| The squirrel-cage induction motor has various inherent advantages not present in other types of ac motors, and is widely used in the industry. Its usage is expected to go up because of possible applications like electric vehicles, which require a light and efficient motor drive. However, the induction motor has a complex and non-linear structure which makes precise control a complicated and expensive process. Added to this complexity is the fact that the motor parameters undergo a variation during regular operation, chiefly due to a change in temperature and nonlinear magnetic characteristics. This variation reduces the efficacy of the control technique, though its effect can be mitigated with the help of robust control techniques. Also, most control techniques require speed feedback from a shaft encoder and these devices have various disadvantages and are considered undesirable for a number of applications. Thus, present day research in this area is mostly focussed on obtaining speed sensorless and robust induction motor drives.; Artificial neural networks (ANNs) have shown great promise in image processing and control applications where robustness is desirable. However, these are at the stage of infancy in the area of induction motor control. The ability of ANNs to map arbitrary nonlinear functions has been used to advantage by many researchers. The motivation behind this work was to investigate the possibility of using ANNs to eventually come up with an ANN based sensorless induction motor drive. This central idea was broken down into two major components---speed estimation of induction motors using ANNs, and control of induction motors using ANNs. Both these areas have attracted attention in recent years, though very little work has been done so far. Because of the complexity of the problem, researchers have been unable to come up with a satisfactory solution.; This work makes an important contribution to the area of induction motor drives, by presenting for the fast time, off-line trained ANN speed estimators. Using the d-q axis dynamic equations of the squirrel-cage induction motor, four methods are proposed whereby an ANN is trained off-line to estimate the speed of the motor. The results presented in the thesis indicate that the proposed schemes are able to track the speed under load variations. The effectiveness and superiority of the fourth method is further demonstrated under vector control conditions in the presence of an inverter. This method has also been experimentally verified.; A novel strategy for control of induction motors using just one off-line trained ANN is also presented. (Abstract shortened by UMI.)... |
