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检索条件"任意字段=IEEE Data Driven Control and Learning Systems Conference"
29323 条 记 录,以下是921-930 订阅
Cooperative control of Power Grid Frequency Based on Expert-Guided Deep Deterministic Policy Gradient Algorithm
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ieee ACCESS 2025年 13卷 38502-38514页
作者: Shen, Tao Zhang, Jing He, Yu Yang, Shengsun Zhang, Demu Yang, Zhaorui Guizhou Univ Sch Elect Engn Guiyang 550025 Peoples R China
In power systems, frequency control is essential to ensure stable grid operation. With the increasing share of renewable energy, frequency fluctuations have become more complex and unpredictable. To address this chall... 详细信息
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A Machine learning Classifier for Detection of Performance Issues in Industrial Closed-Loop PID controllers  10
A Machine Learning Classifier for Detection of Performance I...
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10th International conference on control, Decision and Information Technologies (CoDIT)
作者: Yagci, Mehmet Forsman, Krister Boling, Jari M. Neste Oyj Turku Finland Abo Akad Univ Lab Proc & Syst Engn Turku Finland Perstorp AB Perstorp Sweden NTNU Dept Chem Engn Throndheim Norway Univ Turku Dept Mech & Mat Engn Turku Finland
The tight production objectives and dynamically evolving conditions within industries necessitate the diligent monitoring and evaluation of control assets' operational health. During the past decades, a lot of dat... 详细信息
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Sample-Efficient learning for a Surrogate Model of Three-Phase Distribution System
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ieee TRANSACTIONS ON POWER systems 2024年 第1期39卷 2361-2364页
作者: Nguyen, Hoang Tien Kim, Young-Jin Choi, Dae-Hyun Chung Ang Univ Sch Elect & Elect Engn Seoul 156756 South Korea Pohang Univ Sci & Technol Dept Elect Engn Pohang 790784 South Korea
A surrogate model that accurately predicts distribution system voltages is crucial for reliable smart grid planning and operation. This letter proposes a fixed-point data-driven surrogate modeling method that employs ... 详细信息
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A Fortunate Decision That You Can Trust: Digital Twins as Enablers for the Next Generation of Energy Management systems and Sophisticated Operator Assistance systems
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ieee POWER & ENERGY MAGAZINE 2024年 第1期22卷 24-34页
作者: Brosinsky, Christoph Naglic, Matija Lehnhoff, Sebastian Krebs, Rainer Westermann, Dirk TEN Thueringer Energienetze GmbH & Co KG D-99087 Erfurt Germany TenneT Transmiss Syst Operator NL-6812 Arnhem Netherlands Carl von Ossietzky Univ Oldenburg D-26129 Oldenburg Germany Siemens AG D-91058 Erlangen Germany Univ Technol Ilmenau Ilmenau Germany
Power system operation is gaining complexity due to the changes imposed by the energy transition. Especially, the increased share of intermittent and decentralized renewable generation units in the energy mix, an incr... 详细信息
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Deep Graph Reinforcement learning for Solving Multicut Problem
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ieee TRANSACTIONS ON NEURAL NETWORKS AND learning systems 2024年 第6期PP卷 11493-11506页
作者: Li, Zhenchen Yang, Xu Zhang, Yanchao Zeng, Shaofeng Yuan, Jingbin Liu, Jiazheng Liu, Zhiyong Han, Hua Chinese Acad Sci Inst Automat Key Lab Brain Cognit & Brain Inspired Intelligenc Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Future Technol Beijing 101408 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Multimodal Artificial Intelligence Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China
The multicut problem, also known as correlation clustering, is a classic combinatorial optimization problem that aims to optimize graph partitioning given only node (dis)similarities on edges. It serves as an elegant ... 详细信息
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Adaptive Optimal Output Regulation of Discrete-Time Linear systems: A Reinforcement learning Approach  62
Adaptive Optimal Output Regulation of Discrete-Time Linear S...
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62nd ieee conference on Decision and control (CDC)
作者: Chakraborty, Sayan Gao, Weinan Vamvoudakis, Kyriakos G. Jiang, Zhong-Ping NYU CAN Lab Brooklyn NY 11201 USA Northeastern Univ State Key Lab Synthet Automat Proc Ind Shenyang 110819 Peoples R China Georgia Inst Technol Daniel Guggenheim Sch Aerosp Engn Atlanta GA 30332 USA
In this paper, we solve the optimal output regulation problem for discrete-time systems without precise knowledge of the system model. Drawing inspiration from reinforcement learning and adaptive dynamic programming, ...
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Automated controller Calibration by Kalman Filtering
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ieee TRANSACTIONS ON control systems TECHNOLOGY 2023年 第6期31卷 2350-2364页
作者: Menner, Marcel Berntorp, Karl Cairano, Stefano Di Mitsubishi Elect Res Labs MERL Cambridge MA 02139 USA
This paper proposes a method for calibrating control parameters. Examples of such control parameters are gains of PID controllers, weights of a cost function for optimal control, filter coefficients, the sliding surfa... 详细信息
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Low-cost data-driven Predictive Stabilization of Unknown LTI systems: Two LMI Approaches  12
Low-cost Data-Driven Predictive Stabilization of Unknown LTI...
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12th International conference on control Mechatronics and Automation
作者: Ghorbani, Majid Tepljakov, Aleksei Kim, Yoonsoo Beheshti, Amin Rabiei Petlenkov, Eduard Tallinn Univ Technol Dept Comp Syst Tallinn Estonia Gyeongsang Natl Univ Grad Sch Mech & Aerosp Engn Jinju South Korea
In this paper, we address data-driven predictive control of Linear Time-Invariant (LTI) systems. Specifically, we demonstrate the direct learning of predictive laws from data, eliminating the need for system identific... 详细信息
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Experimental data-driven BESS controller tuning: A Set-Membership Approach
Experimental Data-driven BESS controller tuning: A Set-Membe...
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ieee conference on control Technology and Applications (CCTA)
作者: Cordoba-Pacheco, Andres Petrucci, Lorenzo Filippone, Matteo Ruiz, Fredy Politecn Milan Dipartimento Elettron Informaz & Bioingn Milan Italy Hitachi Energy Genoa Italy
In the context of microgrids, Battery Energy Storage systems play a vital role in maintaining power system reliability and efficiency. Adequate control strategies are necessary for exploiting these resources appropria... 详细信息
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data-driven Model Predictive control for Continuous-Time systems  63
Data-Driven Model Predictive Control for Continuous-Time Sys...
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63rd ieee conference on Decision and control, CDC 2024
作者: Wolski, Aleksander Chu, Bing Rapisarda, Paolo University of Southampton School of Electronics and Computer Science United Kingdom
We present some preliminary ideas on a data-driven Model Predictive control framework for continuous-time systems. We use Chebyshev polynomial orthogonal bases to represent system trajectories and subsequently develop... 详细信息
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