A Neural Network Based SC_MRAS Observer for High-Performance SPIM Drives

Abstract

In the paper, a Stator Current Model Reference
Adaptive System based observer using neural network
for sensorless controlled Six Phase Induction Motor
drive is presented. For this scheme, the measured
stator current components are used as the reference
model of the MRAS observer to avoid the use of a
pure integrator and reduce influence of motor
parameter variations. The adaptive model of proposal
observer use a two-layer neural network to estimate
the stator current, which requires the rotor flux
information that can be obtained from the voltage
model. A back-propagation learning algorithm is used
to minimise the error in current estimation and hence
in generating the estimated speed. Speed estimation
performance of the proposed MRAS scheme is
verified and compared with the classical rotor flux
MRAS when applied to sensorless vector control
SPIM drives. Simulation results have demonstrated
that the proposed MRAS observer for sensorless
control of SPIM drive has good dynamic responses,
high precision in both the transient and steady modes,
the performance of the SC_MRAS using NN is
significantly improved especially at low and near zero
speed range.