Abstract: Multiphase Induction Machine (IM) is normally
controlled using rotor field oriented vector control. Under phase(s)
loss, the machine currents can be optimally controlled to satisfy
certain optimization criteria. In this paper we discuss the performance
of double manifold sliding mode observer (DM-SMO) in Sensorless
control of multiphase induction machine under unsymmetrical
condition (one phase loss). This observer is developed using the IM
model in the stationary reference frame. DM-SMO is constructed by
adding extra feedback term to conventional single mode sliding mode
observer (SM-SMO) which proposed in many literature. This leads to
a fully convergent observer that also yields an accurate estimate of
the speed and stator currents. It will be shown by the simulation
results that the estimated speed and currents by the method are very
well and error between real and estimated quantities is negligible.
Also parameter sensitivity analysis shows that this method is rather
robust against parameter variation.
Abstract: Rotor Flux based Model Reference Adaptive System
(RF-MRAS) is the most popularly used conventional speed
estimation scheme for sensor-less IM drives. In this scheme, the
voltage model equations are used for the reference model. This
encounters major drawbacks at low frequencies/speed which leads to
the poor performance of RF-MRAS. Replacing the reference model
using Neural Network (NN) based flux estimator provides an
alternate solution and addresses such drawbacks. This paper
identifies an NN based flux estimator using Single Neuron Cascaded
(SNC) Architecture. The proposed SNC-NN model replaces the
conventional voltage model in RF-MRAS to form a novel MRAS
scheme named as SNC-NN-MRAS. Through simulation the proposed
SNC-NN-MRAS is shown to be promising in terms of all major
issues and robustness to parameter variation. The suitability of the
proposed SNC-NN-MRAS based speed estimator and its advantages
over RF-MRAS for sensor-less induction motor drives is
comprehensively presented through extensive simulations.
Abstract: This paper presents a speed sensorless direct torque
control scheme using space vector modulation (DTC-SVM) for
permanent magnet synchronous motor (PMSM) drive based a Model
Reference Adaptive System (MRAS) algorithm and stator resistance
estimator. The MRAS is utilized to estimate speed and stator
resistance and compensate the effects of parameter variation on stator
resistance, which makes flux and torque estimation more accurate
and insensitive to parameter variation. In other hand the use of SVM
method reduces the torque ripple while achieving a good dynamic
response. Simulation results are presented and show the effectiveness
of the proposed method.
Abstract: In this paper, we have examined the effect of process
parameter variation on the electrical characteristics of an LDMOS
device. The rate of change in the electrical parameters such as cut off
frequency, breakdown voltage and drain saturation current as a
function of the process parameters is investigated
Abstract: This paper discusses two observers, which are used
for the estimation of parameters of PMSM. Former one, reduced
order observer, which is used to estimate the inaccessible parameters
of PMSM. Later one, full order observer, which is used to estimate
all the parameters of PMSM even though some of the parameters are
directly available for measurement, so as to meet with the
insensitivity to the parameter variation. However, the state space
model contains some nonlinear terms i.e. the product of different
state variables. The asymptotic state observer, which approximately
reconstructs the state vector for linear systems without uncertainties,
was presented by Luenberger. In this work, a modified form of such
an observer is used by including a non-linear term involving the
speed. So, both the observers are designed in the framework of
nonlinear control; their stability and rate of convergence is discussed.