Fadhil A. Hassan

Work place: Department of Electrical Engineering, University of Technology, Baghdad, Iraq

E-mail: 30077@uotechnology.edu.iq


Research Interests: Computational Engineering, Engineering


Fadhil A. Hasan was born in Baghdad, Iraq in February 17, 1970. He received the B.Sc. from the university of Al-mustansiryah, Baghdad, Iraq, in 1991, in electrical engineering. And he received the  M.Sc. and Ph.D. degrees from University of Technology, in 2008 and 2017 respectively, in electrical machine and control engineering.

In 2008, he joined the department of electrical engineering at university of technology, Baghdad, in Iraq, as an Asst. Lecturer. Currently he is a lecturer in the department of electrical engineering at university of technology, Baghdad, in Iraq. He has published over 16 refereed journal and conference papers in the areas of induction heating, control systems, power electronics and electrical machine.

Author Articles
Artificial Neural Estimator and Controller for Field Oriented Control of Three-Phase I.M.

By Lina J. Rashad Fadhil A. Hassan

DOI: https://doi.org/10.5815/ijisa.2019.06.04, Pub. Date: 8 Jun. 2019

Speed control for an I.M is a few what complex strategies; the complexity is regularly increasing in line with the required system achievement. The main forms of control strategies are scalar, direct torque, adaptive, sensorless, and vector or Field Oriented Control (FOC). The FOC method is the most efficient technique in which machine parameters: Rotor flux, unit vector, and electromagnetic torque, usually are estimated by means of using Digital Signal Processing (DSP). The Artificial Neural Network (ANN) becomes an effective tool for controlling nonlinear device in present time. This paper proposes the using of ANN instead of DSP to estimate the machine parameters in order to reduce the hardware complexity and the Electromagnetic Interference (EMI) impact. Also, it presents the PI-NN controller which is based totally on ANN. The systems simulations for both DSP and ANN are depicted. The performance of the ANN-based system gives excellent results: overshot less than 0.5%, rise time 0.514 s, steady state error less than 0.2%, settling time 0.7 s. in conjunction with that of DSP-based performance: overshot about 2%, rise time 0.64 s, steady state error less than 0.4%, settling time 0.75 s.

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