scholarly journals Active Magnetic Bearing Online Levitation Recovery through μ-Synthesis Robust Control

Actuators ◽  
2017 ◽  
Vol 6 (1) ◽  
pp. 2 ◽  
Author(s):  
Alexander Pesch ◽  
Jerzy Sawicki
Keyword(s):  
Robust Control ◽  
Magnetic Bearing ◽  
Active Magnetic Bearing

scholarly journals Robust control of active magnetic bearing systems for helium centrifugal cold compressors

2019 ◽  
Vol 502 ◽  
pp. 012055
Author(s):  
S Y Zhang ◽  
C B Wei ◽  
S Q Yang ◽  
Q M Jia ◽  
X B Dong ◽  
...  
Keyword(s):  
Robust Control ◽  
Magnetic Bearing ◽  
Active Magnetic Bearing

scholarly journals System Identification and Robust Control of Multi-Input Multi-Output Active Magnetic Bearing Systems

2016 ◽  
Vol 24 (4) ◽  
pp. 1227-1239 ◽  
Author(s):  
Amin Noshadi ◽  
Juan Shi ◽  
Wee Sit Lee ◽  
Peng Shi ◽  
Akhtar Kalam
Keyword(s):  
Robust Control ◽  
System Identification ◽  
Magnetic Bearing ◽  
Active Magnetic Bearing

Estimating Model Uncertainty using Confidence Interval Networks: Applications to Robust Control

2005 ◽  
Vol 128 (3) ◽  
pp. 626-635 ◽  
Author(s):  
Gregory D. Buckner ◽  
Heeju Choi ◽  
Nathan S. Gibson
Keyword(s):  
Robust Control ◽  
Confidence Interval ◽  
Soft Computing ◽  
Model Uncertainty ◽  
Intelligent System ◽  
Magnetic Bearing ◽  
Active Magnetic Bearing ◽  
Control Synthesis ◽  
Model Parameters ◽  
Computing Approach

Robust control techniques require a dynamic model of the plant and bounds on model uncertainty to formulate control laws with guaranteed stability. Although techniques for modeling dynamic systems and estimating model parameters are well established, very few procedures exist for estimating uncertainty bounds. In the case of H∞ control synthesis, a conservative weighting function for model uncertainty is usually chosen to ensure closed-loop stability over the entire operating space. The primary drawback of this conservative, “hard computing” approach is reduced performance. This paper demonstrates a novel “soft computing” approach to estimate bounds of model uncertainty resulting from parameter variations, unmodeled dynamics, and nondeterministic processes in dynamic plants. This approach uses confidence interval networks (CINs), radial basis function networks trained using asymmetric bilinear error cost functions, to estimate confidence intervals associated with nominal models for robust control synthesis. This research couples the “hard computing” features of H∞ control with the “soft computing” characteristics of intelligent system identification, and realizes the combined advantages of both. Simulations and experimental demonstrations conducted on an active magnetic bearing test rig confirm these capabilities.

The Robust Control of Magnetic Suspension with Rapidly Changing of Rotor Speed

2009 ◽  
Vol 147-149 ◽  
pp. 302-307 ◽  
Author(s):  
Arkadiusz Mystkowski ◽  
Zdzisław Gosiewski
Keyword(s):  
Control System ◽  
Robust Control ◽  
High Speed ◽  
Controller Design ◽  
Angular Speed ◽  
Magnetic Bearing ◽  
Magnetic Suspension ◽  
Experimental Results ◽  
Active Magnetic Bearing ◽  
Robust Controller

An optimal robust vibration control of a rotor supported magnetically over a wide angular speed range is presented in the paper. The laboratory stand with the high speed rotor (max. 24000 rpm) was designed. The wide bandwidth controller with required gain, which is necessary to stabilize the structurally unstable and active magnetic bearing system was computed. For controller design, the weighting functions putted on the input and output signals were chosen. For control design, the dynamics of the rotor and uncertain parameters were considered. The optimized control system by minimization of the H norm putted on transient process of the system was presented. The robust controller was designed with considered asymmetrically magnetic bearings, signal limits and power amplifiers dynamic. The success of the robust control is demonstrated through computer simulations and experimental results. Matlab-Simulink was used for the numerical simulation. The experimental results show the effectiveness of the control system as good vibrations reducing and robustness of the designed controller in all dynamic states.

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