Improving Operational Performance of Active Magnetic Bearings Using Kalman Filter and State Feedback Control

Schuhmann, Thomas; Hofmann, W.; Werner, Ralf

doi:10.1109/tie.2011.2161056

Cited by 166 publications

(57 citation statements)

References 21 publications

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“…In this subsection, a Kalman filter is selected for state estimation, which is considered as the the optimal recursive data processing algorithm, and has the highest efficiency in solving most of the engineering problems [13]- [15]. Kalman filter uses the state space equation and recursive method to observe the states, and has no requirements on the smooth or time invariant characteristics of the signal.…”

Section: State Estimationmentioning

confidence: 99%

Explicit model predictive control on the air path of turbocharged diesel engines

Zhao

Liu

Stobart

et al. 2013

2013 American Control Conference

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Section: State Estimationmentioning

confidence: 99%

Explicit model predictive control on the air path of turbocharged diesel engines

Zhao

Liu

Stobart

et al. 2013

2013 American Control Conference

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“…Accurate models obtained by system identification are needed in order to design high-performance controllers (Noshadi et al, 2016), and they have an increasingly important role for diagnostic (Schuhmann et al, 2012) and monitoring purposes (Quinn et al, 2005), (Aenis et al, 2002), (Tiwiri and Chougale, 2014). A common choice for the excitation signal is a sine-wave-based spectrally rich signal, such as a multisine or swept sinusoid signal with a frequency content covering the desired frequency bands.…”

Section: Introductionmentioning

confidence: 99%

Comparison of Excitation Signals in Active Magnetic Bearing System Identification

Vuojolainen¹,

Nevaranta²,

Jastrzębski³

et al. 2017

MIC

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Active magnetic bearings (AMBs) offer frictionless suspension, vibration insulation, programmable stiffness, and damping, among other advantages, in levitated rotor applications. However, AMBs are inherently unstable and require accurate system models for the high-performance model-based multi-input multi-output control of rotor position. Control electronics with high calculation capacity and accurate sensors of AMBs provide an opportunity to implement various identification schemes. A variety of artificial excitation signal-based identification methods can thus be achieved with no additional hardware. In this paper, a selection of excitation signals, namely the pseudorandom binary sequence (PRBS), chirp signal, multisine, and stepped sine are presented, applied, and compared with the AMB system identification. From the identification experiments, the rotor-bearing system, the inner current control loop, and values of position and current stiffness are identified. Unlike recently published works considering excitation-based identification of AMB rotor systems, it is demonstrated that identification of the rotor system dynamics can be carried out using various well-established excitation signals. Application and feasibility of these excitation signals in AMB rotor systems are analyzed based on experimental results.

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“…Due to this importance, various kinds of active magnetic bearings have been developed and their control methods have been studied (e.g., [2][3][4][5][6][7][8][9][10]). …”

Section: Introductionmentioning

confidence: 99%

Electromagnetic Levitation System for Active Magnetic Bearing Wheels

Park¹

2017

Bearing Technology

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In this chapter, the author presents an electromagnetic levitation system for active magnetic bearing wheels. This system consists of a rotor, a shaft, a cover, and a base. The author derives a meaningful electromagnetic force by using the singular value decomposition. The author develops a control system using the proportional-integral-derivative controller to control the position of the rotor and regulate the two gimbal angles of the rotor. The author gives the numerical simulation and experimental results on the control of the electromagnetic levitation system.

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Improving Operational Performance of Active Magnetic Bearings Using Kalman Filter and State Feedback Control

Cited by 166 publications

References 21 publications

Explicit model predictive control on the air path of turbocharged diesel engines

Explicit model predictive control on the air path of turbocharged diesel engines

Comparison of Excitation Signals in Active Magnetic Bearing System Identification

Electromagnetic Levitation System for Active Magnetic Bearing Wheels

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