Control Sciences


The Journal addresses the needs of a wide community of specialists dealing with control in technical, organizational, social, economic, environmental, biological, and medical systems. The Journal audience also includes control and automation system engineers and developers as well as undergraduate and postgraduate researchers.

The Journal clearly and intelligibly explains the ideas and methods to solve control problems based on modern modeling approaches under a variable environment, incomplete or limited information, conflicting goals, and weakly structured and multiple objectives. These problems are commonly encountered in the real world.

The Journal focuses on systems analysis and general-system operation mechanisms of large-scale objects, theory and techniques of choice and decision-making in complex systems, expertise and data analysis, large-scale system models, applied mathematical control models of various systems, prediction techniques and models, applied models and methods of management in organizations and economy, and the problems of national and regional administration.

Compared to similar journals of the field, Control Sciences is remarkable for a uniform scope of modern control problems, an interdisciplinary approach, and an emphasis on the potential applicability of scientific results.

The Journal publishes original papers with new scientific results on theoretical control problems and applications, comprehensive state-of-the-art surveys of key scientific problems, conference reports of major interest, and brief communications.

The Journal is indexed in Russian Science Citation Index (RSCI) on the Web of Science platform and is recommended by the State Commission for Academic Degrees and Titles (VAK RF) for publishing the materials of candidate’s and doctoral dissertations on control, computer and information science.

No publication fees or payments are charged from the Journal authors.

Issued since 2003. English version issued since 2021.

Frequency: 6 issues per year.

 

 

 

 

 

Current Issue

No 6 (2024)

Cover Page

Full Issue

Surveys

Prospective Approaches to Predicting the Remaining Useful Life of Aircraft Engines
Kulida E.L., Lebedev V.G.
Abstract
This survey covers the literature on the fault diagnosis and prediction of the remaining useful life of aircraft engines based on deep learning. A formal statement of the remaining useful life estimation problem is given. The basic architectures of deep neural networks are considered to detect rare failures and predict the next failures using aircraft engine condition monitoring data. The extraction of informative features using autoencoders is discussed. The structure of long short-term memory (LSTM) and attention mechanism (AM) cells applied in deep neural networks to predict the remaining useful life is described. The problem of integrating remaining useful life prediction into maintenance planning based on reinforcement learning is considered.
Control Sciences. 2024;(6):3-19
pages 3-19 views
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Analysis and Design of Control Systems

Calculating the Spectral Entropy of a Stationary Random Process
Belov A.A., Andrianova O.G.
Abstract
The problem of calculating the spectral entropy of a stationary random process is solved. The spectral entropy (σ-entropy) of a signal is understood as a scalar value characterizing the noise color; it describes the class of signals affecting a system depending on the band under study. By assumption, the random process is defined by a shaping filter, with the Gaussian white noise with a unit covariance matrix supplied at its input, or by an autocorrelation function. The spectral entropy of the stationary random process is analytically derived using a known mathematical model of the shaping filter in the form of a log-determinant function that depends on the transfer matrix and the observability Gramian of the filter. An algorithm for calculating the σ-entropy of stationary random processes with a known autocorrelation function is proposed. The method reduces to reconstructing the mathematical model of the shaping filter using its spectral density factorization. A numerical example is provided: spectral entropy is calculated for a disturbance describing the velocity of wind gusts that affect an aircraft.
Control Sciences. 2024;(6):20-26
pages 20-26 views
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Control in Medical and Biological Systems

An Artificial Sensory Component in a Man-Machine System with Combined Feedback
Kubryak O.V., Kovalchuk S.V.
Abstract
This paper proposes a conceptual approach to constructing combined feedback in a human–machine interaction system through introducing an artificial sensory feedback component controlled by a technical subsystem. The approach is intended to systematize the role of combined feedback in the control of multi-agent systems with additional elements, humans, and artificial agents. This approach is studied for human vertical posture control and in synthetic experiments (within the CartPole model) considered using reinforcement learning as an example. The efficiency of the control problem solution is investigated by varying the characteristics of information transmission channels and the properties of the artificial sensory feedback component. According to the results, natural experiment observations are conceptually similar to those of the artificial numerical experiment in terms of additional feedback channel operation: there are a similar overshoot effect and prospects for improving control performance by tuning the artificial sensory component.
Control Sciences. 2024;(6):27-37
pages 27-37 views
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Control of Technical Systems and Industrial Processes

Adaptive Control of a Scalar Plant in the Input-Output Form Based on the Identification-Approximation Approach
Kruglov S.P.
Abstract
This paper considers a scalar plant with current parametric uncertainty in which only the input and output are measured. For such plants, an adaptive control design approach based on simplified adaptability conditions is presented. The approach refers to indirect self-tuning control using the current parametric identification algorithm and an implicit reference model. The tuned model structure in the identification algorithm is selected as simple as possible, corresponding to the main motion of the controlled plant and an elementary dynamic link or links. As a result, the current estimates in this model approximate the plant’s motion, which is confirmed by the convergence criterion of the identification residual. Also, it is required to satisfy definite requirements for the current parameter estimates. The estimates, even if imprecise, are used to construct a control law ensuring given properties of the closed-loop control system. This postulate is interpreted as a refinement of the well-known certainty equivalence principle except for the asymptotically accurate parameter estimation requirement to achieve adaptive properties of a self-tuning control system in output-feedback control problems. The main relationships are given for an example when the plant’s dominant dynamics are close to an oscillatory process without an additional time delay. The identification algorithm is applied in the form of a recurrent least-squares method with a forgetting factor and some modifications. Two illustrative examples of adaptive control system design are provided: control of the angular motion of an overhead crane and counteraction to the vibrations of an elastic three-mass drive. The approach under consideration is called the identification–approximation one. The possibilities and ways of its further improvement are outlined.
Control Sciences. 2024;(6):38-50
pages 38-50 views
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Chronicle

17th International Conference on Management of Large-Scale System Development (MLSDʹ2024)
Tsvirkun A.D., Dranko O.I., Stepanovskaya I.A.
Abstract
The 17th International Conference on Management of Large-Scale Systems Development (MLSD’2024) was held on September 24–26, 2024. This annual event is organized by the Trapeznikov Institute of Control Sciences, the Russian Academy of Sciences, with the technical support of the IEEE Russia Section. MLSD conferences are intended to discuss research in the theory and applications of computer control and management for developing large-scale manufacturing, transport, energy, financial, and social systems. The peculiarity of this year’s conference is that the models of large-scale systems are largely focused on solving current problems of strategic management. The MLSD’2024 program included one plenary session and 16 sections with 393 participants. The original proceedings of MLSD’2024 (195 papers) have been published in Russian and indexed by the National Electronic Library (eLIBRARY.RU). Of these, 169 papers have been extended and published electronically in English in IEEE Xplore (Scopus indexing).
Control Sciences. 2024;(6):51-58
pages 51-58 views
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