Andriy Malyar

This paper presents a comprehensive approach to modeling wind disturbances and improving the robustness of control systems for multicopter-type unmanned aerial vehicles (UAV). Since multicopters are underactuated nonlinear systems, their stability depends to a large extent on the correct consideration of the stochastic nature of wind, aerodynamic uncertainties, and the impossibility of direct measuring the external influences. The review of deterministic, stochastic, and Computational Fluid Dynamics (CFD) wind models was conducted, and their suitability for the tasks of UAV state estimation and stability analysis was determined. Particular attention is paid to the Dryden spectral model, gust models, and wind shear profiles as key elements for constructing realistic scenarios of atmospheric disturbances. A comprehensive control system model is proposed combining Model Predictive Control (MPC), an Uncentered Kalman Filter (UKF), an Extended Disturbance Observer (EDOB), and a Physics-Based Machine Learning (Physics-ML) approach for estimating aerodynamic drag and relative airspeed. This integration enables coordinated realtime prediction, estimation, and compensation of total disturbances, significantly improving flight accuracy and stability under conditions of turbulence, gusts, and partial model uncertainty. The results confirm that the integrated combination of wind models and modern control methods provides an effective framework for enhancing the robustness of multicopters in challenging atmospheric conditions. The proposed methodology can serve as a foundation for the further development of autonomous control systems and high-precision simulation tools in both research and applied applications.

This study aims to identify an effective neural network architecture for the task of semantic segmentation of the surface of beer wort at the stage of primary fermentation, using deep learning methodologies. Four contemporary architectures were evaluated and contrasted. The following networks are presented in both baseline and optimized forms: U-Net, DeepLabV3+, Fast-SCNN, and SegNet. The models were trained on a dataset of images depicting real beer surfaces at the primary fermentation stage. This was followed by the validation of the models using key metrics, including pixel classification accuracy, Mean Intersection over Union (mIoU), Dice Coefficient, inference time per image, and Graphics Processing Unit (GPU) resource utilization. Results indicate that the optimized U-Net achieved the optimal balance between performance and efficiency, attaining a validation accuracy of 88.85%, mIoU of 76.72%, and a Dice score of 86.71%. With an inference time of 49.5 milliseconds per image, coupled with minimal GPU utilization (18%), the model proves suitable for real-time deployment in production environments. Conversely, complex architectures, such as DeepLabV3+, did not yield the anticipated benefits, thereby underscoring the viability of utilizing compact models for highly specialized industrial tasks. This study establishes a novel quantitative metric for the assessment of fermentation. This is based on the characteristics of the foam surface and thus offers an objective alternative to traditional subjective inspections. The findings emphasize the potential of adapting optimized deep learning architectures to quality control tasks within the food industry, particularly in the brewing sector, and they pave the way for further integration into automated computer vision systems.

The object of the study are the processes occurring in electric drives of sucker rod pumping units, which are used for mechanical oil production. When designing oil pumping units, one of the issues is the synthesis of control systems for the electric drives of pump jacks. In the synthesis of the control system, it is important to take into account as many factors as possible that influence the performance of the well and its equipment. When synthesizing the criterion for optimal operation of the system, it is necessary to take into account changes in the technical condition of the oil production facility and the conditions of the reservoir operation. This is implemented using a control system that adjusts the electric drive speed according to the real operating conditions of the well. This approach allows improving the productivity of the oil pumping unit and increasing its economic efficiency. The main focus is on the synthesis of a control system for the electric drive of a pump jack, which makes it possible to balance the intensity of fluid inflow into the well and its withdrawal. An optimization criterion is proposed, which takes into account different fluid inflow rates, thus adapting the electric drive operation to the specific conditions of each well. The study used the theory of classical variational calculus. To find the minimum of the functional, and therefore the optimal parameters of the control system, a quadratic form, which is a Lyapunov function, was used. The study resulted in the formulae for calculating the key operational parameters of the pump jack electric drive. The synthesized optimization criterion links the unit's productivity to parameters such as the number of crankshaft rotations, the rod pump delivery coefficient, and the load torque of the drive motor. In practice, the proposed optimization criterion for the electric drive operation will enable improving the efficiency of the oil pumping unit through the rational selection of the drive motor's speed and torque. This, in turn, will enhance the well's operating conditions and extend its operational life.

The uneven load of the electric drive of sucker-rod pumping units causes an increase in the consumption of reactive power, which requires compensation. This article discusses the issue of calculating the processes in sucker-rod pumping units in the case of individual compensation of the reactive power. Mathematical models for calculating the process of changing the capacity of cosine capacitors for reactive power compensation in the starting and steady-state modes of the electric drive of the sucker-rod pumping unit, which are characterized by the periodically varying load and moment of inertia, have been developed. The calculation is based on a mathematical model of the induction motor that takes into account the saturation of the magnetic path and the skin effect in the bars of the deep-bar rotor. The created mathematical model can be used to regulate the capacitance of the capacitors connected to the induction motor in order to reduce the reactive power consumption.

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МЕТОД РОЗРАХУНКУ МЕХАНІЧНИХ ХАРАКТЕРИСТИК АСИНХРОННИХ ДВИГУНІВ З КОРОТКОЗАМКНЕНИМ РОТОРОМ

На основі розробленого методу розрахунку усталених асинхронних режимів явнополюсних синхронних двигунів запропоновано методику математичного моделювання пускових характеристик. Задача розрахунку стаціонарного асинхронного режиму розв’язується як крайова для диференціальних рівнянь електричної рівноваги контурів двигуна. Алгебризація системи диференціальних рівнянь здійснюється шляхом апроксимації рівнянь стану кубічними сплайн-функціями на сітці вузлів періоду з урахуванням періодичного закону зміни координат. В результаті здійснюється перехід від їх континуальних значень до вузлових. Розрахунок пускових статичних характеристик здійснюється методом продовження по параметру. Для дослідження впливу величини пускового опору на асинхронні характеристики двигуна використано математичну модель двигуна, в якій враховуються реальні контури пускової обмотки, насичення та несиметрія магнітопроводу.

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