f Improving Robustness and Dynamic Performance of Sensor less Vector-Controlled IM Drives with ANFIS-Enhanced MRAS
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Research Article |

Improving Robustness and Dynamic Performance of Sensor less Vector-Controlled IM Drives with ANFIS-Enhanced MRAS

Author(s): Govindharaj I*, Rampriya R, Balamurugan S, Yazhinian S, Dinesh Kumar K and Anandh R

Published In : International Journal of Electrical and Electronics Research (IJEER)        Volume 12, Issue 3

Publisher : FOREX Publication

Published : 20 August 2024

e-ISSN : 2347-470X

Page(s) : 975-980

DOI: https://doi.org/10.37391/IJEER.120331

Abstract

The Model Reference Adaptive System (MRAS) enables effective speed control of sensorless Induction Motor (IM) drives at zero and very low speeds. This study aims to enhance the resilience and dynamic performance of MRAS by integrating an Adaptive Neuro-Fuzzy Inference System (ANFIS) controller into sensorless vector-controlled IM drives. To address issues related to parameter uncertainties, load variations, and disturbances, the combination of MRAS and ANFIS is investigated. The ANFIS controller improves the dynamic performance by adapting its parameters based on the error between estimated and measured rotor speeds. This allows for better tracking of the reference speed and smoother drive operation. The proposed MRAS scheme with ANFIS reduces the sensitivity of the sensorless control system to parameter variations, such as changes in motor parameters or load torque, thereby enhancing stability. The primary goal of this system is to maintain stability and reduce the impact of parameter variations on the sensorless control system. The performance of the proposed system is evaluated using the MATLAB platform and compared with existing systems. Results indicate that the ANFIS-enhanced MRAS offers superior dynamic performance and robustness, making it a viable solution for applications requiring precise speed control and high reliability.

Keywords: Sensorless Vector, Model Reference Adaptive System, Induction Motor Drive, Adaptive Neuro-Fuzzy Inference System Controller, Neural Networks.




Govindharaj I*, Assistant Professor, Department of Computer Science and Engineering, Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology, Tamil Nadu 600062, India; Email: gvraj87@gmail.com

Rampriya R, Assistant Professor, Department of Computer Science and Engineering (Artificial Intelligence), Madanapalle Institute of Technology & Science, Andhra Pradesh 517325, India

Balamurugan S, Assistant Professor, School of Computer Science and IT, JAIN (Deemed-to-be University), Karnataka 560069, India

Yazhinian S, Associate Professor, Department of Computer Science and Engineering, Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology, Tamil Nadu 600062, India

Dinesh Kumar K, Assistant Professor (Sr. Grade), Department of Computer Science and Engineering, Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology, Tamil Nadu 600062, India

Anandh R, Assistant Professor (Sr. Grade), Department of Artificial Intelligence and Data Science, Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology, Tamil Nadu 600062, India

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Govindharaj I, Rampriya R, Balamurugan S, Yazhinian S, Dinesh Kumar K and Anandh R (2024), Improving Robustness and Dynamic Performance of Sensor less Vector-Controlled IM Drives with ANFIS-Enhanced MRAS. IJEER 12(3), 975-980. DOI: 10.37391/IJEER.120331.