Artificial Neural Network Controller for Induction Motor Drive

Induction motors are being applied today to a wider range of applications requiring variable speed. Generally, variable speed drives for Induction Motor require both wide operating range of speed and fast torque response, regardless of any disturbances and uncertainties (like load variation, parameters variation and un-modeled dynamics). This leads to more advanced ANN control methods to meet the real demand. The recent advances in the area of field-oriented control along with the rapid development and cost reduction of power electronics devices and microprocessors have made variable speed induction motor drives an economical alternative for many industrial applications. These AC drives are nowadays replacing their DC counterpart and are becoming a major component in today-s sophisticated industrial manufacturing and process automation. Advent of high switching frequency PWM inverters has made it possible to apply sophisticated control strategies to AC motor drives operating from variable voltage, variable frequency source. In the formulation of any control problem there will typically be discrepancies between the actual plant and the mathematical model developed for controller design. This mismatch may be due to un-modeled dynamics, variation in system parameters or the approximation of complex plant behavior by a straightforward model. The designer must ensure that the resulting controller has the ability to produce required performance levels in practice despite such plant/model mismatches. In this dissertation report, a sliding mode controller is designed for an induction motor drive. The gain and band width of the controller is designed considering rotor resistance variation, model in accuracies and load disturbance, to have an ideal speed tracking. The chattering effect is also taken into account. The controller is simulated under various conditions and a comparative study of the results with that of ANN controller has been presented.