This paper presents a new sensorless vector controlled induction motor drive robust against rotor resistance variation. Indeed, the speed and rotor resistance are estimated using extended Kalman filter （EKF）. Then, ...This paper presents a new sensorless vector controlled induction motor drive robust against rotor resistance variation. Indeed, the speed and rotor resistance are estimated using extended Kalman filter （EKF）. Then, we introduce a new fuzzy logic speed controller based on learning by minimizing cost function. This strategy is based on a topology control self-organized and an algorithm for modifying the knowledge base of fuzzy corrector. The learning mechanism addresses the con- sequences of corrector rules, which are modified according to the comparison between the current speed of machine and an output signal or a desired trajectory. Thus, fuzzy associative memory is constructed to meet the criteria imposed in problems either control or pursuit. The consequent algorithm updating consists of a regulator mechanism allowing a fast and robust learning without unnecessarily compromising the control signal and steady- state performance. The performance of this new strategy is satisfactory, even in the presence of noise or when there are variations in the parameters of induction motor drive.展开更多
文摘This paper presents a new sensorless vector controlled induction motor drive robust against rotor resistance variation. Indeed, the speed and rotor resistance are estimated using extended Kalman filter （EKF）. Then, we introduce a new fuzzy logic speed controller based on learning by minimizing cost function. This strategy is based on a topology control self-organized and an algorithm for modifying the knowledge base of fuzzy corrector. The learning mechanism addresses the con- sequences of corrector rules, which are modified according to the comparison between the current speed of machine and an output signal or a desired trajectory. Thus, fuzzy associative memory is constructed to meet the criteria imposed in problems either control or pursuit. The consequent algorithm updating consists of a regulator mechanism allowing a fast and robust learning without unnecessarily compromising the control signal and steady- state performance. The performance of this new strategy is satisfactory, even in the presence of noise or when there are variations in the parameters of induction motor drive.
