On-line process identification using the Modulating Functions Method and non-asymptotic state estimation

• Autorzy: Byrski Witold , Drapała Michał

Identyfikatory

DOI

10.24425/acs.2022.142845

• Języki publikacji: EN

Abstrakty

EN

The paper presents an iterative identification method dedicated for industrial processes. The method consists of two steps. In the first step, a MISO system is identified with the Modulating Functions Method to obtain sub-models with a common denominator. In the second step, the obtained subsystems are re-identified. This procedure enables to obtain the set of models with different denominators of the transfer functions. The algorithm was used for on-line identification of a glass conditioning process. Identification window is divided into intervals, in which the models can be updated based on recent process data, with the use of the integral state observer. Results of the performed simulations for the identified models are compared with the historical process data.

Słowa kluczowe

EN

system identification; modulating functions method; state observers; signal processing; adaptive algorithms

• Wydawca: Polish Academy of Sciences, Committee of Automatic Control and Robotics
• Czasopismo: Archives of Control Sciences
• Rocznik: 2022
• Tom: Vol. 32, No. 3
• Strony: 535--555
• Opis fizyczny: Bibliogr. 23 poz., rys., tab., wzory

Twórcy

autor

Byrski Witold

• Department of Automatic Control and Robotics, AGH University of Science and Technology, Al. Mickiewicza 30, 30-059 Kraków, Poland

autor

Drapała Michał

• Department of Automatic Control and Robotics, AGH University of Science and Technology, Al. Mickiewicza 30, 30-059 Kraków, Poland

Bibliografia

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Uwagi

1. 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.

2. Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023)