Search Results (1,795)

Flying cars are an important vehicle for future urban air mobility. Mainstream flying cars predominantly adopt the e-VTOL-like configuration. Unlike traditional aircraft, these flying cars must be operated by a single pilot. The corresponding hybrid ground-flight control scheme remains immature, with only a few reliability analyses focused on flight safety. Based on the single-pilot operation (SPO) concept, this paper designs a hybrid control scheme for e-VTOL-like flying cars and proposes a restricted driving mode for the the take-off and landing stages and an autonomous driving mode for the cruising stage, respectively. Taking the landing phase as an example, a fault mode analysis and fault tree analysis are conducted for the restricted driving mode, focusing on factors that are sensitive to flight safety. A fault probability analysis is performed of the landing control unit in the restricted driving mode. The calculated probability of the top event occurring is 1.98 × 10⁻⁸ per flight, which proves the feasibility of the design meets the safety requirements. This study provides a foundation for a safety assessment of driving modes in future designs of flying cars. Full article

Numerical simulations were performed using the RANS (Reynolds-averaged Navier–Stokes) approach to analyze the flow field around an aircraft during the landing rollout phase with thrust reversers deployed. The objective was to characterize the flow structure modifications induced by the reversed jet flow and to assess its impact on the aerodynamic performance of various control surfaces. The results demonstrate that the reverse jet flow introduces significant disturbances to the flow field, substantially altering the aerodynamic load distribution over the airframe and causing a marked reduction in overall lift. High-lift devices are particularly susceptible to these effects: the pressure distributions on both the leading-edge slats and trailing-edge flaps are severely disrupted, resulting in a notable degradation of their lift augmentation capabilities. The rudder retains a generally linear response characteristic, though a slight reduction in effectiveness is observed. In contrast, the elevator exhibits a pronounced asymmetry in control effectiveness, with significantly greater degradation under positive deflection compared to negative deflection. This study elucidates the complex interference mechanisms associated with thrust reverser-induced flows and provides valuable insights for the optimization of thrust reverser system design and the enhancement of flight control strategies during the landing phase. It further delivers the first quantitative evaluation of elevator response asymmetry and accompanying lift degradation caused by reverse jet plumes, supplying design-ready metrics for reverser integration. Full article

Accurate state estimation for quadrotors under wind-induced disturbances remains a critical challenge in dynamic outdoor environments. Existing model-based and data-driven approaches often struggle with real-time adaptation and catastrophic forgetting when faced with continuous wind disturbances. This paper proposes an online continual physics-informed learning framework that integrates physics-informed neural networks with continual backpropagation to address these limitations. The physics-informed neural networks architecture embeds quadrotor dynamics into the neural network training process, ensuring physical consistency, while continual backpropagation enables continual learning from real-time streaming data without compromising previously acquired knowledge. Experimental validation on a simulation platform demonstrates the accuracy and robustness of the framework in ideal and wind-disturbed scenarios. Full article

The growing adoption of unmanned aerial vehicles (UAVs) for urban parcel delivery highlights the need for efficient distribution center placement. Particularly in dense urban environments, obstacle avoidance usually increases real path distances beyond straight-line measurements. Considering these distance differences, this study first employs the Informed-RRT* algorithm with spatial indexing to calculate real UAV flight paths between locations. We use the real path distances instead of straight-line distances to determine service coverage areas in the distribution center layout model. In this model, we aim to minimize the total economic cost and maximize customer satisfaction, considering the service range, the balance between delivery capability and demand, and the constraints of the distribution mode. An immune-algorithm-enhanced genetic algorithm, the immune genetic algorithm, is used to solve the model, acquiring the distribution center layout. We conduct a simulation experiment in Shenzhen’s low-altitude airspace and compare the layout results between the real path distance criterion and the straight-line path distance criterion. The results show that using the real path distances instead of the straight-line distances leads to changes in distribution center layout, demonstrating the necessity of this framework. Full article

The integration of diverse robotic platforms with varying payload capacities is a critical challenge in swarm robotics and autonomous systems. This paper presents a robust, modular framework designed to manage and coordinate heterogeneous swarms of autonomous vehicles, including terrestrial, aerial, and aquatic platforms. Built on the Robot Operating System (ROS) and integrated with C++ and ArduPilot, the framework enables real-time communication, autonomous decision-making, and mission execution across multi-domain environments. Its modular design supports seamless scalability and interoperability, making it adaptable to a wide range of applications. The proposed framework was evaluated through simulations and real-world experiments, demonstrating its capabilities in collision avoidance, dynamic mission planning, and autonomous target reallocation. Experimental results highlight the framework’s robustness in managing UAV swarms, achieving 100% collision avoidance success and significant operator workload reduction, in the tested scenarios. These findings underscore the framework’s potential for practical deployment in applications such as disaster response, reconnaissance, and search-and-rescue operations. This research advances the field of swarm robotics by offering a scalable and adaptable solution for managing heterogeneous autonomous systems in complex environments. Full article

The method of extracting energy from a fluid environment using flapping foils offers advantages such as structural simplicity and environmental friendliness. However, its low energy harvesting efficiency remains a significant factor limiting its development. This study employs suction and blower-based control (SBC) to enhance the energy harvesting efficiency of flapping foils. Using an orthogonal experimental design and numerical methods, 49 representative combinations of SBC geometries were selected for numerical simulation. The effects and priority rankings of geometric parameters on foil performance were statistically analyzed. It was found that under the optimal geometry (the suction slot position is 0.54c, the injection slot position is 0.79c, the width of the slot is 0.015c, the angle of the suction slot is −3°, and the angle of the injection slot is −9°), the energy harvesting efficiency can reach 40.7%. Furthermore, under laminar flow conditions, the benefit of SBC increases with higher Reynolds numbers (Re). At Re = 2200, SBC maximized the improvement in energy harvesting efficiency by 76%. No significant correlation was observed between the flapping amplitude and the SBC effect. However, the reduced frequency significantly influences the efficiency improvement generated by SBC. The SBC method shifts the foil’s optimal operating region towards lower reduced frequencies, which benefits energy harvesting efficiency. The research presented herein may have potential applications in the development of marine energy systems and bio-inspired propulsion. Full article

To enhance the reliability of electric propulsion in electric aircraft and address power interruptions caused by current sensor failures, this study proposes a current sensorless fault-tolerant control strategy for permanent magnet synchronous motors (PMSMs) based on a long short-term memory (LSTM) network. First, a hierarchical architecture is constructed to fuse multi-phase electrical signals in the fault diagnosis layer (sliding mode observer). A symbolic function for the reaching law observer is designed based on Lyapunov theory, in order to generate current predictions for fault diagnosis. Second, when a fault occurs, the system switches to the LSTM reconstruction layer. Finally, gating units are used to model nonlinear dynamics to achieve direct mapping of speed/position to phase current. Verification using a physical prototype shows that the proposed method can complete mode switching within 10 ms after a sensor failure, which is 80% faster than EKF, and its speed error is less than 2.5%, fully meeting the high speed error requirements of electric aircraft propulsion systems (i.e., ≤3%). The current reconstruction RMSE is reduced by more than 50% compared with that of the EKF, which ensures continuous and reliable control while maintaining the stable operation of the motor and realizing rapid switching. The intelligent algorithm and sliding mode control fusion strategy meet the requirements of high real-time performance and provide a highly reliable fault-tolerant scheme for electric aircraft propulsion. Full article

This paper develops empirical relationships to estimate FAA/EASA and MIL-3013B rules-compliant take-off field performance for single and multi-engine aircraft. Recent experience with modern aircraft flight manuals revealed that popular empirical legacy methods are no longer accurate; improvements in tires and brakes lead to significantly shorter certified distances. This work relies upon a survey of current operational aircraft and extensive numerical simulations of generic configurations to support the development of a collection of new equations to estimate take-off performance for single and multi-engine aircraft under dry and wet conditions. These relationships are individually tailored for civilian and U.S. Military rules; they account for the superior capability of modern braking systems and the implications of minimum-control speed on the certified distance. Full article

Civil aviation safety remains a critical concern globally, with continuous efforts aimed at reducing accidents and fatalities. This paper focuses on the comprehensive evaluation of civil aviation safety in the Slovak Republic over the past several years, with the main objective of identifying prevailing trends and key risk factors. A comprehensive analysis of 155 accidents and incidents was conducted based on selected operational parameters. Logistic regression was applied to identify potential causal factors influencing various levels of injury severity in aviation accidents. Moreover, the prediction model can also be used to predict the probability of specific injury severity for accidents with given parameter values. The results indicate a clear declining trend in the annual number of aviation safety events; however, the fatality rate has stagnated or slightly increased in recent years. Human error, particularly mistakes and intentional violations of procedures, was identified as the dominant causal factor across all sectors of civil aviation, including flight operations, airport management, maintenance, and air navigation services. Despite technological advancements and regulatory improvements, human-related failures persist as a major safety challenge. The findings highlight the critical need for targeted strategies to mitigate human error and enhance overall aviation safety in the Slovak Republic. Full article

The interaction between a shock wave and a boundary layer promotes corner separation and prevents performance enhancement in a supersonic compressor cascade. Different vortex generator (VG) designs are presented to control corner separation in a supersonic compressor cascade, including endwall VGs (EVG), suction surface VGs (SVG), and combined endwall and suction surface VGs (E-SVGs). It is demonstrated that EVG and coupled E-SVGs reduce losses in the supersonic compressor cascade. For an optimal EVG, the total loss is reduced by 24.6% and the endwall loss is reduced by 33.6%. The coupled E-SVG better controls corner separation and reduces endwall losses by 56.9%. The suppression mechanism is that vortices alter the direction of the separated flow, allowing it to overcome the chordwise pressure gradient. Moreover, the VGs change the shock structure near the endwall. For the EVG, clockwise vortices are effective in controlling corner separation due to their minor effect on the shock structure near the endwall. However, anticlockwise vortices are not suitable for controlling corner separation in the supersonic compressor because they increase the shock strength induced by the VG. The control mechanism of the coupled E-SVG on corner separation is also discussed. Full article

This paper proposes a hierarchical cooperative tracking control method for multi-missile formations under dynamic event-triggered mechanisms, addressing parameter uncertainties and saturated overload constraints. The proposed hierarchical structure consists of a reference-trajectory generator and a trajectory-tracking controller. The reference-trajectory generator considers communication and collaboration among multiple interceptors, imposes saturation constraints on virtual control inputs, and generates reference trajectories for each receptor, effectively suppressing aggressive motions caused by overload saturation. On this basis, a radial basis function neural network (RBFNN) combined with a sliding-mode disturbance observer is adopted to estimate unknown external disturbances and unmodeled dynamics, and the finite-time convergence of the disturbance observer is proved. A tracking controller is then designed to ensure precise tracking of the reference trajectory by missile. This approach not only reduces communication and computational burdens but also effectively avoids Zeno behavior, enhancing the practical feasibility and robustness of the proposed method in engineering applications. The simulation results verify the effectiveness and superiority of the proposed method. Full article

This paper presents a flight control framework based on neural network Model Predictive Control (NN-MPC) to tackle the challenges of acceleration command tracking for supersonic vehicles (SVs) in complex flight environments, addressing the shortcomings of traditional methods in managing nonlinearity, random disturbances, and real-time performance requirements. Initially, a dynamic model is developed through a comprehensive analysis of the vehicle’s dynamic characteristics, incorporating strong cross-coupling effects and disturbance influences. Subsequently, a predictive mechanism is employed to forecast future states and generate virtual control commands, effectively resolving the issue of sluggish responses under rapidly changing commands. Furthermore, the approximation capability of neural networks is leveraged to optimize the control strategy in real time, ensuring that rudder deflection commands adapt to disturbance variations, thus overcoming the robustness limitations inherent in fixed-parameter control approaches. Within the proposed framework, the ultimate uniform bounded stability of the control system is rigorously established using the Lyapunov method. Simulation results demonstrate that the method exhibits exceptional performance under conditions of system state uncertainty and unknown external disturbances, confirming its effectiveness and reliability. Full article

This paper presents a preliminary investigation into the aeroelastic behavior of a tailless flying wing equipped with a rotating wingtip. Based on the configuration of Innovative Control Effectors (ICE) aircraft, an aeroelastic model of the tailless flying wing with a rotating wingtip has been developed. Both numerical simulation and wind tunnel tests (WTTs) are employed to study the aeroelastic characteristics of this unique design. The numerical simulation involves the coupling of computational fluid dynamics (CFD) and implicit dynamic approaches (IDAs). Using the CFD/IDA coupling method, aeroelastic response results are obtained under different flow dynamic pressures. The critical flutter dynamic pressure is identified by analyzing the trend of the damping coefficient, with a focus on its transition from negative to positive values. Additionally, the critical flutter velocity and flutter frequency are obtained from the WTT results. The critical flutter parameters, including dynamic pressure, velocity, and flutter frequency, are examined under different wingtip rotation frequencies and angles. These parameters are derived using both the CFD/IDA coupling method and WTT. The results indicate that the rotating wingtip plays a significant role in influencing the flutter behavior of aircraft with such a configuration. Research has shown that the rotation characteristics of the rotating wingtip are the primary factor affecting its aeroelastic behavior, and increasing both the rotation frequency and rotation angle can raise the flutter boundary and effectively suppress flutter onset. Full article

The increasing demand for noninvasive inspection (NII) of complex civil infrastructures requires overcoming the limitations of traditional ground-penetrating radar (GPR) systems in addressing diverse and large-scale applications. The solution proposed in this study focuses on an initial design that integrates a low-SWaP (Size, Weight, and Power) ultra-wideband (UWB) impulse radar with realistic electromagnetic modeling for deployment on unmanned aerial vehicles (UAVs). The system incorporates ultra-realistic antenna and propagation models, utilizing Finite Difference Time Domain (FDTD) solvers and multilayered media, to replicate realistic airborne sensing geometries. Verification and calibration are performed by comparing simulation outputs with laboratory measurements using varied material samples and target models. Custom signal processing algorithms are developed to extract meaningful features from complex electromagnetic environments and support anomaly detection. Additionally, machine learning (ML) techniques are trained on synthetic data to automate the identification of structural characteristics. The results demonstrate accurate agreement between simulations and measurements, as well as the potential for deploying this design in flight tests within realistic environments featuring complex electromagnetic interference. Full article

The advancement of computational capabilities has allowed for more efficient airfoil analysis and design. Consequently, it has become possible to expand the design space and explore new geometries and configurations. However, the current state of development does not yet support a fully automated optimization process. Instead, the newly introduced capabilities have effectively transferred the previously trial-and-error-based approach used in geometry design to the formulation of the optimization problem. The goal of this work is to study the formulation of an optimization problem and propose a new methodology that better portrays the aircraft’s requirements for airfoil performance. The new objective function, added to an existing tool, estimates the main performance parameters of an aircraft for the Air Cargo Challenge (ACC) 2022 competition using a method that extrapolates the characteristics of the airfoil into the aircraft’s performance. In addition, the traditional relative aerodynamic property improvements, in this work, are coupled with the performance results to smooth the polar curve of the resulting airfoil. The optimization algorithm is based on the free-gradient technique Particle Swarm Optimization (PSO), using the B-spline parametrization and a coupled viscous/inviscid interaction method as the flow solver. Full article