Data driven discovery of cyber physical systems
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 (TU Delft - Robust Robot Systems)
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 (Université du Luxembourg, Cavendish Laboratory, Huazhong University of Science and Technology)
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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.
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