Sensorless field oriented control of nonsinusoidal flux-distribution permanent magnet synchronous motor with a FEM based ANN observer

Abstract

The sensorless vector control of a nonsinusoidal flux-distribution permanent magnet synchronous motor (PMSM) has been performed by a trained artificial neural network (ANN) using flux data obtained from the finite element method (FEM). A more sensitive rotor position has been estimated by using the fluxes of each of the three phases of the PMSM. In the proposed approach, magnet flux of the nonsinusoidal PMSM has been calculated by FEM for every single degree. Rotor position and speed values have been estimated by training an ANN with this information. The experimental results obtained by DS1103 development kit have proved the validity of the proposed approach.

Finite Element Analysis of the Inductance and Magnetic Field in the Permanent Magnet Spherical Motor

“…Especially motor parameter changes can cause incorrect determination of rotor and speed position in sensorless control methods and incorrect determination of motor flux in direct moment control [20]. For parameter estimation of permanent magnet motors, extended kalman filter [21], recursive least squares method [22], model reference adaptive system [23], finite elements method, artificial neural networks [24] and adaptive prediction methods [25] have been proposed. Since magnetic flux primariy flows through the iron core with high permeability, a core-type spherical motor shows the advantage of low-leakage magnetic flux.…”

Performance optimization of permanent magnet synchronous motor by cogging torque reduction

“…The comparison of the parameters and the characteristics of the two models give an insight into the impact that these three optimization parameters have on the cogging torque reduction but also on the overall motor operation. The accuracy of the motor design in terms of the magnetic flux density distribution and the core saturation is verified by the finite element analysis (FEA) as this numerical method has been proved to be a reliable tool in the machine design [18][19][20][21][22][23]. The FE models of the motors (BM and OM) had been derived and the magnetic flux density distribution in the air gap as well as in the motor cross-section was calculated as well.…”