Персона: Толоконский, Андрей Олегович
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Институт ядерной физики и технологий
Цель ИЯФиТ и стратегия развития - создание и развитие научно-образовательного центра мирового уровня в области ядерной физики и технологий, радиационного материаловедения, физики элементарных частиц, астрофизики и космофизики.
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Руководитель научной группы "Лаборатория элементов и систем автоматики, АСУТП"
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Толоконский
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Андрей Олегович
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- ПубликацияТолько метаданныеUltimate design and testing TPTS-based control systems with using full-scaled physical models of nuclear power plants(2019) Zhukov, I. M.; Tolokonsky, A. O.; Толоконский, Андрей Олегович© 2019 Published under licence by IOP Publishing Ltd. Radiation degradation rate of base current in SiGe HBTs was experimentally investigated using X-ray irradiation source with Cu anode at room and low temperatures. The dependences of base and collector current on the emitter-base voltage of the transistors were measured during radiation impact and presented for different total dose levels and irradiation conditions.
- ПубликацияТолько метаданныеAnalysis of Using of Neural Networks for Real-Time Process Control(2021) Volodin, V. S.; Tolokonskij, A. O.; Толоконский, Андрей Олегович© 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.Machine learning is one of the key technologies of the current scientific and technological revolution. Despite the fact that research in the field of “intelligent” control systems began in the last century, real-time control systems based on machine learning, specifically neural networks, began to be actively implemented only in the past decade. In this paper, the authors analyze the current state of the problem of using real-time control systems based on neural networks and using machine learning in real-time control systems.
- ПубликацияТолько метаданныеActive Disturbance Rejection Control of Nuclear Pressurized Water Reactor for Power Generation(2022) Ahmad, S.; Abdulraheem, K. K.; Tolokonsky, A. O.; Ahmed, H.; Толоконский, Андрей ОлеговичControl design for pressurized water reactor (PWR) is difficult due to associated non-linearity, modelling uncertainties and time-varying system parameters. Extended state observer (ESO) based active disturbance rejection control (ADRC) presents a simple and robust control solution which is almost model free and has few tuning parameters. However, conventional ESO suffers from noise over-amplification in the obtained estimates due to high-gain construction which in turn degrades the noise sensitivity of the closed-loop system and limits the achievable dynamic performance in practical scenarios. To overcome this problem, two recent techniques namely cascade ESO (CESO) and low-power higher-order ESO (LHESO) are implemented for control of PWR. Simulation analysis conducted in MATLAB illustrates the performance improvement obtained over conventional ESO based ADRC. Extensive simulation analysis is also conducted to investigate robustness towards parametric uncertainties. © 2022 IEEE.
- ПубликацияТолько метаданныеApplication of Machine Learning for Solving Problems of Nuclear Power Plant Operation(2022) Volodin, V. S.; Tolokonskij, A. O.; Толоконский, Андрей Олегович© 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.Nowadays, the industry is actively introducing technologies based on machine learning: predictive analytics, computer vision, industrial robots, etc. In this article authors discuss the possible application of machine learning to improve the operation of nuclear power plant (NPP) power units: diagnostics of the state of equipment (both technological equipment of normal operation systems and equipment of safety systems); definition of irrelevant alarm; determination of the state of the reactor plant; application of machine learning in equipment control algorithms. The report also examines the existing difficulties in introducing machine learning into NPP operation: issues of stability of control systems based on machine learning; the issue of interpretability of solutions issued by systems based on machine learning; small data set size for training machine learning models.
- ПубликацияТолько метаданныеAdaptive second-order sliding-mode control for a pressurized water nuclear reactor in load following operation with Xenon oscillation suppression(2022) Abdulraheem, K. K.; Tolokonsky, A. O.; Laidani, Z.; Толоконский, Андрей Олегович© 2022 Elsevier B.V.This study proposes an adaptive second-order sliding mode control based on a twisting algorithm to control nuclear reactor core power during load-following power maneuvers. The control system was designed based on the concept of an extended equivalent control. The control technique does not require knowledge of the uncertainty or upper bound of the disturbance in the system. Additionally, the gain of the control system is a dynamic gain that increases or decreases according to the system requirements within a specified period. Consequently, chattering was attenuated. Chattering excites high-frequency dynamics and causes wear out of the control mechanism, making chattering a severe challenge in sliding-mode control. A nuclear reactor is a time-varying, complex, nonlinear, and constrained system. The characteristics of a nuclear reactor are a function of its operating power level, fuel burnup, and aging. In addition, the load-following mode of operation causes Xenon oscillation, which can further cause instability in the core. Therefore, the non-base load operation of the system, including load following, further aggravates uncertainty and disturbances in the system. To this end, an adaptive second-order sliding mode control that does not require knowledge of the uncertainty or the upper bound of the disturbance in the system was designed. The reactor core was modelled using an experimentally verified and validated multi-point kinetic model with four nodes. Simulation experiments were conducted using a multi-point kinetics model and an adaptive second-order sliding mode control. The results of the simulation experiments indicated that reactor core integrity is guaranteed, and the core is protected against peak power densities, such as linear heat generation rates (LHGRs) and lower departure from nucleate boiling ratios (DNBR). Moreover, the control system achieved the load-following objective and suppressed Xenon oscillation. The performance of the proposed control system was further compared with that of a twisting control system and classical proportional integral derivative control system to validate the effectiveness and reliability of the adaptive twisting second-order sliding mode control system.
- ПубликацияТолько метаданныеReal-Time Performance Monitoring of Digital X-Ray Diagnostic Equipment Prior to Patient Admission(2024) Akhmedzyanova, M. R.; Zelikman, M. I.; Kruchinin, S. A.; Lantukh, Z. A.; Tolokonskiy, A. O.; Толоконский, Андрей Олегович