An adaptive neuro fuzzy inference system control for a bearingless induction motor based on a disturbance observer

Abstract

There is some poor performance regarding controlling capacity of the bearing-less induction motor (BIM) when there are deviations in the parameters, outer disturbances and changes in the loads. So to solve this issue design of an adaptive exponential sliding-mode (AESM) controller and an observer for extended SM disturbance for finding system disturbance variables while operating are done. This adaptive exponential control is explained by combining order one norm and switching function law into regular control strategy. We can adjust the conjuction speed time adaptively as per variation of the SM switch surface and the system status. The controller used in this control strategy is Adaptive Neuro-Fuzzy Inference System (ANFIS). The observer used senses the speed and outer disturbances of the bearing-less induction motor. As feed forward contribution for system speed, the response of DSMO is utilized. The disturbance in the motor can be reduced by adjusting error in the speed by this feedback speed. From simulation output it can be seen that proposed system with ANFIS control strategy has good strength to control disturbances and to find the uncertain disturbances accurately. Hence the controlling capacity of the bearing-less induction motor (BIM) when there are deviations can be improved by using this proposed system.