Identification of magnetically levitated machines
Vuojolainen, Jouni (2020-06-16)
Väitöskirja
Vuojolainen, Jouni
16.06.2020
Lappeenranta-Lahti University of Technology LUT
Acta Universitatis Lappeenrantaensis
School of Energy Systems
School of Energy Systems, Sähkötekniikka
Kaikki oikeudet pidätetään.
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In reference to IEEE copyrighted material which is used with permission in this thesis, the IEEE does not endorse any of Lappeenranta-Lahti University of Technology LUT's products or services. Internal or personal use of this material is permitted. If interested in reprinting/republishing IEEE copyrighted material for advertising or promotional purposes or for creating new collective works for resale or redistribution, please go to http://www.ieee.org/publications_ standards/publications/rights/rights_ link.html to learn how to obtain a License from RightsLink.
Julkaisun pysyvä osoite on
https://urn.fi/URN:ISBN:978-952-335-518-7
https://urn.fi/URN:ISBN:978-952-335-518-7
Tiivistelmä
Magnetically levitated machines use magnetic levitation to maintain the separation between the bearing races. They offer various benefits compared with other types of bearings: no contact between the bearing races, the absence of lubrication, and suitability for high-speed applications. Magnetically levitated machines can be divided into two groups: active magnetic bearing (AMB) and bearingless machines.
An active magnetic bearing is the traditional magnetic bearing. AMBs use electromagnets to maintain the rotor in a stable position. Bearingless machines are similar to AMB machines, but they use the same air gap for the generation of the torque-producing and levitation flux. Magnetically levitated systems are unstable, complex, and nonlinear multi-input multi-output systems. Thus, they require feedback control for stable operation. Further, accurate modeling is essential for the robust control of these systems.
In this doctoral dissertation, the system identification aspect of magnetically levitated systems is considered. In general, system identification refers to construction of mathematical models of systems by measuring input-output data during an identification experiment. System identification can assist in the modeling, and more accurate models can be built with real data. In this dissertation, different excitation signals; pseudorandom binary sequence (PRBS), chirp, stepped sine, and multisine are first applied to the AMB system identification for single-input single-output and multi-input multi-output cases. Next, the online identification of an AMB rotor–bearing system with a sliding discrete Fourier transform with the direct and indirect identification is shown. Then, the identification methods used for AMB machines are applied to bearingless machines. The effects of noise and delay on the linearized plant identification accuracy based on nonlinear simulation models are examined. Finally, the AMB rotor–bearing system identification with the PRBS for a multi-input multi-output system is presented.
The doctoral dissertation provides results for the identification of a magnetically levitated system. Several laboratory test rigs were used to obtain the results. It is shown that the methods used for the AMB system identification can be applied to bearingless machines.
An active magnetic bearing is the traditional magnetic bearing. AMBs use electromagnets to maintain the rotor in a stable position. Bearingless machines are similar to AMB machines, but they use the same air gap for the generation of the torque-producing and levitation flux. Magnetically levitated systems are unstable, complex, and nonlinear multi-input multi-output systems. Thus, they require feedback control for stable operation. Further, accurate modeling is essential for the robust control of these systems.
In this doctoral dissertation, the system identification aspect of magnetically levitated systems is considered. In general, system identification refers to construction of mathematical models of systems by measuring input-output data during an identification experiment. System identification can assist in the modeling, and more accurate models can be built with real data. In this dissertation, different excitation signals; pseudorandom binary sequence (PRBS), chirp, stepped sine, and multisine are first applied to the AMB system identification for single-input single-output and multi-input multi-output cases. Next, the online identification of an AMB rotor–bearing system with a sliding discrete Fourier transform with the direct and indirect identification is shown. Then, the identification methods used for AMB machines are applied to bearingless machines. The effects of noise and delay on the linearized plant identification accuracy based on nonlinear simulation models are examined. Finally, the AMB rotor–bearing system identification with the PRBS for a multi-input multi-output system is presented.
The doctoral dissertation provides results for the identification of a magnetically levitated system. Several laboratory test rigs were used to obtain the results. It is shown that the methods used for the AMB system identification can be applied to bearingless machines.
Kokoelmat
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