Abstract
Modern diagnostics and control algorithms rely on physical processes models. Such models have often complicated structure and their synthesis is usually difficult. The approaches based on Partial Differential Equations (PDE) work well for simulation purposes, however their usefulness can be limited in case of industrial applications when a full set of processes data is often inaccessible. The aforementioned problem was the main motivation to propose an adaptive identification method available to work on-line based on processes data. It is based on the Modulating Functions Method (MFM) and utilizes the Exact State Observers. The method was applied for real processes data collected from the industrial glass conditioning installation. The experimental results are presented and discussed in the paper.
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Acknowledgements
This work was supported by the scientific research funds from the Polish Ministry of Education and Science and AGH UST Agreement no. 16.16.120.773 and was also conducted within the research of EC Grant H2020-MSCARISE-2018/824046.
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Byrski, W., Drapała, M. (2023). A New Version of the On-Line Adaptive Non-standard Identification Procedure for Continuous-Time MISO Physical Processes. In: Kowalczuk, Z. (eds) Intelligent and Safe Computer Systems in Control and Diagnostics. DPS 2022. Lecture Notes in Networks and Systems, vol 545. Springer, Cham. https://doi.org/10.1007/978-3-031-16159-9_34
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