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Article Navigation >  IEEE/CAA Journal of Automatica Sinica  > 2024  > In Press, Accepted Manuscript
P. Song, J. Wang, C. Zhao, and B. Huang, “From static and dynamic perspectives: A survey on historical data benchmarks of control performance monitoring,” IEEE/CAA J. Autom. Sinica, 2024. doi: 10.1109/JAS.2024.124902
Citation: P. Song, J. Wang, C. Zhao, and B. Huang, “From static and dynamic perspectives: A survey on historical data benchmarks of control performance monitoring,” IEEE/CAA J. Autom. Sinica, 2024. doi: 10.1109/JAS.2024.124902
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From Static and Dynamic Perspectives: A Survey on Historical Data Benchmarks of Control Performance Monitoring

doi: 10.1109/JAS.2024.124902
Funds:  This work was supported in part by the National Natural Science Foundation of China (62125306), Zhejiang Key Research and Development Project (2024C01163), and the State Key Laboratory of Industrial Control Technology, China (ICT2024A06)
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    Abstract
  • In recent decades, control performance monitoring (CPM) has experienced remarkable progress in research and industrial applications. While CPM research has been investigated using various benchmarks, the historical data benchmark (HIS) has garnered the most attention due to its practicality and effectiveness. However, existing CPM reviews usually focus on the theoretical benchmark, and there is a lack of an in-depth review that thoroughly explores HIS-based methods. In this article, a comprehensive overview of HIS-based CPM is provided. First, we provide a novel static-dynamic perspective on data-level manifestations of control performance underlying typical controller capacities including regulation and servo: static and dynamic properties. The static property portrays time-independent variability in system output, and the dynamic property describes temporal behavior driven by closed-loop feedback. Accordingly, existing HIS-based CPM approaches and their intrinsic motivations are classified and analyzed from these two perspectives. Specifically, two mainstream solutions for CPM methods are summarized, including static analysis and dynamic analysis, which match data-driven techniques with actual controlling behavior. Furthermore, this paper also points out various opportunities and challenges faced in CPM for modern industry and provides promising directions in the context of artificial intelligence for inspiring future research.

     

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