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• ALGORITHM FOR SELECTING THE OPTIMAL OPERATING FREQUENCY AND TRANSMISSION PARAMETERS IN THE HF CHANNEL BASED ON PREDICTION USING GNSS MONITORING DATA

  А. D. Skorik

  120-130

  2026-09-10

  Abstract ▼

  Relevance. Modern requirements for specialized automated control systems and advances in communication technology are reviving interest in the HF band as a promising means of controlling ground, sea, and air-based robotic complexes (RCs). Existing adaptation algorithms for HF communication systems, by national and international standards are reactive and do not take into account the frequency dependence of ionospheric parameters, which limits their effectiveness for reliable RC control. Objective. To develop an algorithm for selecting the optimal operating frequency and transmission parameters (bandwidth, data rate, power, interleaving depth) based on prediction of frequency-dependent characteristics of a diffuse HF channel using passive GNSS monitoring data. Methods. A structural multipath model for correlation intervals, the ITU-R method for the mean signal-to-interference ratio, and analytical expressions for the Nakagami parameter  and the permissible signal-to-interference ratio taking into account diversity reception are used. Results. An algorithm has been developed that includes estimating ionospheric parameters from GNSS data, calculating the frequency-dependent characteristics of the channel, and selecting the optimal operating mode that maximizes the energy margin and communication reliability. Numerical simulation for a typical path confirmed its efficiency. Conclusions. The proposed algorithm provides proactive selection of communication parameters taking into account the real state of the ionosphere, increasing reliability and data rate, which is critically important for RC control tasks. It is a step towards the creation of cognitive HF systems.

• IMPACT OF SAMPLING TECHNIQUES ON EXPLORATORY LANDSCAPE ANALYSIS

  М.V. Pikalov

  2026-02-27

  Abstract ▼

  Exploratory Landscape Analysis (ELA) features are numerical descriptors of a problem's fitness landscape, often used to recommend optimal algorithm parameters. This study investigates the critical impact of sampling methods and sample size on the approximation of ELA features and the subsequent performance of machine learning models. The research demonstrates that these feature approximations are not absolute characteristics of the landscape but are significantly influenced by the method used to generate the sample points. While increasing the sample size reduces the variance of feature estimates, the choice of sampling strategy itself introduces substantial bias, leading to statistically different feature values across methods like Mersenne Twister, Latin Hypercube Sampling (LHS), and Faure sequences. The core experiment involved predicting the parameters of the tunable W-model problem using regression models trained on ELA features. The results showed that models trained and tested on data from the same sampling method performed best, highlighting a lack of interoperability between different sampling techniques. Notably, the Faure quasirandom sequences consistently yielded the lowest regression error, outperforming common methods like uniform random sampling and LHS. Furthermore, cross-sampling validation revealed that models, especially those trained on Faure sequences, suffered a significant performance drop when tested on data from any other method, confirming that the sampling strategy imparts a specific "fingerprint" on the feature data. In conclusion, the findings challenge the default use of common sampling methods in ELA. The accuracy of machine learning models for algorithm selection and configuration is highly sensitive to the sampling strategy employed for feature extraction. Therefore, ensuring consistency between the sampling methods used during model training and application is crucial. The superior performance of Faure sequences suggests that low-discrepancy sequences are a promising avenue for future research in making ELA-based models more robust and accurate

• THE MODULE FOR PREDICTING CONVERTER PARAMETERS BASED ON SPECIFIED AMPLITUDE-FREQUENCY CHARACTERISTICS

  V.I. Shlaev

  93-103

  2025-11-10

  Abstract ▼

  The article discusses the solution of the problem of developing converters based on specified amplitude-frequency characteristics. The main problem is to carry out a large number of measuring measures with changes in the parameters of the transducers to achieve the necessary amplitude-frequency characteristics, which leads to high time and resource costs for development. The analysis of the main parameters of the converters affecting the specified amplitude-frequency characteristics is carried out. The existing approaches, methods and algorithms for creating converters of the required characteristics are analyzed. The development of a module for predicting the parameters of electromechanical converters based on specified amplitude-frequency characteristics is described. The research objectives include the creation of structural-parametric and mathematical models for calculating the characteristics of converters at the design stage. An algorithm for training a model based on experimental data obtained during measurements is described. The use of machine learning methods to predict parameters minimizes the number of experiments performed and reduces the cost of developing converters. The proposed approach is based on the use of the relationship between the design parameters of the converters and their frequency characteristics. The gradient boosting algorithm is used to increase the accuracy of forecasting. The stages of data preparation for model training are presented. The learning process of the model is described. The results demonstrate a significant reduction in the modeling time of the converters: the use of the module makes it possible to speed up the process several times compared with the experimental approach. Predicting characteristics based on a model provides comparable accuracy with a larger amount of data. The findings of the study confirm the effectiveness of the proposed approach in the development of converters, reducing time and financial costs, increasing the accuracy of modeling and applicability in conditions of limited resources.

• A METHOD FOR EXPRESS ASSESSMENT OF PI-REGULATOR PARAMETERS FOR APERIODIC TRANSIENT PROCESSES IN AUTOMATIC CONTROL SYSTEMS OF NUCLEAR POWER PLANT UNITS

  А.О. Tolokonsky , D.S. Menyuk

  64-71

  2025-11-10

  Abstract ▼

  This article discusses the key aspects of setting the parameters of automatic regulators that are used in process control systems, in particular at nuclear power plants (NPP). The need for fine-tuning regulators is emphasized to ensure the stability, efficiency and safety of the systems. Traditional tuning methods such as the Ziegler-Nichols method and frequency analysis are described, which, despite their reliability, require significant time and an accurate mathematical model of the control object. In modern production conditions, where efficiency is important, express methods are relevant to reduce setup time, but their accuracy and versatility remain questionable. Special attention is paid to the problems that arise when using real regulators, such as integral saturation and periodic invocation of the control algorithm. Integral saturation can lead to a deterioration in the dynamic characteristics of the system and even to the activation of technological protections, and an incorrect choice of the period for calling the regulator can cause a loss of stability of the system. Methods A method for tuning PI controllers is proposed that takes into account the dynamic characteristics of control objects and the results of experimental studies. Recommendations are given on the choice of proportionality coefficients and the integration time constant, which make it possible to achieve an aperiodic transition process, minimize the risk of saturation and ensure high quality control. Results The results of experiments conducted on the UMICON software and hardware complex confirmed the effectiveness of the proposed approach. Conclusion. The developed rules for rapid evaluation of regulator parameters make it possible to simplify the setup process, reduce setup time, and improve the reliability of automatic control systems at nuclear power plants. This is especially important to ensure the safety and stability of such critical facilities as nuclear power plants.

• OPTIMIZATION OF PID PARAMETERS OF SERVO SYSTEMS USING A GENETIC ALGORITHM AND A NEURAL NETWORK CLASSIFIER

  Ahmad Zoualfikar , Y.А. Kravchenko , А.М. Mansour

  237-250

  2025-10-01

  Abstract ▼

  Machine learning algorithms play a vital role in enhancing the performance of industrial systems, providing high precision and operational efficiency in real time. In servo motor control systems, these algorithms help reduce noise and vibration, improving efficiency and extending equipment lifespan. This article examines various types of noise that occur and their negative impact on industrial processes. The primary research objective is to optimize PID controller parameters in servo systems using a combined algorithm that combines neural networks and genetic algorithms. Unlike traditional methods such as genetic algorithms (GA) and particle swarm optimization (PSO), which suffer from slow convergence and risk of motor damage, the proposed solution is based on a control software platform. This platform ensures safe real-time interaction with the servo motor. A CAN Bus-based control system has been developed that enables developers to: read all servo motor parameters (speed, current, voltage, encoder position); modify PID coefficients with a single click, eliminating the need for manual tuning as in MOTO-MASTER. The implementation of the developed control system allowed the use of a trained neural classifier to constrain PID parameters within safe limits, reducing search space and accelerating the optimization process. Experimental results on SPH-S servo motors demonstrated significant reduction in noise and mechanical vibrations during real-time operation while maintaining stability across a wide speed range (0-1500 rpm).

• FEATURES OF THE FORMATION OF THE PROCESS OF CLASSIFYING THE CONDITION OF A TECHNICAL FACILITY BASED ON THE ANALYSIS OF POINTS IN THE TIME SERIES OF THE PARAMETER

  S.I. Klevtsov

  47-57

  2025-10-01

  Abstract ▼

  Assessment of the operability of a technical facility in real time is important for the stable and trouble-free operation of the facility during its operation. Previously, a classification model for the rate of parameter change was proposed based on specialized point cloud processing of a time series segment without trend extraction. However, some proposals, for example, related to the non-inclusion of some points of the series in the model construction procedure, were not sufficiently justified and are an unobvious attempt to get rid of abnormal values of the time series. Some stages of the model implementation, for example, building an ellipse on a transformed point cloud, require a detailed representation, which is important for further model training and classification.  In the article, as part of the preliminary data preparation, a procedure is proposed for detecting and screening out abnormal values of the time series of a parameter based on a modification of the Irwin method. In addition, an updated scheme for evaluating the values of the criterion in the classification model for the condition of a technical facility parameter is presented. The ellipse compression ratio is used as the evaluation criterion, which is based on a cloud of scatter plot points cut out by a sliding time window from the time series of the parameter. An iterative ellipse construction procedure has been developed for this purpose. The new procedure provides a more informed and accurate assessment of the criterion. Thus, a modified model has been built that will allow real-time assessment of the occurrence of an emergency situation at an early stage of its development.
  The evaluation procedure can be implemented as part of the hardware and software of the monitoring system of a technical facility