Robust intelligent control of a speed sensorless induction motor

Abstract

AbstractThis paper reports results of recent applications of intelligent control techniques to a field oriented induction machine. Induction motors are characterised by complex, highly non‐linear and time‐varying dynamics and inaccessibility of some states and outputs for measurements. The advent of vector control techniques has partially solved induction motor control problems because they are sensitive to drive parameter variations and performance may deteriorate if conventional controllers such as PID are used. With fuzzy logic and neural networks based control‐lers, uncertainties in the plant parameters and non‐linearities can be dealt with more efficiently than model‐based techniques. However, a commonly known disadvantage of these methods is the lack of systematisarion and rigorous design tools. Hybrid control architectures, combining control theory with artificial intelligent tools can solve efficiently complex control problems. Three control approaches are developed and applied to adjust the speed of the drive system. The first control design combines the variable structure theory with fuzzy logic concept. In the second approach neural networks are used in an internal model control structure. Finally, a fuzzy state feedback controller is developed based on pole placement technique. A simulation study of these methods is presented. The effectiveness of these controllers is demonstrated for different operating conditions of the drive system.