Data-Driven Self-sensing Technique for Active Magnetic Bearing

Abstract

In the last two decades, soft sensors proved themselves as a valuable alternative to the physical sensor for gathering critical process information. A self-sensing technique for the magnetic bearing is considered as a soft sensor since the object position is estimated from the current signal of the electromagnet. Self-sensing techniques developed so far are the model-driven soft sensors. This paper presents a data-driven self-sensing technique to compensate for the nonlinear characteristic of the electromagnet. First, model-driven self-sensing techniques and their problems are reviewed. Then, data-driven self-sensing technique using recurrent neural network (RNN) is proposed to compensate for the nonlinear characteristics. Both the position control and self-sensing with the RNN are implemented in a single digital signal processor. The effectiveness of the proposed method is experimentally verified by comparison with the current slope method. Both estimation errors during initial levitation and jitter after levitation are reduced by 90% and 36%, respectively. Estimation error with 2 Hz sine wave is improved by 65.9%, while jitter during self-sensing levitation is cut down to 26.8%.