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Issue title: Special Section: Intelligent tools and techniques for signals, machines and automation
Guest editors: Smriti Srivastava, Hasmat Malik and Rajneesh Sharma
Article type: Research Article
Authors: Tabrez, Md.a | Bakhsh, Farhad Ilahib; * | Hassan, Mahboobc | Shamganth, K.a | Al-Ghnimi, Samia
Affiliations: [a] Department of Electrical Engineering, Ibra College of Technology, Ibra, Oman | [b] Department of Electrical and Renewable Energy Engineering, SOET, BGSBU, Rajouri, J&K, India | [c] Department of Electrical Engineeirng, Aligarh Muslim University, Aligarh, UP, India
Correspondence: [*] Corresponding author. Farhad Ilahi Bakhsh, Department of Electrical and Renewable Energy Engineering, SOET, BGSBU, Rajouri, J&K, India. E-mail: [email protected].
Abstract: This paper deals with MATLAB/SIMULINK simulation and analysis of a position sensor-less field oriented control of permanent magnet synchronous motor. Adaptive position estimators are required as the parameters of the machines like rotor resistance, inductance changes sometimes. Adaptive position and speed estimators viz. SMO, MRAS are much discussed in literature but the artificial neural network, adaptive neuro-fuzzy inference based estimators are least discussed. In this paper a MATLAB study of MRAS, ANN and ANFIS based position estimator in a Field oriented control of a permanent magnet synchronous motor drive is being done. MRAS, ANN, ANFIS estimators adaptive in nature so these estimators can adapt if there is any parameters change online. The performances of these three drives are analyzed, and results are compared. It is seen that ANFIS based system performance is better even when the parameters of the machines vary with time. This work is limited to analysis and simulation only and could be extended to a practical realization in future work.
Keywords: Field oriented control (FOC) drive, permanent magnet synchronous machine (PMSM), artificial neural network (ANN), model reference adaptive system (MRAS), ANFIS
DOI: 10.3233/JIFS-169801
Journal: Journal of Intelligent & Fuzzy Systems, vol. 35, no. 5, pp. 5177-5184, 2018
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