aerodynamic interference effects, is developed to validate the controller. Four representative ADS-33E-PRF tasks—Hover, Hovering Turn, Pirouette, and Vertical Maneuver—are simulated. Results demonstrate that the proposed controller achieves accurate trajectory tracking, stable flight performance, and full compliance with ADS-33E-PRF criteria, highlighting its potential for autonomous urban air mobility applications. Full article

While electric unmanned aerial vehicles (UAVs) offer advantages in noise reduction, safety, and operational efficiency, their endurance is limited by current battery technology. Extending flight autonomy without compromising performance is a critical challenge in UAV system development. Previous studies introduced hybrid micro-turbogenerator architectures, but limitations in control stability and output power constrained their practical implementation. This study aimed to finalize the design and experimental validation of an optimized hybrid power system featuring a micro-turboprop engine mechanically coupled to an upgraded electric generator. A fuzzy logic-based control algorithm was implemented on a single-board computer to enable autonomous voltage regulation. The test bench architecture was reinforced and instrumented to allow stable multi-stage testing across increasing power levels. Results demonstrated stable voltage control at 48 VDC and electrical power outputs up to 3 kW, with an estimated maximum of 3.5 kW at full throttle. Efficiency was calculated at approximately 67%, and analysis of the generator’s KV constant revealed that using a lower KV variant (KV80) could reduce required rotational speed (RPM) and improve performance. These findings underscore the value of adaptive hybridization in UAVs and suggest that tuning generator electromechanical parameters can significantly enhance overall energy efficiency and platform autonomy. Full article

To address the growing demand for industrial software in the digital transformation of small and medium-sized enterprises (SMEs) in the manufacturing sector, and to ensure the stable integration and operation of multi-source heterogeneous industrial software under complex conditions—such as heterogeneous compatibility, component dependencies, and uncertainty disturbances—this study established a comprehensive evaluation index system for service composition and optimal selection (SCOS). The system incorporated key criteria including service time, service cost, service reputation, service delivery quality, and availability. Based on this, a bi-objective SCOS model was established with the goal of maximizing both quality of service (QoS) and availability. To efficiently solve the proposed model, a hybrid enhanced multi-objective Gray Wolf Optimizer (HEMOGWO) was developed. This algorithm integrated Tent chaotic mapping and a Levy flight-enhanced differential evolution (DE) strategy. Extensive experiments were conducted, including performance evaluation on 17 benchmark functions and case studies involving nine industrial software integration scenarios of varying scales. Comparative results against state-of-the-art, multi-objective, optimization algorithms—such as MOGWO, MOEA/D_DE, MOPSO, and NSGA-III—demonstrate the effectiveness and feasibility of the proposed approach. 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

The Flexible Combined Imager (FCI), developed as the next-generation imager for the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) Meteosat Third Generation (MTG) satellite series, represents a significant advancement over its predecessor, SEVIRI, on the Meteosat Second Generation (MSG) satellites. FCI offers more spectral bands, higher spatial resolution, and faster imaging capabilities, supporting a wide range of applications in weather forecasting, climate monitoring, and environmental analysis. On 13 January 2024, the FCI onboard MTG-I1 (renamed Meteosat-12 in December 2024) experienced a critical anomaly involving the failure of its onboard Calibration and Obturation Mechanism (COM). As a result, the use of the COM was discontinued to preserve operational safety, leaving the instrument dependent on alternative calibration methods. This loss of onboard calibration presents immediate challenges, particularly for the infrared channels, including image artifacts (e.g., striping), reduced radiometric accuracy, and diminished stability. To address these issues, EUMETSAT implemented an external calibration approach leveraging algorithms from the Global Space-based Inter-Calibration System (GSICS). The inter-calibration algorithm transfers stable and accurate calibration from the Infrared Atmospheric Sounding Interferometer (IASI) hyperspectral instrument aboard Metop-B and Metop-C satellites to FCI’s infrared channels daily, ensuring continued data quality. Comparisons with Cross-track Infrared Sounder (CrIS) data from NOAA-20 and NOAA-21 satellites using a similar algorithm is then used to validate the radiometric performance of the calibration. This confirms that the external calibration method effectively compensates for the absence of onboard blackbody calibration for the infrared channels. For the visible and near-infrared channels, slower degradation rates and pre-anomaly calibration ensure continued accuracy, with vicarious calibration expected to become the primary source. This adaptive calibration strategy introduces a novel paradigm for in-flight calibration of geostationary instruments and offers valuable insights for satellite missions lacking onboard calibration devices. This paper details the COM anomaly, the external calibration process, and the broader implications for future geostationary satellite missions. Full article

With the enhancement of thermodynamic cycle parameters and heat dissipation constraints in aero-engines, effective thermal management has become a critical challenge to ensure safe and stable engine operation. This study developed a transient temperature evaluation model applicable to the entire flight envelope, considering fluid–solid coupling heat transfer on both the main flow path and fuel systems. Firstly, the impact of heat transfer on the acceleration and deceleration performance of a low-bypass-ratio turbofan engine was analyzed. The results indicate that, compared to the conventional adiabatic model, the improved model predicts metal components absorb 4.5% of the total combustor energy during cold-state acceleration, leading to a maximum reduction of 1.42 kN in net thrust and an increase in specific fuel consumption by 1.18 g/(kN·s). Subsequently, a systematic evaluation of engine thermal management performance throughout the complete flight mission was conducted, revealing the limitations of the existing thermal management design and proposing targeted optimization strategies, including employing Cooled Cooling Air technology to improve high-pressure turbine blade cooling efficiency, dynamically adjusting low-pressure turbine bleed air to minimize unnecessary losses, optimizing fuel heat sink utilization for enhanced cooling performance, and replacing mechanical pumps with motor pumps for precise fuel supply control. Full article

General aviation trajectory prediction plays a crucial role in enhancing safety and operational efficiency at non-towered airports. However, current research faces multiple challenges including variable weather conditions, complex aircraft interactions, and flight pattern constraints specified by general aviation regulations. This paper proposes a deep learning method based on stochastic processes aimed at addressing uncertainty issues in general aviation trajectory prediction. First, we design a probabilistic encoder–decoder structure enabling the model to output trajectory distributions rather than single paths, with regularization terms based on Lyapunov stability theory to ensure predicted trajectories maintain stable convergence while satisfying flight patterns. Second, we develop a multi-layer attention mechanism that accounts for weather factors, enhancing the model’s responsiveness to environmental changes. Validation using the TrajAir dataset from Pittsburgh-Butler Regional Airport (KBTP) not only advances deep learning applications in general aviation but also provides new insights for solving trajectory prediction problems. Full article

In the U-Space environment, seamless communication between key stakeholders—such as U-Space Service Providers (USSP), Common Information Service Providers (CISP), and drone operators—is very important for the safe and efficient management of Unmanned Aerial Vehicle (UAV) operations. A major challenge in this context is minimizing communication latency, which directly affects the performance of time-sensitive services. This study investigates latency issues by evaluating two communication protocols: push–pull (using REST-API and ZeroMQ) and publish–subscribe (using AMQP and MQTT). Through a case study focused on drone detection, the research examines latency across critical operational activities, including conformance monitoring, flight plan confirmation, and the transmission of alerts via the USSP system under varying message intervals and payload sizes. The results indicate that while message interval has a significant influence on latency, message size has a minimal effect. Furthermore, the push–pull protocols consistently deliver lower and more stable latency compared to publish–subscribe protocols under the tested conditions. Both approaches, however, achieve latency levels that align with EASA’s operational requirements for U-Space systems. Full article

Background: In offshore energy logistics, contracted helicopters frequently transport personnel to and from offshore installations. Regular and efficient transportation is vital to maintain planned activities at the installations. We focus on tactical helicopter planning from a single heliport for a period of stable weekly transport demands in a heliport operating area on the Norwegian Continental Shelf (NCS). This results in the construction of a repetitive weekly flight program, integrating the selection of helicopter resources optimally matching demand with the generation of a weekly timetable of flights assigning them to start times. The purpose of our research is to develop optimisation-based weekly flight program planning algorithms for energy companies operating on the NCS. Methods: We present a developed two-step solution method sequentially generating possible flights and solving a flight-based integer programming model, and an iterative algorithm based on the decomposition of the flight-based model for the construction of cost-optimal weekly flight programs. Results: The developed algorithms were validated on the real instances from Equinor, the largest NCS energy operator. The decomposition-based algorithm was able to solve to optimality all tested instances, with up to 20 installations served from the heliport within less than 9 min. Conclusions: Equinor logistics planners have tested and verified that the developed flight-based model satisfies the goals and planning policies imposed on the NCS for integrated tactical helicopter planning. Considering the advantages of the decomposition-based algorithm performance in solution quality and speed, energy companies on the NCS find it well-suited as a solution engine in the highly demanded automated decision support tools for tactical helicopter transportation planning. Full article

Agriculture, as a core pathway for advancing modern agricultural development, emphasizes data-driven perception and intelligent decision-making. Unmanned aerial vehicles (UAVs), with advantages such as high-resolution imaging, flexible deployment, and adaptability to diverse terrains, have become an essential tool in this domain. This Editorial synthesizes the key findings from nine representative studies featured in this Special Issue, focusing on recent advancements in UAV-based remote sensing, flight control, and precision spraying. The results indicate that the integration of multispectral imagery with deep learning models significantly enhances crop identification and parameter inversion accuracy. Flight control performance has been greatly improved through innovations such as free-tail configuration optimization and fuzzy sliding mode composite control, ensuring stable operations in complex environments. In the realm of precision spraying, progress in wind vortex regulation and airflow modeling has led to improved droplet deposition consistency and target accuracy. Overall, UAV technologies demonstrate strong potential for cross-disciplinary integration and scalable application, offering robust support for the intelligent transformation of agricultural production. 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

Heterogeneous UAVs offer unique advantages in multi-agent systems due to their varying capabilities including (a) different payload capacities, (b) maneuverability, and (c) flight endurance. These properties made them particularly well suited for complex operations such as lifting and transporting irregularly shaped payloads with even mass distribution. Homogeneous UAV systems may face limitations. By utilizing these capabilities, heterogeneous UAVs enable efficient resource utilization, adaptability to dynamic conditions, and precise coordination for challenging missions. This paper presents a distributed sliding mode control (DSMC) strategy, designed to achieve stable trajectory tracking and synchronized irregular-shaped payload lifting by heterogeneous UAVs. The proposed approach ensures maintaining stability throughout the operation. The framework dynamically adjusts roll, pitch, and yaw angles to achieve precise payload lifting, while maintaining stability during transportation. Additionally, we conduct a comparative analysis between DSMC and PID controller, evaluating their performance in terms of trajectory tracking accuracy, payload stability, and safety distance between the drones. Simulation results demonstrate the effectiveness of the proposed method in minimizing trajectory tracking errors, achieving smooth payload transportation, and ensuring robust performance. The findings highlight the potential of DSMC as a reliable control strategy for multi-UAV coordination in complex payload transportation scenarios. Full article

As the global demand for sustainable energy grows, hydrogen fuel has become a promising alternative to fossil fuels, particularly in the drone industry. Drones, known for their high mobility and low operational costs, are increasingly utilized in sectors like defense, agriculture, and logistics. However, traditional battery-powered drones are limited by flight duration and recharging times. Hydrogen fuel cells present a viable solution, with effective hydrogen pressure regulation being the key to ensuring their stable operation. This paper presents an innovative valve design for drones, developed to regulate the pressure reduction of high-pressure hydrogen gas from the storage tank to the fuel cell system. The valve incorporates a multi-stage pressure reduction mechanism, optimized to minimize the adverse effects of gas flow. Using a combination of experimental tests and numerical simulations, the study examines hydrogen flow characteristics at various valve openings, focusing on pressure, velocity distribution, and energy consumption. The results demonstrate that narrowing the valve opening improves pressure reduction, effectively controlling hydrogen flow and stabilizing pressure, thereby ensuring proper fuel cell operation. Further analysis reveals that smaller valve openings help reduce turbulence and energy loss, improving flow stability and system efficiency. This research provides valuable insights into hydrogen pressure regulation in drone fuel delivery systems, especially under extreme conditions such as high pressures and large pressure ratios. The findings offer both theoretical and practical guidance for optimizing hydrogen fuel delivery systems in fuel cell-powered drones, contributing to improve energy management and enhance performance in future drone applications. Full article

Quad-tilt-wing (QTW) Unpiloted Aerial Vehicles (UAVs) combine the vertical takeoff and landing capabilities of rotary-wing designs with the high-speed, long-range performance of fixed-wing aircraft, offering significant advantages in both civil and military applications. The unique configuration of QTW UAVs presents complex control challenges due to nonlinear dynamics, strong coupling between translational and rotational motions, and significant variations in aerodynamic characteristics during transitions between flight modes. To address these challenges, this study develops an optimal control framework tailored for real-time operations. A State-Dependent Riccati Equation (SDRE) approach is employed for attitude control, addressing nonlinearities, while a Linear Quadratic Regulator (LQR) is used for position and velocity control to achieve robustness and optimal performance. By integrating these strategies and utilizing the inverse dynamics approach, the proposed control system ensures stable and efficient operation. This work provides a solution to the optimal control complexities of QTW UAVs, advancing their applicability in demanding and dynamic operational environments. Full article

Autonomous vehicles must make quick and accurate decisions to operate efficiently in complex and dynamic urban traffic environments, necessitating a reliable and stable learning mechanism. The proximal policy optimization (PPO) algorithm stands out among reinforcement learning (RL) methods for its consistent learning process, ensuring stable decisions under varying conditions while avoiding abrupt deviations during execution. However, the PPO algorithm often becomes trapped in a limited search space during policy updates, restricting its adaptability to environmental changes and alternative strategy exploration. To overcome this limitation, we integrated Lévy flight’s chaotic and comprehensive exploration capabilities into the PPO algorithm. Our method helped the algorithm explore larger solution spaces and reduce the risk of getting stuck in local minima. In this study, we collected real-time data such as speed, acceleration, traffic sign positions, vehicle locations, traffic light statuses, and distances to surrounding objects from the CARLA simulator, processed via Apache Kafka. These data were analyzed by both the standard PPO and our novel Lévy flight-enhanced PPO (LFPPO) algorithm. While the PPO algorithm offers consistency, its limited exploration hampers adaptability. The LFPPO algorithm overcomes this by combining Lévy flight’s chaotic exploration with Apache Kafka’s real-time data streaming, an advancement absent in state-of-the-art methods. Tested in CARLA, the LFPPO algorithm achieved a 99% success rate compared to the PPO algorithm’s 81%, demonstrating superior stability and rewards. These innovations enhance safety and RL exploration, with the LFPPO algorithm reducing collisions to 1% versus the PPO algorithm’s 19%, advancing autonomous driving beyond existing techniques. Full article