Centralized optimal position control for active magnetic bearings: comparison with decentralized control

This paper discusses a closed-loop centralized control for a multi-input multi-output active magnetic bearing system. A genetic algorithm for design and a credible comparison of different controller structures are proposed. The evaluation of the design trade-offs of linear-quadratic and loop transfer recovery controllers are studied. The model-based controllers are compared with the classical PID controller and the cascaded PI/PD controller. The properties of the tested control configurations are examined using maximum singular values of the output sensitivity function of the closed-loop system and the tolerated disturbance at the input of the plant. Furthermore, indices such as measured peak output sensitivity, singular values of the uncertain control system, responses to the step reference position, step disturbance, and impulse force disturbance are examined. A good agreement between the simulation and the experimental results from the test-rig is shown.

[1]  Peter J. Fleming,et al.  On-line genetic auto-tuning of mixed H2/H∞ optimal magnetic bearing controllers , 1998 .

[2]  Osami Matsushita,et al.  An evaluation of stability indices using sensitivity functions for active magnetic bearing supported high-speed rotor , 2007 .

[3]  Gene F. Franklin,et al.  Digital control of dynamic systems , 1980 .

[4]  J. Pyrhönen,et al.  Modelling the Force versus Current Characteristics, Linearized Parameters and Dynamic Inductance of Radial Active Magnetic Bearings Using Different Numerical Calculation Methods , 2005 .

[5]  Hung-Cheng Chen,et al.  Genetic Algorithms Based Optimization Design of a PID Controller for an Active Magnetic Bearing , 2006 .

[6]  Peter J. Fleming,et al.  Multi-objective optimisation of distributed active magnetic bearing controllers , 1997 .

[7]  Huibert Kwakernaak,et al.  Optimal low-sensitivity linear feedback systems , 1969, Autom..

[8]  Sung-Chong Chung,et al.  Integrated design of radial active magnetic bearing systems using genetic algorithms , 2002 .

[9]  Panagiotis Tsiotras,et al.  Zero- and low-bias control designs for active magnetic bearings , 2003, IEEE Trans. Control. Syst. Technol..

[10]  Yuri N. Zhuravlyov,et al.  On LQ-control of magnetic bearing , 2000, IEEE Trans. Control. Syst. Technol..

[11]  Rafal Piotr Jastrzebski,et al.  Design and implementation of FPGA-based LQ control of active magnetic bearings , 2007 .

[12]  D. Dolinar,et al.  Decentralized PI/PD position control for active magnetic bearings , 2006 .

[13]  Wojciech Grega,et al.  COMPARISON OF LINEAR CONTROL METHODS FOR AN AMB SYSTEM , 2005 .

[14]  Ph. Bühler,et al.  Identification and Self Tuning Control for Active Magnetic Bearing Systems: Levitation of Unknown Rotors , 2002 .

[15]  Baoye Song,et al.  Fast Genetic Algorithms Used for PID Parameter Optimization , 2007, 2007 IEEE International Conference on Automation and Logistics.

[16]  Erkki Lantto,et al.  Robust control of magnetic bearings in subcritical machines , 1999 .

[17]  Alan F. Lynch,et al.  Experimental comparison of nonlinear tracking controllers for active magnetic bearings , 2007 .