Miklós Kuczmann

Reliable V2X downlink communication along mountainous road corridors is strongly affected by terrain-induced shadowing, diffraction, and rapid spatial field-strength variations. These effects are particularly relevant at 3.6 GHz, where infrastructure-based wireless systems offer a balance between coverage and capacity while remaining sensitive to topographical obstruction. Deterministic propagation models, such as Parabolic Equation Modeling (PEM), can represent terrain-related effects accurately, but their computational cost limits repeated route-level evaluations. This paper investigates an artificial intelligence (AI)-enhanced propagation modeling approach for a mountainous V2X downlink scenario with an average 30 m high base station as a transmitter and a 1.5 m vehicle-mounted receiver (downlink). Fast empirical and hybrid baseline models are corrected toward a PEM reference using machine-learning-based delta correction. Four learning strategies are evaluated: ridge regression, kernel ridge regression, gradient boosting regression trees, and a neural network model. Besides conventional error metrics, coverage availability is used to assess route-level V2X reliability.

The present study investigates the applicability of capacitor-based emergency energy buffering in a tethered unmanned aerial vehicle (tUAV) system under sudden power supply interruption conditions. For the investigations, a custom-developed one-degree-of-freedom (1-DOF) hardware-in-the-loop (HIL) experimental platform was utilized, incorporating a mini drone moving along a vertical guide rod. The system was controlled in a MATLAB-Arduino environment using a sliding mode control (SMC) algorithm. Capacitor banks with different capacitance values were connected in parallel to the power supply line of the propulsion system using diode isolation. During the experiments, the main power supply was manually interrupted, and the transient behavior of the system was analyzed. The results demonstrated that lower-capacitance capacitors did not produce measurable changes, whereas the higher-capacitance capacitor bank increased the current decay time. However, the system was unable to maintain stable hovering or controlled emergency descent following the loss of the main power supply.

During the development of modern electric drives, vibroacoustic quality is one of the most critical parameters alongside motor performance and efficiency. One source of poor vibroacoustic characteristics is the inhomogeneity and imperfection of the soft magnetic materials found in the electrical machine. The primary indicators of these defects are the selected electrical steel and the machining processes used for the stator (waterjet cutting, laser cutting, punching). These inhomogeneities excite the mass-spring-damper oscillating system through higher-order harmonics induced in the air gap. The objective of the present research is the identification of these air-gap harmonics, for which this study aims to perform a systematic investigation chain starting from closed magnetic circuit measurements and building up to measurements loaded with an air gap. This is based on classical Epstein measurements, followed by induction measurements conducted in the air gap of a C-core. The test specimens were fabricated from M400-50A electrical steel using three different machining methods. The results show that the machining process has a significant impact on the magnetic characteristics, and the exit angle of the induction vectors measurable in the air gap also deviates from the ideal.

Deterministic radio wave propagation models provide high accuracy in complex outdoor environments but remain computationally impractical for large-scale network planning and spectrum management. In contrast, empirical and hybrid models offer low complexity at the expense of reduced accuracy, systematic bias, and limited terrain sensitivity. This paper proposes a unified delta learning framework that enhances fast baseline propagation models by learning a data-driven correction toward a deterministic Parabolic Equation Modeling (PEM) reference. A key novelty lies in a compact, physics-informed feature representation that replaces the full terrain profile with an 18-dimensional vector combining local geometric descriptors, global terrain characteristics, and baseline responses, enabling accurate correction with low-dimensional input. The study also provides the first systematic investigation of delta-based correction across multiple widely used propagation models. The framework is evaluated for free-space propagation, ITU-R P.1546, ITU-R P.1812, and ITU-R P.452 using ridge regression, kernel ridge regression, gradient boosting regression trees, and a neural network model. Model performance is assessed in terms of error reduction, bias mitigation, robustness across learning algorithms, and profile-level generalization to previously unseen propagation paths within the considered terrain categories. Results show substantial error reduction, with up to twofold improvement for simpler baseline models and consistent gains for hybrid models, while preserving computational efficiency.

This study analyzes how dynamic disturbances influence the decisions made during the human supervision of autonomous unmanned aerial vehicles. While previous research has primarily focused on control algorithms and system stability, the effect of disturbances originating from system dynamics on operator intervention behavior has been less extensively investigated. To examine this problem, a hardware-in-the-loop (HIL) experimental framework was developed, which is based on a previously validated unmanned aerial vehicles (UAVs) test platform and was adapted in this study to enable the investigation of human supervisory decision-making. Participants observed the behavior of an autonomously operating system under controlled disturbances and were provided with the possibility to intervene by activating an emergency landing mechanism. The results indicate that the disturbance intensity had a significant effect on intervention decisions, while the reaction times did not show notable differences. This finding suggests that supervisory behavior is primarily determined by the evaluation of the system state rather than by timing characteristics. It also identifies that subjective risk perception plays a decisive role in the formation of intervention decisions, indicating the presence of an implicit decision threshold for participant behavior. The research findings offer a novel approach to the interpretation of human–UAV interaction by emphasizing the role of system dynamics in shaping user decisions. The presented method may provide a foundation for the development of predictive and adaptive supervisory systems that take into account the characteristics of human decision-making, thereby contributing to the design of safer and more efficient autonomous systems.

The energy efficiency and hover stability of unmanned aerial vehicles are critical factors, since improper battery utilization and unstable control are major sources of operational failures and accidents. The proportional–integral–derivative (PID) controller, which is applied in approximately 97% of multirotor unmanned aerial vehicle (UAV) systems, is widely used due to its simplicity; however, it is sensitive to external disturbances and often fails to ensure optimal energy utilization, resulting in reduced flight time. Therefore, the experimental investigation of advanced control methods in a real physical environment is well justified. The objective of the present research is the comparative evaluation of seven control strategies—PID, linear quadratic controller with integral action (LQI), model predictive control (MPC), sliding mode control (SMC), backstepping control, fractional-order PID (FOPID), and H∞ control—using a single-degree-of-freedom drone test platform in a MATLAB R2023b-Arduino hardware-in-the-loop (HIL) environment. Although the theoretical advantages and model-based results of the aforementioned control methods are well documented, the number of real-time comparative HIL experiments conducted under identical physical conditions remains limited. Consequently, only a small amount of unified and directly comparable experimental data is available regarding the performance of different controllers. The measurements were performed at a reference height of 120 mm under disturbance-free conditions and under wind loading with a velocity of 10 km/h applied at an angle of 45°. The controller performance was evaluated based on hover accuracy, settling time, overshoot, and real-time measured power consumption. The results indicate that modern control strategies provide significantly improved energy efficiency and faster stabilization compared to the PID controller in both disturbance-free and wind-loaded test scenarios. The investigations confirm that several advanced controllers can be applied more effectively than the PID controller to enhance hover stability and reduce energy consumption.

The aim of the present study was to investigate a control strategy designed for the BLDC (Brushless Direct Current) motor of a quadcopter-type drone, with particular emphasis on the precise and stable maintenance of altitude in a disturbed environment. Two different control methods were implemented and compared during the research: the classical PID (Proportional–Integral–Derivative) controller and the H∞ (H-infinity) control technique. The investigations were carried out along two approaches. On the one hand, a transfer function—reproduced from a previous study—was used as a possible reference, and tested in the MATLAB simulation environment. On the other hand, a custom-developed physical prototype with one degree of freedom was created, capable of vertical motion along a single axis, allowing for the examination of altitude control under real-world conditions. The purpose of the system was to maintain a predetermined hovering altitude even in the presence of external disturbances, such as artificially generated wind. During the design of the control algorithms, a state-space-based modeling approach was applied, and appropriate weighting functions were defined, with special attention given to robustness against disturbances, control accuracy, and energy-efficient operation. The simulation results showed that the H∞ controller reduced average power demand by 19.43% compared to PID control, while practical measurements demonstrated a 38% decrease in average power consumption. In addition, overshoot was reduced by 96% and oscillation amplitude by 86% under wind disturbance. The objective of the research was to examine the practical applica-bility of an advanced control method that can provide greater stability, reliability, and energy effi-ciency under varying environmental conditions compared to traditional solutions.

Unmanned Aerial Vehicles are being applied in an increasing number of fields; however, their autonomous operation is associated with significant regulatory challenges. In this study, the performance of a PID and a Model Predictive Controller is compared based on the transfer function of the BLDC motor of a quadcopter using MATLAB simulations in the presence of white noise. The simulation results are used as reference values for measurements conducted on a cost-effective, custom-developed prototype drone. The prototype has been designed for short-duration hovering, allowing for an initial evaluation, but a more thorough analysis requires prolonged hovering tests to be carried out in an industrial environment. Based on the results, a recommendation is formulated for improving the PID controller to achieve performance closer to that of the MPC. The research is aimed at enhancing the energy efficiency of UAV systems and optimizing battery capacity, enabling longer autonomous flight time and more reliable control.

In the control of unmanned aerial vehicles (UAVs), proportional–integral–derivative (PID) controllers continue to play a central role due to their simplicity, rapid implementation, and low computational demand. However, the classical PID approach faces inherent limitations, including sensitivity to disturbances, complex tuning, and limited adaptability, which increasingly highlight the relevance of advanced control methods. Recent research has investigated adaptive, fuzzy, and fractional-order PID (FOPID) solutions, as well as methods based on artificial intelligence and predictive control techniques such as linear quadratic regulator (LQR), model predictive control (MPC), sliding mode control (SMC), and H∞ approaches, many of which provide improved robustness and performance. The purpose of this study is to review the application of PID control in UAV systems, to identify its limitations, and to present alternative control strategies that may become dominant in the future through the development of hybrid and intelligent systems.

This paper presents a comparative analysis of three outdoor wave propagation models—ITU-R P.1546-6, the SUI model, and ITU-R P.452-17—benchmarked against the deterministic Parabolic Equation Modeling (PEM) method at 3.6 GHz and 6 GHz. The evaluation focuses on prediction accuracy (RMSE, MAE, bias, relative error), terrain sensitivity, and computational efficiency. At 3.6 GHz, ITU-R P.1546-6 shows poor terrain responsiveness and high relative errors, while ITU-R P.452-17 demonstrates strong terrain sensitivity and low errors in flat areas, but decreased accuracy over hilly terrain. At 6 GHz, the SUI model consistently underestimates field strength and exhibits weak terrain sensitivity, limiting its use to rough estimations. In contrast, ITU-R P.452-17 maintains good terrain correlation and acceptable accuracy, although it slightly overestimates field strength in complex environments. The results confirm that prediction accuracy, terrain sensitivity, and bias are highly model- and frequency-dependent. ITU-R P.452-17 emerges as the most reliable and computationally efficient alternative to deterministic methods when terrain effects must be considered without significant computational overhead.