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sábado, 16 de maio de 2026
Лекции по теории цепей-Lectures on Circuit Theory-Год издания: 1991 Автор: С. И. Баскаков Жанр или тематика: Электротехника-Year of publication: 1991 Author: S.I. Baskakov Genre or subject: Electrical engineering
Lectures on Circuit Theory 30285K (download pdf) 1991 edition (follow) Added: 10.03.2024 Cover image Abstract
Year of publication: 1991
Author: S.I. Baskakov
Genre or subject: Electrical engineering Publisher: Moscow Power Engineering Institute Publishing House ISBN: 978-5-7046-0027-1
Language: Russian
Format: PDF
Description: A systematic presentation of the course material "Fundamentals" "Circuit Theory" in accordance with the modern university curriculum. Methods for analyzing stationary harmonic modes of linear circuits, the theory of four-terminal networks, the characteristics of frequency-selective circuits and filters, and the fundamentals of nonlinear circuit theory are discussed. Methods for determining the response of a linear circuit to pulsed influences are examined in detail. The theory of circuits with distributed parameters is presented. Methods for synthesizing linear two-terminal networks are discussed. A separate chapter is devoted to the use of computers for calculating complex circuits. For students majoring in radio engineering at universities.
ORIGINAL LINK: http://flibusta.site/b/775497/download
ALTERNATIVE LINK 1:https://www.mediafire.com/file/xe0kppe8zhtu4g9/Baskakov.pdf/file
ALTERNATIVE LINK 2:https://mega.nz/file/MQ1E2Cga#ZGURJ0wtbOm3inwijscEdhvyHprUlWclIDxNNvBuW-w
sexta-feira, 15 de maio de 2026
A Deep Reinforcement Learning Approach to DC-DC Power Electronic Converter Control with Practical Considerations-Nafiseh Mazaheri * , Daniel Santamargarita , Emilio Bueno , Daniel Pizarro and Santiago Cobrece
A Deep Reinforcement Learning Approach to DC-DC Power Electronic Converter Control with Practical Considerations Nafiseh Mazaheri * , Daniel Santamargarita , Emilio Bueno , Daniel Pizarro and Santiago Cobreces Department of Electronics, Alcalá University (UAH), Plaza San Diego S/N, 28801 Madrid, Spain
Abstract: In recent years, there has been a growing interest in using model-free deep reinforcement learning (DRL)-based controllers as an alternative approach to improve the dynamic behavior, efficiency, and other aspects of DC–DC power electronic converters, which are traditionally controlled based on small signal models. These conventional controllers often fail to self-adapt to various uncertainties and disturbances. This paper presents a design methodology using proximal policy optimization (PPO), a widely recognized and efficient DRL algorithm, to make near-optimal decisions for real buck converters operating in both continuous conduction mode (CCM) and discontinuous conduction mode (DCM) while handling resistive and inductive loads. Challenges associated with delays in real-time systems are identified. Key innovations include a chattering-reduction reward function, engineering of input features, and optimization of neural network architecture, which improve voltage regulation, ensure smoother operation, and optimize the computational cost of the neural network. The experimental and simulation results demonstrate the robustness and efficiency of the controller in real scenarios. The findings are believed to make significant contributions to the application of DRL controllers in real-time scenarios, providing guidelines and a starting point for designing controllers using the same method in this or other power electronic converter topologies.
ORIGINAL LINK: https://www.mdpi.com/1996-1073/17/14/3578
terça-feira, 12 de maio de 2026
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