Combining the priniciples of minimum switching loss PWM algorithm and ANFIS for vector controlled induction motor drives

A high performance electrical drive requires suitable control method, even when the load and parameters of the motor are varying during the motion. This paper proposes Adaptive Neural Fuzzy Inference Systems (ANFIS) controller for minimum switching loss PWM algorithm based vector controlled induction motor drives. In the conventional space vector PWM (CSVPWM) algorithm, the zero voltage applying time is distributed equally in every sampling interval. The proposed algorithms use the concept of division of zero state. By varying the zero state time, various discontinuous PWM (DPWM) algorithms are generated and from which minimum switching loss PWM (MSLPWM) algorithm has been developed. Moreover, to reduce the complexity involved in the conventional space vector approach, the proposed PWM algorithms are developed by using the concept of imaginary switching times. By analyzing the switching loss characteristics, the minimum switching loss PWM algorithms are developed for induction motor drives. The theoretical evaluation is validated through the numerical simulation studies.