An integrated self-sensing approach for active magnetic bearings

Abstract

Self-sensing permits active magnetic bearings (AMBs) to consolidate the actuation and sensing functions into a single electromagnetic transducer. Eliminating the position sensing device as well as interfacing reduce potential system failure points, costs, and complexity. Self-sensing performance at present faces technical challenges such as magnetic cross-coupling, saturation, eddy currents, and system robustness. This work proposes an integrated self-sensing approach to collectively address mechanisms that contribute to modelling errors and position estimation inaccuracy. The self-sensing approach is based on the amplitude modulation technique and comprises a coupled reluctance network model (RNM) that is embedded in a nonlinear multiple input multiple output parameter estimator. The estimator employs a frequency-shifted model that is solved at a lower frequency to increase system performance. Furthermore, the RNM incorporates air gap fringing, complex permeability, and magnetic material nonlinearity terms. Magnetic saturation is accounted for using current scaling weights in the position estimation scheme. Basic functionality of the integrated self-sensing approach is demonstrated using an experimentally verified transient simulation model of the magnetic bearing. Verification and refinement of the RNM is accomplished through an iteration process using finite element method (FEM) results and experimental measurements. The simulation results show that the 40 node RNM can be accurate compared to an 80 000 node FEM analysis. Evaluation of the system stability margin indicates that the robustness of the magnetic bearing control is suitable for unrestricted long-term operation.