AC motor drives are widely used in a multitude of industrial and process
applications requiring high performances. In high-performance drive systems, the motor speed
should closely follow a specified reference trajectory regardless of any load
disturbances, parameter variations and model uncertainties. In order to achieve high performance,
field oriented control of induction motor (Blaschke,
1972) drive is employed. However, the controller design of such a system plays a crucial role in performance. The
decoupling characteristics of vector-controlled IM are adversely affected by the parameter
changes in the motor. The motor-control issues are traditionally handled by fixed-gain
Proportional-Integral (PI) and Proportional-Integral-Derivative (PID) controllers. However, the
fixed-gain controllers are very sensitive to parameter variations, load disturbances, etc.
Thus, the controller parameters have to be continually adapted. The problem can be solved
by several adaptive control techniques such as Model Reference Adaptive Control
(MRAC) (Sugimoto and Tamai, 1987), Sliding Mode Control (SMC)
(Won and Bose 1992), Variable Structure Control (VSC)
(Chern and Wu, 1991), and self-tuning PI controllers
(Hung, 1994), etc. The design of all of these controllers depends on the exact system
mathematical model. However, it is often difficult to develop an accurate system mathematical
model due to unknown load variation, and unavoidable parameter variations by
saturation, temperature change, and system disturbances. In order to overcome the above
problems, the Fuzzy Logic Controller (FLC) is being recently used for motor control
(Tang and Xu, 1994; Tony and El-Sharkaur, 1996; Mao-Fu et al., 1997; and Mahesh Kumar et al.,
2007).
FLC yields superior and faster results, without the need of an accurate
mathematical model of the plant and works well for complex nonlinear, multidimensional system
with parameter variation problem or where the sensor signals are not precise. The fuzzy
control is adaptive in nature and gives robust performance. The main design problem lies in
the determination of the consistent and complete rule set and the shape of the
membership functions. A lot of modifications, trial and error have to be carried out to obtain the
desired response, which is time consuming. A new design technique, that is, Adaptive
Neuro-Fuzzy Inference System (ANFIS) (Grabowski et al., 2000), is used for the FLC design. Neuro-fuzzy software tools work as an intelligent assistant to the design. It helps
to generate and optimize membership functions as well as the rule base from the
simple data provided. Combining the learning power of neural network with
knowledge representation of fuzzy logic gives advantage to neuro-fuzzy system. |
