Identification of MIMO Magnetic Bearing System Using Continuous Subspace Method with Frequency Sampling Filters Approach

As mentioned in NASA spinoff, “magnetic bearings support moving machinery without physical contact. They can levitate a rotating shaft and permit relative motion without friction or wear. Long considered a promising advancement, they are now moving beyond promise into actual service in such industrial applications as electric power generation, petroleum refining, machine tool operation and natural gas pipelines

2-stage approach for continuous time identification using step response estimates

This paper presents the 2-stage identification approach. The first stage is the identification of the system step response from the experimental data using the frequency sampling filter approach. This first stage is also referred as data compression stage in which the raw data will be analyzed in order to obtain a parameter that describes the empirical model of the analyzed data. The second stage is the identification of a continuous time state space model using subspace methods from the identified step response. The performance of the proposed approach is evaluated to identify the simulated systems and the real system of magnetic bearing apparatus

Continuous time state-space model identification with application to magnetic bearing systems

This thesis presents the identification of continuous time linear multi-variable systems using state-space models. A data-driven approach in realization by the subspace methods is carried out in developing the models. In this thesis, the approach by subspace methods is considered for both open-loop and closed-loop continuous time system identification. The Laguerre filter network, the instrumental variables and the frequency sampling filters are adopted in the framework of subspace model identification. More specifically, the Laguerre filters play a role in avoiding problems with differentiation in the Laplace operator, which leads to a simple algebraic relation. It also has the ability to cope with noise at high frequency region due to its orthogonality functions. The instrumental variables help to eliminate the process and measurement noise that may occur in the systems. The frequency sampling filters are used to compress the raw data, eliminate measurement noise so to obtain a set of clean and unbiased step response data. The combination of these techniques allows for the estimation of high quality models, in which, it leads to successful performance of the continuous time system identification overall. The application based on a magnetic bearing system apparatus is used to demonstrate the efficacy of the proposed techniques