Data driven discovery of cyber physical systems

Ye Yuan, Xiuchuan Tang, Wei Zhou, Wei Pan, Xiuting Li, Hai-Tao Zhang, Han Ding () and Jorge Goncalves ()
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Ye Yuan: Huazhong University of Science and Technology
Xiuchuan Tang: Huazhong University of Science and Technology
Wei Zhou: Huazhong University of Science and Technology
Wei Pan: Delft University of Technology
Xiuting Li: Huazhong University of Science and Technology
Hai-Tao Zhang: Huazhong University of Science and Technology
Han Ding: Huazhong University of Science and Technology
Jorge Goncalves: Huazhong University of Science and Technology

Nature Communications, 2019, vol. 10, issue 1, 1-9

Abstract: Abstract 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.

Date: 2019
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DOI: 10.1038/s41467-019-12490-1

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