an investigation of dual stator winding induction machines

The basic idea of a closed loop flux observer is that the current error can be driven to
zero through the use of proper feedback acting on the estimated rotor flux linkage since
the rotor flux linkage acts as a state feedback of stator current. In the design of the
observer, the limitations on gains and eigenvalues selection are given in [10.14] as the
disturbances in the form of measured signal quantization noise and harmonic noise. It
should be noted that an interesting conclusion about the accuracy of the observer has
been given in [10.14] also. For the closed loop flux observer, the accuracy of the
estimated quantities is determined by the accuracy of the open loop observer model,
observer topology and observer controller gains. The conclusion is that the accuracy of a
closed loop estimator is primarily determined by the accuracy of the measured variables
(sensor and signal conditioning accuracy) and secondarily by the controller design in the
way the controller rejects disturbances.
The extended Kalman filter based speed estimation algorithm is another equivalent
circuit based method, in which a complete mathematical model of the induction machine
is used [10.16]. The Kalman filters have the ability to obtain accurate estimation under
noisy conditions. However, the intensive computation makes this approach less attractive
and difficult for real implementation.
The most popular speed estimation methodology in the first category is based on
Model Reference Adaptive System (MRAS). The MRAS for sensorless control was
firstly proposed by Schauder in [10.17], where the adaptive mechanism of MRAS has
been explained clearly and an error function based on q- and d-axis rotor fluxes has been
proposed. Popov’s criterion has been used to prove the stability of the adaptive algorithm.
A similar method was presented in [10.18] with discussion of flux observer design. The
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