G. Madhusudhana Rao



Virtual Development of Maximum Torque per Ampere by ANFIS with PI-Based Induction Motor Drive

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This study established an innovative method for controlling the induction motor drive using a neural network with a fuzzy inference (ANFIS) with the help of the modeling of the machine. The suggested technique best considers dynamic reactions, such as those from electric vehicles. The magnetic flux of the rotor is then evaluated for the maximum torque per ampere to produce the required torque at different overshoots and settling parameters of torque and speed of the motor. After this design, the torque flux controller will be improved due to machine saturation. In a non-dynamic induction motor model with a changing field alignment, it is suggested in this Paper that ANFIS-based torque per ampere may lead to the development of a novel torque-flux controller strategy. This technique can boost the stator current while allowing for individuality and free operation. The innovation in some of the earlier contributions connected to this contribution and the current study related to M.T.C. based on vector control with torque ripple reduction strategies via adaptive ANFIS, FLC, and PI for induction motors is described in this paper. To achieve the maximum torque ripple reduction with the new control technique, an adaptive ANFIS controller coupled to the induction motor system is proposed. Additionally covered are comparison results and comparative tables.


Maximum Torque, Induction Motor, Optimization, ANFIS, Fuzzy logic system, flux controller


Cite this paper

G. Madhusudhana Rao. (2023) Virtual Development of Maximum Torque per Ampere by ANFIS with PI-Based Induction Motor Drive. International Journal of Control Systems and Robotics, 8, 11-26


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