1 Introduction
Recently, field-oriented methods [l, 21 have been used in
the design of induction motor drives for high-performance
applications. With these control approaches, the dynamic
behaviour of the induction motor is rather like that of a
separately excited DC motor. However, in the field-oriented
method, the decoupled relationship is obtained by
means of a proper selection of state coordinates under the
assumption that the rotor flux is kept constant. Therefore,
the rotor speed is only asymptotically decoupled from rotor
flux, and the speed is linearly related to torque current only
after the rotor flux reaches the steady-state value. Besides,
the control performance of the induction motor is still
influenced by the plant parameter variation and external
disturbance.
To deal with these uncertainties, much research has been
done in recent years to apply various approaches in the
control field. Conventional proportional-integral-derivative
(PID)-type controllers are popular in industry for their
simple control structure, ease of design, and low cost.
However, the PID-type controller cannot provide perfect
control performance if the controlled plant is highly nonlinear
and uncertain [3]. On the other hand, intelligent control
techniques (fuzzy control or neural-network control) have
been adopted to control induction servo motors, using their
powerful learning ability, without prior knowledge of the
controlled plant in the design process [4-81. Zhen and Xu
[4] proposed a fuzzy model reference learning control technique
for an indirect field-oriented induction machine drive.
Chao and Liaw [6] developed a fuzzy robust controller to
preserve the prescribed response of a detuned indirect field-oriented
induction motor drive. However, the stability of
these control strategies cannot be guaranteed, and their
control structures are more complex than that of the
conventional PID controller. Brdys and Kulawski [8]