Data driven discovery of cyber physical systems

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作者
Ye Yuan
Xiuchuan Tang
Wei Zhou
Wei Pan
Xiuting Li
Hai-Tao Zhang
Han Ding
Jorge Goncalves
机构
[1] Huazhong University of Science and Technology,School of Artificial Intelligence and Automation, Key Laboratory of Image Processing and Intelligent Control
[2] Huazhong University of Science and Technology,State Key Lab of Digital Manufacturing Equipment and Technology
[3] Huazhong University of Science and Technology,School of Mechanical Science and Engineering
[4] Delft University of Technology,Department of Cognitive Robotics
[5] University of Cambridge,Department of Plant Sciences
[6] University of Luxembourg,Luxembourg Centre for Systems Biomedicine
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摘要
Cyber-physical systems embed software into the physical world. They appear in a wide range of applications such as smart grids, robotics, and intelligent manufacturing. Cyber-physical systems have proved resistant to modeling due to their intrinsic complexity arising from the combination of physical and cyber components and the interaction between them. This study proposes a general framework for discovering cyber-physical systems directly from data. The framework involves the identification of physical systems as well as the inference of transition logics. It has been applied successfully to a number of real-world examples. The novel framework seeks to understand the underlying mechanism of cyber-physical systems as well as make predictions concerning their state trajectories based on the discovered models. Such information has been proven essential for the assessment of the performance of cyber-physical systems; it can potentially help debug in the implementation procedure and guide the redesign to achieve the required performance.
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