Zahra Rahmani

Work place: Faculty of Dept. of electrical and computer engineering, Babol University of Technology, Babol, 47148 -71167, Iran

E-mail: zrahmani@nit.ac.ir

Website:

Research Interests: Computational Science and Engineering, Computational Engineering, Computer systems and computational processes, Engineering

Biography

Zahra Rahmani was born in Tehran, Iran, in 1976. She received B.Sc. degree in Control engineering from Sharif University of Technology, Tehran, Iran in 1998, M.Sc. degree and Ph.D. degree in Control engineering from Iran University of Science and Technology, Tehran, Iran in 2000 and 2007 respectively. She works in the department of electrical and computer engineering of Babol University of Technology as an assistant professor from 2008. Her research interests are Nonlinear, Complex, and Hybrid systems, Intelligent, Nonlinear and Adaptive control methods.

Author Articles
Fuzzy Predictive Control of Step-Down DC-DC Converter Based on Hybrid System Approach

By Morteza Sarailoo Zahra Rahmani Behrooz Rezaie

DOI: https://doi.org/10.5815/ijisa.2014.02.01, Pub. Date: 8 Jan. 2014

In this paper, a fuzzy predictive control scheme is proposed for controlling output voltage of a step-down DC-DC converter in presence of disturbance and uncertainty. The DC-DC converter is considered as a hybrid system and modeled by Mixed Logical Dynamical modeling approach. The main objective of the paper is to design a Fuzzy Predictive Control to achieve desired voltage output without increasing complexity of the hybrid model of DC-DC converter in various conditions. A model predictive control is designed based on the hybrid model and applied to the system. Although the performance of the model predictive control method is satisfactory in normal condition, it suffers from lack of robustness in presence of disturbance and uncertainty. So, to succeed in facing up to the problem a fuzzy supervisor is utilized to adjust the main predictive controller based on the measured states of the system. In this paper it is shown that the proposed fuzzy predictive control scheme has advantages such as simplicity and efficiency in normal operation and robustness in presence of disturbance and uncertainty. Through simulations effectiveness of the proposed method is shown.

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