Cascade tanks system identification for robust predictive control

• Autorzy: Jakowluk Wiktor , Jaszczak Sławomir

Identyfikatory

DOI

10.24425/bpasts.2022.143646

• Języki publikacji: EN

Abstrakty

EN

The main goal of estimating models for industrial applications is to guarantee the cheapest system identification. The requirements for the identification experiment should not be allowed to affect product quality under normal operating conditions. This paper deals with ensuring the required liquid levels of the cascade system tanks using the model predictive control (MPC) method. The MPC strategy was extended with the Kalman filter (KF) to predict the system’s succeeding states subject to a reference trajectory in the presence of both process and measurement noise covariances. The main contribution is to use the application-oriented input design to update the parameters of the model during system degradation. This framework delivers the least-costly identification experiment and guarantees high performance of the system with the updated model. The methods presented are evaluated both in the experiments on a real process and in the computer simulations. The results of the robust MPC application for cascade system water levels control are discussed.

Słowa kluczowe

EN

application-oriented input design; Kalman filter; model predictive control; MPC; system identification

PL

projektowanie wejścia zorientowanego na aplikację; filtr Kalmana; sterowanie predykcyjne modelu; identyfikacja systemu

• Wydawca: Polska Akademia Nauk, Wydział IV Nauk Technicznych
• Czasopismo: Bulletin of the Polish Academy of Sciences. Technical Sciences
• Rocznik: 2022
• Tom: Vol. 70, nr 6
• Strony: art. no. e143646
• Opis fizyczny: Bibliogr. 30 poz., rys., tab.

Twórcy

autor

Jakowluk Wiktor

• Bialystok University of Technology, Faculty of Computer Science, Wiejska 45A, 15-351 Białystok, Poland

• https://orcid.org/0000-0003-0942-8903

autor

Jaszczak Sławomir

• West Pomeranian University of Technology in Szczecin, Faculty of Computer Science and Information Technology, Żołnierska 49, ˙71-210 Szczecin, Poland

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Uwagi

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).