A type 2 Neuro-Fuzzy torque controller for indirect vector control (IVC) based induction motor (IM) driving is presented in this work. In various operating modes, a linear fixed-gain proportional-integral (PI) based speed controller is employed in indirect vector control of an IM drive (IMD). To achieve high performance, the PI controller (PIC) requires precise and accurate gain parameters. The PIC gain values have been tuned for a specific operating point and may not perform satisfactorily when the load torque and operating point change. To enhance dynamic performance over a wide speed range and reduce load torque ripple, the PIC is replaced by a Type-1 neuro-fuzzy logic controller (T1NFC). The T1FLC is simple, easy to use, and successful at dealing with nonlinear control systems without the use of complex mathematical equations. Instead, it relies on simple logical rules that are decided by an expert. The T1NFC is replaced by a Type-2 neuro-fuzzy logic controller to enhance controller performance. Owing to the availability of three-dimensional control with type-reduction technique (i.e. Type-2 fuzzy sets and Type-2 reducer set) in the defuzzification process, the T2NFC effectively handles the large footprint of uncertainties compared to the T1NFC, whereas the T1NFC have only Type-1 fuzzy sets and a single membership function. The T2NFC using MATLAB Simulink is used to observe the induction motor performance characteristics like, stator phasor current, torque, and speed under various operating situations. T2NFC controllers provide better driving performance characteristics than PI and T1NFC controllers. When associated to the PI and T1NFC controllers, the suggested T2NFC greatly reduce the amount of ripple in the torque and stator current of the IM drive. Practical validation is also performed with a 3.7 KW IM drive and a DSP 2812 controller for real-time examination of the drive parameters.
Zooming fuzzy logic controller for sensorless vector control of an induction motor in low speed region under 3Hz
