Digitální knihovnaUPCE
 

Fakulta elektrotechniky a informatiky / Faculty of Electrical Engineering and Informatics

Stálý URI pro tuto komunituhttps://hdl.handle.net/10195/3847

Práce obhájené před rokem 2008 jsou uloženy pouze v kolekci Vysokoškolské kvalifikační práce

Procházet

Filtry

2021 - 20232

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Předmět: search.filters.subject.laboratory plant CE 152Datum zahájení: 2020

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Nyní se zobrazuje 1 - 2 z 2
  • Náhled
    Konferenční objektpeer-reviewedpostprintOtevřený přístup
    Application of Model Predictive Controller to Magnetic Levitation
    (IEEE (Institute of Electrical and Electronics Engineers), 2023) Novotný, Aleš; Honc, Daniel
    Model Predictive Control (MPC) is an advanced process control method that is widely used for controlling both linear and under some modifications for non-linear systems. The aim of this work is to show a way how to apply MPC to a non-linear Magnetic Levitation System (MLS) and its capability of stabilization and closed-loop performance. This work is a continuation of the previous article where the laboratory plant CE 152 MLS was identified, and a non-linear model was designed. This paper proposes a control circuit consisting of linearized discretized non-linear MLS model, Extended Kalman Filter (EKF) algorithm for state estimation and linear MPC. The results are verified in simulation and real-world experiment.
  • Náhled
    Konferenční objektpeer-reviewedpostprint (accepted version)Omezený přístup
    Identification of Magnetic Levitation System
    (Springer Science and Business Media, 2021) Novotný, Aleš; Honc, Daniel; Dušek, František
    Magnetic Levitation Systems (MLS) are usually highly non-linear systems with a great sensitivity to the size of the control input. Therefore, special emphasis should be placed on the correct identification of all unknown MLS parameters. This paper describes the principle and procedure for identifying laboratory plant CE 152 MLS with an emphasis on automatically processing identification data. Moving-Average Filter (MAF) and Fast Fourier Transform Filter (FFTF) methods are compared to filtering input data noise. Key parameters are then estimated using the Least Squares Method (LSM). The results are verified in simulation and real-world experiment using a simple PID controller.