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Machine Learning Based \(\mathcal {H}_2\) Norm Minimization for Maglev Vibration Isolation Platform

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Intelligent Systems and Applications (IntelliSys 2021)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 294))

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Abstract

Recent advancements in computational power technology and wide-usage of high qualified electronic hardware allow many engineers to create their control strategies in data-driven and model-free approaches, such as reinforcement-learning, genetic algorithm and other machine learning topics, rather than classical and modern control approaches. In this study, Q-Learning RL algorithm combined with analytic LMI method has been utilized to solve micro-scale vibration isolation problem as energy efficient as possible.

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Authors and Affiliations

  1. Magnetic Levitation Laboratory, Mechatronics Engineering Department, Yıldız Technical University, Beşiktaş, 34349, İstanbul, Turkey

    Ahmet Fevzi Bozkurt, Barış Can Yalçın & Kadir Erkan

Authors
  1. Ahmet Fevzi Bozkurt
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  2. Barış Can Yalçın
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  3. Kadir Erkan
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Corresponding author

Correspondence to Barış Can Yalçın .

Editor information

Editors and Affiliations

  1. Faculty of Science and Engineering, Saga University, Saga, Japan

    Kohei Arai

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Bozkurt, A.F., Yalçın, B.C., Erkan, K. (2022). Machine Learning Based \(\mathcal {H}_2\) Norm Minimization for Maglev Vibration Isolation Platform. In: Arai, K. (eds) Intelligent Systems and Applications. IntelliSys 2021. Lecture Notes in Networks and Systems, vol 294. Springer, Cham. https://doi.org/10.1007/978-3-030-82193-7_7

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