| Active magnetic bearings require some form of control based on feedback of the position of the suspended object to overcome open-loop instability, and to achieve targeted system performance by modifying the bearing dynamics. In many applications of magnetic bearings, a need to eliminate discrete position sensors may arise either from economic or reliability considerations. Magnetic bearings which estimate the position from the information available in the electromagnet signals are referred to as "self-sensing".; A signal processing technique is presented by which the position of a rotor supported in magnetic bearings may be deduced from the bearing current waveform. The bearing currents are presumed to be developed by a bi-state switching amplifier which produces a substantial high frequency switching ripple. The amplitude of this ripple is a function of power supply voltage, switching duty cycle, and bearing inductance. Inductance is predominantly a function of the bearing air gap or, equivalently, the rotor position while the duty cycle is fundamentally dependent upon the developed bearing force. Ideally, the sensor signal processor should exactly extract rotor position information while perfectly rejecting bearing force information.; When the bearing is a perfect inductor, these functional relationships are easily established and the gap dependence is monotonic. Since voltage and duty cycle are both easily measured, the relationships can be inverted with a non-linear parameter estimator to extract the rotor position. One method of implementing this estimator using parameter estimation technique is presented, and its performance is evaluated both by computer simulations and experiments. The method is demonstrated to produce a fairly wide bandwidth sensor with acceptably low feed-through of the bearing force. The effect of nonidealities in the bearing is addressed. |
