Control Systems, Mathematical Modeling and Automation II

A special issue of Computation (ISSN 2079-3197). This special issue belongs to the section "Computational Engineering".

Deadline for manuscript submissions: closed (30 June 2023) | Viewed by 6869

Special Issue Editors


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Faculty of Applied Mathematics, Computer Science and Mechanics, Voronezh State University, Universitetskaya Square, 1, RU-394018 Voronezh, Russia
Interests: mathematical and numerical analysis; fuzzy operators; modeling of targeted systems with fuzzy information component

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Associate Professor and Rector of Lipetsk State Technical University, Department of Automation and Computer Science, Lipetsk State Technical University, Moskovskaya str. 30, RU-398055 Lipetsk, Russia
Interests: mathematical modeling; neural networks; soft computing; interval analysis
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1. Center for Computational and Stochastic Mathematics (CEMAT-IST), University of Lisbon, Av. Rovisco Pais 1, 1049-001 Lisbon, Portugal
2. Assistant Professor, ESTG, Polytechnic of Leiria, Campus 2, Morro do Lena - Alto do Vieiro, P.O. Box 4163, 2411-901 Leiria, Portugal
Interests: applied and numerical analysis; numerical methods for differential equations; meshless (mesh-free) methods
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue will publish a set of selected papers from the 4th International Conference on Control Systems, Mathematical Modeling, Automation and Energy Efficiency (SUMMA2022), which will be held 9–11 November 2022, in Lipetsk, Russia (due to the health crisis, the conference will be held in a hybrid format). You are invited to submit a contribution to the conference for consideration in this Special Issue.

The SUMMA2022 program includes topics of interest that consist of, but are not limited to:

  • Industrial Applied Mathematics and Modeling (Mathematical Foundations of Control Theory; Control of Organizational and Socioeconomic Systems; Machine Learning; and Natural Language Processing);
  • Automation (Industrial Automation and Control Theory Applied to Technological Processes; Digitalization in Industrial, Economic and Social Systems; Metals and Mining Industry; and Transportation Systems);
  • Industrial and Commercial Power and Power Conversion Systems (Energy Systems and Power Systems Engineering; Electric Machines and Industrial Drives; and Power Electronic Devices and Components).

For detailed information on all further aspects of the conference, including the dates, keynote speakers, committees, registration, and accommodation, please check the conference website at: https://summa.stu.lipetsk.ru/.

Prof. Dr. Tatiana Ledeneva
Dr. Pavel Saraev
Dr. Svilen S. Valtchev
Guest Editors

Manuscript Submission Information

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Computation is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • control systems
  • mathematical modeling
  • automation
  • computational methods

Published Papers (5 papers)

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Research

18 pages, 703 KiB  
Article
Quadrotor Trajectory Tracking Using Model Reference Adaptive Control, Neural Network-Based Parameter Uncertainty Compensator, and Different Plant Parameterizations
by Anton Glushchenko and Konstantin Lastochkin
Computation 2023, 11(8), 163; https://doi.org/10.3390/computation11080163 - 18 Aug 2023
Cited by 2 | Viewed by 1146
Abstract
A quadrotor trajectory tracking problem is addressed via the design of a model reference adaptive control (MRAC) system. As for real-world applications, the entire quadrotor dynamics is typically unknown. To take that into account, we consider a plant model, which contains uncertain nonlinear [...] Read more.
A quadrotor trajectory tracking problem is addressed via the design of a model reference adaptive control (MRAC) system. As for real-world applications, the entire quadrotor dynamics is typically unknown. To take that into account, we consider a plant model, which contains uncertain nonlinear terms resulting from aerodynamic friction, blade flapping, and the fact that the mass and inertia moments of the quadrotor may change from their nominal values. Unlike many known studies, the explicit equations of the parameter uncertainty for the position control loop are derived in two different ways using the differential flatness approach: the control signals are (i) used and (ii) not used in the parametric uncertainty parameterization. After analysis, the neural network (NN) is chosen for both cases as a compensator of such uncertainty, and the set of NN input signals is justified for each of them. Unlike many known MRAC systems with NN for quadrotors, in this study, we use the kxx+krr baseline controller, which follows from the control system derivation, with both time-invariant (parameterization (i)) and adjustable (parameterization (ii)) parameters instead of an arbitrarily chosen non-tunable PI/PD/PID-like one. Adaptive laws are derived to adjust the parameters of NN uncertainty compensator for both parameterizations. As a result, the position controller ensures the asymptotic stability of the tracking error for both cases under the assumption of perfect attitude loop tracking, which is ensured in the system previously developed by the authors. The results of the numerical experiments support the theoretical conclusions and provide a comparison of the effectiveness of the derived parameterizations. They also allow us to make conclusions on the necessity of the baseline controller adjustment. Full article
(This article belongs to the Special Issue Control Systems, Mathematical Modeling and Automation II)
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18 pages, 1956 KiB  
Article
A Parametric Family of Triangular Norms and Conorms with an Additive Generator in the Form of an Arctangent of a Linear Fractional Function
by Tatiana Ledeneva
Computation 2023, 11(8), 155; https://doi.org/10.3390/computation11080155 - 8 Aug 2023
Viewed by 923
Abstract
At present, fuzzy modeling has established itself as an effective tool for designing and developing systems for various purposes that are used to solve problems of control, diagnostics, forecasting, and decision making. One of the most important problems is the choice and justification [...] Read more.
At present, fuzzy modeling has established itself as an effective tool for designing and developing systems for various purposes that are used to solve problems of control, diagnostics, forecasting, and decision making. One of the most important problems is the choice and justification of an appropriate functional representation of the main fuzzy operations. It is known that, in the class of rational functions, such operations can be represented by additive generators in the form of a linear fractional function, a logarithm of a linear fractional function, and an arctangent of a linear fractional function. The paper is devoted to the latter case. Restrictions on the parameters, under which the arctangent of a linear fractional function is an increasing or decreasing generator, are defined. For each case, a corresponding fuzzy operation (a triangular norm or a conorm) is constructed. The theoretical significance of the research results lies in the fact that the obtained parametric families enrich the theory of Archimedean triangular norms and conorms and provide additional opportunities for the functional representation of fuzzy operations in the framework of fuzzy modeling. In addition, in fact, we formed a scheme for study functions that can be considered additive generators and constructed the corresponding fuzzy operations. Full article
(This article belongs to the Special Issue Control Systems, Mathematical Modeling and Automation II)
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27 pages, 22430 KiB  
Article
The Problem of Effective Evacuation of the Population from Floodplains under Threat of Flooding: Algorithmic and Software Support with Shortage of Resources
by Oksana Yu. Vatyukova, Anna Yu. Klikunova, Anna A. Vasilchenko, Alexander A. Voronin, Alexander V. Khoperskov and Mikhail A. Kharitonov
Computation 2023, 11(8), 150; https://doi.org/10.3390/computation11080150 - 1 Aug 2023
Cited by 3 | Viewed by 1794
Abstract
Extreme flooding of the floodplains of large lowland rivers poses a danger to the population due to the vastness of the flooded areas. This requires the organization of safe evacuation in conditions of a shortage of temporary and transport resources due to significant [...] Read more.
Extreme flooding of the floodplains of large lowland rivers poses a danger to the population due to the vastness of the flooded areas. This requires the organization of safe evacuation in conditions of a shortage of temporary and transport resources due to significant differences in the moments of flooding of different spatial parts. We consider the case of a shortage of evacuation vehicles, in which the safe evacuation of the entire population to permanent evacuation points is impossible. Therefore, the evacuation is divided into two stages with the organization of temporary evacuation points on evacuation routes. Our goal is to develop a method for analyzing the minimum resource requirement for the safe evacuation of the population of floodplain territories based on a mathematical model of flood dynamics and minimizing the number of vehicles on a set of safe evacuation schedules. The core of the approach is a numerical hydrodynamic model in shallow water approximation. Modeling the hydrological regime of a real water body requires a multi-layer geoinformation model of the territory with layers of relief, channel structure, and social infrastructure. High-performance computing is performed on GPUs using CUDA. The optimization problem is a variant of the resource investment problem of scheduling theory with deadlines for completing work and is solved on the basis of a heuristic algorithm. We use the results of numerical simulation of floods for the Northern part of the Volga-Akhtuba floodplain to plot the dependence of the minimum number of vehicles that ensure the safe evacuation of the population. The minimum transport resources depend on the water discharge in the Volga river, the start of the evacuation, and the localization of temporary evacuation points. The developed algorithm constructs a set of safe evacuation schedules for the minimum allowable number of vehicles in various flood scenarios. The population evacuation schedules constructed for the Volga-Akhtuba floodplain can be used in practice for various vast river valleys. Full article
(This article belongs to the Special Issue Control Systems, Mathematical Modeling and Automation II)
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23 pages, 403 KiB  
Article
Hyperstability of Linear Feed-Forward Time-Invariant Systems Subject to Internal and External Point Delays and Impulsive Nonlinear Time-Varying Feedback Controls
by Manuel De la Sen
Computation 2023, 11(7), 134; https://doi.org/10.3390/computation11070134 - 7 Jul 2023
Viewed by 783
Abstract
This paper investigates the asymptotic hyperstability of a single-input–single-output closed-loop system whose controlled plant is time-invariant and possesses a strongly strictly positive real transfer function that is subject to internal and external point delays. There are, in general, two controls involved, namely, the [...] Read more.
This paper investigates the asymptotic hyperstability of a single-input–single-output closed-loop system whose controlled plant is time-invariant and possesses a strongly strictly positive real transfer function that is subject to internal and external point delays. There are, in general, two controls involved, namely, the internal one that stabilizes the system with linear state feedback independent of the delay sizes and the external one that belongs to an hyperstable class and satisfies a Popov’s-type time integral inequality. Such a class of hyperstable controllers under consideration combines, in general, a regular impulse-free part with an impulsive part. Full article
(This article belongs to the Special Issue Control Systems, Mathematical Modeling and Automation II)
18 pages, 14052 KiB  
Article
Developing a Numerical Method of Risk Management Taking into Account the Decision-Maker’s Subjective Attitude towards Multifactorial Risks
by Aleksandr Alekseev, Zhanna Mingaleva, Irina Alekseeva, Elena Lobova, Alexander Oksman and Alexander Mitrofanov
Computation 2023, 11(7), 132; https://doi.org/10.3390/computation11070132 - 5 Jul 2023
Viewed by 1521
Abstract
Risk involves identifying several options that the decision-maker can opt for while making a choice either in the direction of risk or reliability. In this approach, risk is defined as the action of the subject which will lead to the loss or guaranteed [...] Read more.
Risk involves identifying several options that the decision-maker can opt for while making a choice either in the direction of risk or reliability. In this approach, risk is defined as the action of the subject which will lead to the loss or guaranteed safety of what has been achieved. As the uncertainty of the external business environment increases for companies, the task of managing risks both individually and as a set of risks becomes more and more relevant. The purpose of this study is to solve the problem of managing multifactorial risks using mathematical methods for determining the optimal risk management trajectories separately for each factor. To determine the optimal risk management trajectories for each factor, a numerical method is used based on the choice of the most effective direction, which is defined as the ratio of risk change to cost change. An information system prototype has been created that can support the management of a set of risks. Approbation of the information system was carried out on an example containing two conceptual risk factors. The proposed prototype builds a three-dimensional risk map by interpolating the risk matrix entered by the risk manager using an additive–multiplicative aggregation procedure, as well as optimal risk management trajectories for all entered risk factors. Full article
(This article belongs to the Special Issue Control Systems, Mathematical Modeling and Automation II)
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