Due to their frictionless operation active magnetic bearings (AMBs) are essential components
in high-speed rotating machinery. Active magnetic control of a high speed rotating rotor
requires precise knowledge of its position. Self-sensing endeavours to eliminate the required
position sensors by deducing the rotor’s position from the voltages and currents with which it
is levitated. For self-sensing AMBs to be of practical worth, they have to be robust. Robustness
analysis aims to quantify a control system’s tolerance for uncertainty. In this study the stability
margin of a two degree-of-freedom self-sensing AMB is estimated by means of μ-analysis.
Detailed black-box models are developed for the main subsystems in the AMB by means of
discrete-time system identification. Suitable excitation signals are generated for system identification
in cognisance of frequency induced nonlinear behaviour of the AMB. Novel graphs
that characterize an AMB’s behaviour for input signals of different amplitudes and frequency
content are quite useful in this regard. In order to obtain models for dynamic uncertainty in
the various subsystems (namely the power amplifier, self-sensing module and AMB plant), the
identified models are combined to form a closed-loop model for the self-sensing AMB. The
response of this closed-loop model is compared to the original AMB’s response and models for
the dynamic uncertainty are empirically deduced. Finally, the system’s stability margin for the
modelled uncertainty is estimated by means of μ-analysis. The potentially destabilizing effects
of parametric uncertainty in the controller coefficients as well as dynamic uncertainty in the
AMB plant and self-sensing module are examined. The resultant μ-analyses show that selfsensing
AMBs are much less robust for parametric uncertainty in the controller than AMBs
equipped with sensors. The μ-analyses for dynamic uncertainty confirm that self-sensing
AMBs are rather sensitive for variations in the plant or the self-sensing algorithm. Validation
of the stability margins estimated by μ-analysis reveal that μ-analysis is overoptimistic for
parametric uncertainty on the controller and conservative for dynamic uncertainty. (Validation
is performed by means of Monte Carlo simulations.) The accuracy of μ-analysis is critically
dependent on the accuracy of the uncertainty model and the degree to which the system is
linear or not. If either of these conditions are violated, μ-analysis is essentially worthless. / Thesis (Ph.D. (Electronical Engineering))--North-West University, Potchefstroom Campus, 2010
| Identifer | oai:union.ndltd.org:NWUBOLOKA1/oai:dspace.nwu.ac.za:10394/3993 |
| Date | January 2009 |
| Creators | Van Vuuren, Pieter Andries |
| Publisher | North-West University |
| Source Sets | North-West University |
| Language | English |
| Detected Language | English |
| Type | Thesis |
Page generated in 0.0017 seconds