Direct Torque Control (DTC) is the dominant strategy used in three-phase
induction motor control, thanks to its excellent and vibrant characteristics, consistent
operation, fewer mathematical calculations, and rigidity in adjustable velocity drives.
However, torque ripple is the main drawback of DTC, and it is challenging to reduce it.
While DTC based conventional PID controller is utilized, it gets pretentious by lengthy
settling time, maximum peak overshoot, and torque and speed curve oscillations. The
current research aims to diminish the torque ripple and augment the DTC-enabled
induction motor drive performance. Various control methods, such as Fuzzy Logic
Control (FLC), Artificial Neural Network (ANN), and Adaptive Neuro-Fuzzy
Inference System (ANFIS), were used in the chapter to enhance the DTC-enabled
induction motor drive performance. These control methods were carefully verified and
simulated under MATLAB/Simulink 2017. The effectiveness of the projected work
was confirmed through simulation, which achieved promising results, thus establishing
the supremacy of the proposed model.
Keywords: Adaptive Neuro Fuzzy Inference System, Direct Torque Control, Fuzzy Logic Controller, Neural Network Controller, PID controller.
