Position and Speed Estimation for a Surface-Mount PMSM using RBFNN Observer with Stability Guarantee

Abstract

This paper proposes a radial basis function neural network (RBFNN) observer for surface-mount permanent magnet synchronous motor (SPMSM). The corresponding observer is used to estimate the rotor speed, and the rotor position. The convergence of the observer estimation error is analyzed using Lyapunov theory, and uniformly ultimate boundedness stability is guaranteed. Simulation results are shown to confirm the effectiveness of the proposed observer.