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• RESEARCH OF AN INTELLIGENT ADAPTIVE CONTROL ALGORITHM BASED ON THE REINFORCEMENT LEARNING METHOD

  А. N. Karapeev, Е.Y. Kosenko, М. Y. Medvedev, V. K. Pshikhopov

  2025-04-27

  Abstract ▼

  An algorithm for adaptive control of a DC motor based on the use of machine learning technology
  with reinforcement is proposed and investigated. An overview and brief analysis of the state of affairs in
  the field of intelligent motor control systems is given. A mathematical model of the DC motor is presented,
  and a structural scheme for training an intellectual agent is presented. An intelligent adaptive motor speed
  control system is proposed. The DC motor is represented as a black box with the limited input and output.
  The control system is based on a zero-order Q-learning algorithm. It is assumed that the output of the
  intelligent agent is a control applied to the motor input. The intelligent system uses a tabular approximation
  of the value of each of the control action. In this article, we study the effect of the discreteness of the
  representation of state, the set of control effects used, the applied rewards, and the parameters of the
  learning algorithm on the control error. The sensitivity of the control system to the parameters of the motor
  and an unmeasured moment is investigated. Based on the results of the study, a modified algorithm is
  proposed, which assumes the measurement or evaluation of the current of the motor stator. The control
  algorithm provides robustness to parameters and external disturbance. Additionally, the approximation of
  the control value function using polynomials and using a neural network are investigated

• CONCEPTUAL MODEL OF FACTORS INFLUENCING THE EFFICIENCY OF GAS PREPARATION AND SEPARATION PROCESS

  А. V. Martirosyan , D. V. Romashin

  241-249

  2026-09-10

  Abstract ▼

  The paper presents the concept of adaptive control in natural gas separation. The control systems used in practice are usually based on fixed control algorithms and do not consider dynamic changes in physical, technical and operational parameters, which leads to a decrease in separation quality and an increase in energy consumption. The article describes the approach of combining system analysis, modeling and the Pareto method. This paper presents a concept for adaptive control in natural gas separation. Control systems used in practice are typically based on fixed control algorithms and do not account for dynamic changes in physical, technical, and operational parameters, which leads to reduced separation quality and increased energy costs. This paper describes an approach combining systems analysis and the Pareto method. The aim of this paper is to develop a concept for adaptive control of the natural gas treatment process based on systems analysis. Recent studies demonstrate that data-driven methods enable more precise parameter adjustment, better responsiveness to raw-gas fluctuations and improved impurity removal efficiency. To achieve this goal, this paper addresses the challenges of identifying and classifying factors affecting the quality and efficiency of gas separation, as well as integrating their relationships within a unified conceptual control model. Particular attention is paid to the impact of precise control of key parameters, such as pressure, temperature, and flow rate, on the efficiency of these processes. An analysis of recent research demonstrates the growing use of neural networks and machine learning models in gas purification for predictive control, anomaly detection, and optimization of operating parameters. A comparative evaluation of classic PID controllers, fuzzy, adaptive, and neural control methods confirms the advantages of intelligent control in terms of stability, adaptability, and energy efficiency. The main result of this study is the substantiation of key factors determining separation efficiency, among which pressure, temperature, and gas flow rate have the greatest impact. The resulting model forms a methodological basis for the development of intelligent and adaptive control systems for gas purification processes.

• REINFORCEMENT LEARNING METHODS IN ADAPTIVE CONTROL OF NONLINEAR DYNAMIC OBJECTS

  М.Y. Medvedev , V.K., А.R. Gaiduk , I.М. Medvedev , Е.Y. Kosenko

  2026-04-29

  Abstract ▼

  The relevance of the problem of adaptive control of nonlinear dynamic objects is ensured by the ever-increasing demands on the quality and operating conditions of technical systems. Automation and robotics lead to an increase in the complexity of the problems being solved and the need to adapt to structural and parametric uncertainty and external disturbances. In recent years, the use of reinforcement learning methods for the synthesis of adaptive control systems has gained popularity. These methods demonstrate effectiveness in controlling uncertain dynamic objects. However, there are two fundamental problems with the application of machine learning methods to control systems for dynamic objects. First, the need to ensure asymptotic stability of the desired trajectory of a closed-loop system limits the application of deep learning methods. Second, during the learning process, it is necessary that the intelligent controller does not generate controls that lead to state variables exceeding specified limits. The purpose of this article is to review and analyze recent advances in the application of reinforcement learning methods to the synthesis of adaptive control systems for nonlinear dynamic objects. Particular attention is given to Actor-Critic methods, which are structurally similar to adaptive control systems with self-adjusting parameters. Based on the analysis, the structure of an adaptive system with two-component control, including nominal and adaptive controllers, is proposed. An adaptive control algorithm is proposed, distinguished by the use of a modified Actor-Critic algorithm, distinguished by a new form of the delta error and the absence of a singularity in the neighborhood of zero. The proposed algorithm allows for a reduction in the number of adjustable parameters. The article presents conditions for Lyapunov stability of the zero-equilibrium position of a closed-loop system and an example of the synthesis and modeling of the proposed adaptive control algorithm

• THE FORMALIZED APPROACH TO SYNTHESIS OF ARCHITECTURE IN THE SYSTEM OF ADAPTIVE GROUP CONTROL OF ROBOTIC COMPLEXES IN THE CONDITIONS OF THE NONDETERMINISTIC DYNAMIC ENVIRONMENT

  V.V. Sviridov

  2022-05-26

  Abstract ▼

  The rapid development of "multi-agent systems" as an independent and multifaceted section
  of artificial intelligence attracts many researchers in various fields of activity. The pace of progress
  in the development of information technologies, distributed information systems, and computer
  technology determines the possibilities of using robotics technologies in the Armed Forces of
  the Russian Federation. The factors presented in the article authorize the need to introduce new
  intelligent technologies into the troops - autonomous robotic complexes (systems). The development
  of artificial intelligence methods makes it possible to take a new step towards changing the
  style of interaction of complexes with each other as part of a robotic system. The idea of creating
  so-called "autonomous complexes" arose, which gave rise to a new style of adaptive group management.
  Instead of interaction initiated by the user-operator through commands and direct manipulations,
  complexes are independently involved in the joint process of solving a common problem
  in a non-deterministic dynamic environment. The article proposes a formalized approach to
  the design of architectures for group interaction of autonomous robotic complexes in a system
  based on the law of open control, i.e. induced and reliable preferences of each complex for action,
  satisfying the conditions of perfect coordination of their activities, by identifying parameters at
  which the objective function is maximized in various modes of functioning of the robotic system. A
  formalized formulation of the problem of synthesis of the adaptive group control system of autonomous
  robotic complexes under conditions of a priori uncertainty is presented. The architecture of
  group interaction of complexes is adaptively built based on the conditions of the external environment
  and the internal state of the system, in which each complex of the group functions to achieve
  a common goal (solving a system problem) at the time under consideration.