TABLE OF CONTENTS In classical control systems, knowledge of the controlled system (plant) is required in the form of a set of algebraic and differential equations, which analytically relate inputs and outputs. However, these models can become complex, rely on many assumptions, may contain parameters which are difficult to measure or may change significantly during operation as in the case of the rotor flux oriented control (RFOC) induction motor drive. Classical control theory suffers from some limitations due to the assumptions made for the control system such as linearity, time-invariance, etc. These problems can be overcome by using artificial intelligence-based control techniques, and these techniques can be used, even when the analytical models are not known. Such control systems can also be less sensitive to parameter variation than classical control systems.
The main advantages of using artificial intelligence-based controllers and estimators are:
Their design does not require a mathematical model of the plant.
They can lead to improved performance, when properly tuned.
They can be designed exclusively on the basis of linguistic information available from experts or by using clustering or other techniques.
They may require less tuning effort than conventional controllers.
They may be designed on the basis of data from a real system or a plant in the absence of necessary expert knowledge.
They can be designed using a combination of linguistic and response-based information.
