Identification and modeling of the dynamical object with the use of hil technique

• Autorzy: Sajewski Łukasz , Karwowski Przemysław

Identyfikatory

DOI

10.14313/JAMRIS/3‐2024/21

• Języki publikacji: EN

Abstrakty

EN

This article presents a comparison of a classical approach to identification of unstable object and an approach based on artificial neural networks. Model verification is carried out based on the Quanser Qube-Servo object with the using of myRIO Real-Time controller as target. It is shown that model identification using neural networks gives a more accurate representation of the object. In addition, the Hardware-in-the-loop (HIL) technique is discussed and used, for implement of the control algorithm.

Słowa kluczowe

EN

HIL; neural networks; inverted pendulum

• Wydawca: Łukasiewicz Industrial Research Institute for Automation and Measurements PIAP
• Czasopismo: Journal of Automation Mobile Robotics and Intelligent Systems
• Rocznik: 2024
• Tom: Vol. 18, No. 3
• Strony: 49--54
• Opis fizyczny: Bibliogr. 7 poz., rys.

Twórcy

autor

Sajewski Łukasz

• Faculty of Electrical Engineering, Bialystok University of Technology, Bialystok, 15-351, Poland

• https://orcid.org/0000-0003-1098-5571

autor

Karwowski Przemysław

• Faculty of Electrical Engineering, Bialystok University of Technology, Bialystok, 15-351, Poland

• https://orcid.org/0009-0004-3374-6658

Bibliografia

• [1] R. Isermann, M. Münchhof, Identification of Dynamic Systems: An Introduction with Applications, Springer-Verlag Berlin Heidelberg 2011. 10.1007/978-3-540-78879-9.
• [2] J. Ledin, “Simulation Engineering: Build Better Embedded Systems Faster”, CRC Press 2001. doi: 10.1201/9781482280722.
• [3] J. Apkarian, M. Lévis, Quanser Student Workbook QUBE-Servo Experiment for MATLAB/Simulink Users, Markham, Ontario, 2014.
• [4] NI myRIO-1900 User Guide and Specifications, National Instruments, 2018.
• [5] L. Zadeh, “Probability measures of fuzzy events”, Journal Math. Analysis and Appl., vol. 23, no. 2, 1968, 421–427. doi: 10.1016/0022-247X(68)90078-4.
• [6] W. Rudin, “Principles of mathematical analysis”, McGraw-Hill: New York, 1967, 10–54.
• [7] T. Gabor, S. Illium, M. Zorn, C. Lenta, A. Mattausch, L. Belzner, C. Linnhoff-Popien, “Self-Replication in Neural Networks”, Artif Life, vol. 28, no. 2, 2022, 205–223. doi: 10.1162/artl_a_00359.

Uwagi

Opracowanie rekordu ze środków MNiSW, umowa nr POPUL/SP/0154/2024/02 w ramach programu "Społeczna odpowiedzialność nauki II" - moduł: Popularyzacja nauki (2025).