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• ALGORITHMS OF ELECTRIC NETWORK CONTROL OF A HYBRID POWER SUPPLY SYSTEM OF AUV

  N.K. Kiselev, L.A. Martynova

  2022-03-02

  Abstract ▼

  The aim of the research was to control the electrical network of a hybrid power supply system
  for an autonomous underwater vehicle designed to travel over ultra-long distances over tens
  of thousands of kilometers. To overcome ultra-long distances, the urgent task is to minimize the
  specific consumption of electricity, provided that all consumers are provided with electricity. The
  relevance of the work is determined by the novelty of using a hybrid power supply system in autonomous
  unmanned underwater vehicles, consisting of heterogeneous sources of electricity operating
  on different physical principles. Due to the lack of research to date, related to the control of
  the hybrid power supply system, coordinated with the modes of motion of the vehicle in a wide range of speeds, the problem arose of developing control algorithms for the hybrid power supply
  system. To solve the problem, the reasons for the change in current consumption during the
  movement of the device were analyzed, the necessary conditions for connecting consumers to the
  bus ducts were formed, including providing all consumers with electricity in full, excluding the
  excess of the rated currents of each bus duct with consumption currents, minimizing electricity
  losses when passing through the conductor and through the equipment. In this regard, the possible
  configurations of the construction of the electrical network using conductors and equipment were
  analyzed, and losses on the current conductors and on the equipment used were estimated. Based
  on the results of the research, a graph of consumers' connections to the conductors was formed,
  and to determine the way of connecting each consumer to the energy source through the power
  grid, a connection path was determined that minimizes losses. The problem was formalized as
  finding the shortest path in a graph, and Dijkstra's algorithm was used as a basis to solve it. Based
  on the research results, algorithms were formed for the formation of ways to connect consumers to
  electricity sources through the power grid and an algorithm for controlling the switching of keys
  in the power grid when the consumption currents change. The developed algorithms were implemented
  in software, and a numerical experiment was carried out using a simulation model. The
  results of the experiment showed the correctness of the developed algorithms, and can be further
  used for implementation in the devices under development for moving over ultra-long distances.

• ORGANIZATION OF THE ELECTRIC NETWORK OF THE HYBRID POWER SUPPLY SYSTEM OF AUTONOMOUS UNDERWATER VEHICLE

  N.K. Kiselev, L.A. Martynova, I.V. Pashkevich

  2021-04-04

  Abstract ▼

  The aim of the study is to organize the power grid of a hybrid power supply system for an autonomous
  underwater vehicle capable of moving in a wide range of speeds. The need to move the
  autonomous underwater vehicle in a wide range of speeds requires the use of heterogeneous sources
  of electricity operating on different physical principles - storage batteries and electrochemical generators
  using reagents from the reagent storage. In addition, in order to provide consumers with
  electricity with the required parameters (currents, voltages, volumes of electricity), it is necessary to
  use additional switchboards, voltage converters, protective switching equipment, keys. The use of
  additional equipment in the power grid allows you to flexibly configure the power grid in order to
  generate energy in an amount consistent with the amount of electricity consumed. On the other hand,
  additional equipment causes losses of electricity in the network, and, accordingly, additional electricity.
  In this regard, the task of determining the option for organizing the power grid, at which the loss
  of electricity would be minimal, is relevant. To solve this problem, the features of the use of additional
  equipment in the power grid were analyzed, the consumption of electricity by an autonomous underwater
  vehicle at different stages of a route assignment was analyzed, the minimum and maximum
  volumes of consumption were determined when an autonomous underwater vehicle moved in different
  speed modes. This made it possible to determine the degree of involvement of heterogeneous
  sources of electricity in the process of performing a route assignment. Based on the results of the
  analysis, alternative options for the power grid were formed. To select the option of the organization
  that ensures the minimum losses of electricity, a target graph of the effect of losses on individual
  devices of the power grid was formed - on the losses of the entire power grid, and using the method of
  distributing tags, quantitative estimates of each of the alternative options were obtained. Teaching
  quantitative assessments made it possible to determine the option of organizing an electrical network
  that minimizes losses. This allows, in turn, to formulate the requirements for the functioning of the
  elements of the hybrid power supply system, to develop control algorithms. In general, the result
  obtained makes it possible to minimize the consumption of energy resources during the movement of
  an autonomous underwater vehicle throughout the entire duration of the route assignment.

• METHOD OF AUTOMATIC OPTIMIZATION OF THE FUZZY RULE BASE OF AN INTELLIGENT CONTROLLER BASED ON SUBTRACTIVE CLUSTERING

  А.S. Ignatyeva , V.V. Shadrina , D.S. Ignatyev , А.V. Maksimov

  181-197

  2025-07-24

  Abstract ▼

  The aim of the work is to develop a method for optimizing the fuzzy rule base of an intelligent controller for controlling a technical object using subtractive clustering. The article provides an overview and a brief analysis of the state of affairs in the field of optimizing the operation of intelligent control systems. To achieve the goal of the study, a hybrid model has been developed in which the technical object is controlled using a classical PI controller and a fuzzy PI controller with a generated structure of a Cygeno-type fuzzy inference system and a developed model of an adaptive neuro-fuzzy inference system. This configuration of the model allows you to form a fuzzy rule base that does not depend on the expert's knowledge in the subject area. The article proposes a new method for optimizing the fuzzy controller rule base based on clustering methods, in particular subtractive clustering, which allows you to reduce the number of fuzzy logical inference rules and increase the performance of the technical object control system. First, a hybrid model synthesized on the basis of the values of the fuzzy and classical controllers before applying subtractive clustering was simulated. The application of subtractive clustering according to the method developed in the study for the values of the classical and fuzzy controllers allowed us to achieve their quantitative reduction by 1.7 and 5.25 times, respectively. Then, the hybrid model synthesized on the basis of the values of the fuzzy and classical controllers after applying subtractive clustering was simulated. The results obtained in the process of simulation showed high efficiency of the proposed method for optimizing the fuzzy controller rule base. Due to the application of subtractive clustering in the hybrid model for the intelligent controller, it was possible to significantly reduce the number of membership functions required to describe the input linguistic variables (from five to four) and reduce the number of fuzzy logical inference rules (from twenty-five to sixteen). The analysis of the resulting graphs of transient processes obtained for the hybrid models before and after applying subtractive clustering showed that the main indicators of the quality of the control process remain unchanged with a significant reduction in the calculations performed.

• A TIME SERIES FORECASTING METHOD BASED ON COGNITIVE FUZZY MODELING AND REGRESSION ANALYSIS

  А.I. Guseva , R.М. Romanov

  157-178

  2025-12-30

  Abstract ▼

  The relevance of the study stems from the low effectiveness of traditional time series forecasting methods under conditions of high uncertainty and limited data, which are typical of weakly formalized systems. The aim of the work is to develop and substantiate a time series forecasting method based on a hybrid approach that integrates cognitive fuzzy modelling, regression analysis, and the analytic network process. Within the study, a systematic review and comparative analysis of existing forecasting methods was carried out, including approaches based on fuzzy logic, neural network and cognitive modelling, as well as ensemble and hybrid methods, and their limitations were identified when dealing with small samples, nonlinear dependencies, and uncertainty. The proposed method includes: the construction of fuzzy cognitive maps, defuzzification of linguistic assessments, clustering of factors, application of the analytic network process to determine priorities, and the formation of a weighted regression model. The model undergoes statistical validation using the , , , and  metrics, as well as diagnostic checks of the assumptions underlying regression analysis, including tests for multicollinearity and autocorrelation. Application of the method reduced  from 0.38 to 0.22,  from 0.30 to 0.18, and  from 11.65 % to 7.12 %, thereby confirming an improvement in the accuracy and robustness of forecasts under limited data compared with classical multiple regression. The novelty of the proposed method lies in the integration of cognitive modelling, regression analysis, and the analytic network process, whereby the strengths of each component compensate for their individual limitations, providing more accurate and robust forecasting under the uncertainty inherent in the system under study. The practical significance of the work consists in the possibility of applying the proposed method to support decision-making and to enhance the validity of forecasts in various subject domains and situations characterized by a limited number of observations, a substantial role of expert judgments, and a complex structure of causal relationships between indicators over time

• ANALYSIS OF TRADITIONAL AND NEURAL NETWORK-BASED CONTROL METHODS FOR ELECTRIC DRIVES IN ROBOTICS AND PERSPECTIVES OF HYBRID APPROACHES

  А. I. Tataurov , V.Е. Vavilov

  287-298

  2025-12-30

  Abstract ▼

  The objective of this study is to conduct a comparative analysis of traditional and neural network-based control methods for electric drives in robotics, with an emphasis on identifying their strengths and weaknesses, determining their areas of application, and assessing the prospects for the development of hybrid approaches. Effective control of electric drives is critically important for modern robotic systems, which must demonstrate high performance, reliability, and versatility in various application domains. Specifically, key challenges include high-precision trajectory tracking, energy-efficient control, robust control under uncertainties and disturbances, constraint-aware control, as well as synchronized and coordinated control of multiple electric drives. In this regard, optimizing the control of electric drives to ensure motion accuracy, energy efficiency, and adaptation to changing conditions becomes a top priority. To achieve this goal, the study systematizes and analyzes the characteristics and applications of traditional electric drive control methods, such as PID controllers, Kalman filters, sliding mode control, and model predictive control. It also examines key neural network-based approaches to electric drive control, including feedforward neural networks, recurrent neural networks, radial basis functions, neuro-fuzzy systems, and reinforcement learning. A comparative analysis of these methods is conducted to identify their advantages and limitations based on key parameters such as trajectory tracking accuracy, robustness to disturbances and uncertainties, adaptability to changing operating conditions, and computational complexity. Additionally, the study investigates and assesses the prospects for hybrid electric drive control methods that combine the reliability and control quality of traditional methods in linear and structured environments with the flexibility and adaptability of neural network-based methods in complex and dynamic robotic systems. The study’s key findings indicate that traditional electric drive control methods, such as PID controllers and sliding mode control, remain effective and preferable in linear and well-defined systems due to their simplicity and reliability. At the same time, neural network-based approaches demonstrate significant advantages in controlling complex nonlinear systems, as well as in uncertain conditions requiring adaptation to changing environments. Special attention is given to hybrid control methods, which integrate the strengths of both traditional and neural network-based approaches. These methods are regarded as the most promising and advanced direction, enabling the development of intelligent and robust electric drive control systems capable of operating efficiently in complex and dynamic environments.