Enhanced Performance of Indirect Vector Controlled Induction Motor Drive with a Modified Type 2 Neuro-Fuzzy Torque Controller in Interfacing with dSPACE DS-2812

Abstract

Due to the nonlinearities of the PI controller, the performance of the PI controller is not satisfactory. The gains must be properly selected after changes in control parameters is one of the issues of the PI controller. The modified type 2 Neuro-Fuzzy torque controller of indirect vector control-based induction motor drive is proposed in this paper by taking single input as an error i.e. speed and torque against two inputs error and change in error of conventional T2NFC.The superiority of fuzzy and neural networks has been utilized by T2NFC as type 2 MF’s consist of fuzzy and FOU. The intersection point of the membership function is smaller so that the value of the centroid method is more precise than the T1NFC. The induction motor parameters, such as stator phase current, speed, and torque of the proposed T2NFC are simulated in MATLAB at different operating conditions and compared with PI, T1NFC controllers. The proposed T2NFC significantly minimizes the ripples in torque of the induction motor in comparison with PI and T1NF controllers. The practical implementation is also carried out with a 3.7 KW induction motor using DSP 2812 controller to analyse induction motor parameters in real-time.