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In-Depth Information
1 Introduction
The doubly fed induction machine (DFIM) is a wound rotor asynchronous machine;
this form of drive is widely used in many industrial plants, for example pumps,
compressors and fans. The DFIM has some distinct advantages over the conven-
tional squirrel-cage machine. The DFIM can be fed and controlled from either or
both the stator and the rotor windings. Sub and super-synchronous speeds are
possible and the system can be used in generator or motor operation like a DC
motor (Morel et al.
1998
). In motor operation, two solutions are possible, namely:
the machine can be supplied by one converter (at the rotor) or by two converters
(one at the stator and one at the rotor). The advantage of the
first solution is that the
power electronic equipment only has to handle a fraction (
30 %) of the total
system power. This allows the minimizing of converter size and therefore a
decreased price of the whole system (Morel et al.
1998
). However, the disadvantage
in terms of cost of the second solution can be compensated by the best control
performances of the powered systems (Brown et al.
1992
). In the DFI-Motor
operation, the inherent instability due of the double feeding requires a performing
control to achieve a good stability and to obtain a high dynamic behavior. Different
strategies were proposed in the literature to solve the DFI-Motor control problem.
Most of the control strategies are established on the vector control based on the
*
ux
orientation that offers the decoupled control of the active and reactive powers
(Bogalecka and Kzeminski
1993
; Drid et al.
2005
; Hopfensperger et al.
2000
;
Leonhard
1997
; Morel et al.
1998
; Peresada et al.
2003
,
1999
; Wang and Ding
1993
). Therefore, most of the reported control approaches are based on exact
knowledge of the DFI-Motor nonlinear model. Then, the control performance of the
DFI-Motor is still in
fl
uenced by the uncertainties, such as parameter variations,
external disturbance and unmodeled dynamics, etc.
In electric motor drives and motion control, the fuzzy controller is considered as
a promising alternative for conventional control methods in the control of complex
nonlinear plants (Ghamri et al.
2007
). The fuzzy controller is applied to static power
converters, DC and induction motors. It has been reported that fuzzy controllers are
more robust to system parameter changes and have better disturbance rejection. The
main advantage of fuzzy control as compared to conventional control resides in the
fact that no mathematical model of the plant is required and the human experience
can be implanted in the controller as fuzzy rules. However, classical fuzzy con-
trollers (i.e. the non-adaptive fuzzy controllers) can not adapt themselves to changes
in their environment or in operating conditions. Then, it is necessary to add some
form of adaptation that updates the controller parameters in order to maintain and
improve the control performance in wide range of changing conditions Lee (
1990
);
Li and Lau (
1989
). Using fuzzy systems for approximating of the nonlinear
uncertain functions, adaptive fuzzy controllers for inductions motors (IM) have
been developed in Agamy et al. (
2004
), Lin et al. (
2002
), Youcef and Wahba
(
2009
).
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