Shahnaz TayebiHaghighi

Work place: Control and Robotic Lab, IRAN SSP Research and Development Center, Shiraz, Iran



Research Interests: Control Theory, Process Control System


Shahnaz Tayebihaghighi is a research associate at the IRANSSP research and development center. She has published nine peer reviewed research articles and three books. Her research areas are nonlinear model-reference control, artificial intelligence, and system modeling.

Author Articles
Adaptive Finite-Time Convergence Fuzzy ARX-Laguerre System Estimation

By Farzin Piltan Shahnaz TayebiHaghighi Amirzubir Sahamijoo Hossein Rashidi Bod Somayeh Jowkar Jong-Myon Kim

DOI:, Pub. Date: 8 May 2019

Convergence speed for system identification and estimation is a popular topic for determining the kinematics and dynamic identification/estimation of the parameters of robot manipulators. In this paper, adaptive fuzzy inverse dynamic system estimation is used to improve robust modeling, especially for a serial links robot manipulator. The Lyapunov technique is used to analyze the convergence rate of the tracking error and increase the accuracy response of the parameter estimation. Performance of robot estimation is conducted, and the results show fast convergence of the proposed finite time technique for a 6-DOF robot manipulator.

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A Novel Intelligent ARX-Laguerre Distillation Column Estimation Technique

By Farzin Piltan Shahnaz TayebiHaghighi Somayeh Jowkar Hossein Rashidi Bod Amirzubir Sahamijoo Jeong-Seok Heo

DOI:, Pub. Date: 8 Apr. 2019

In practical applications, modeling of real systems with unknown parameters such as distillation columns are typically complex. To address issues with distillation column estimation, the system is identified by a proposed intelligent, auto-regressive, exogenous-Laguerre (AI-ARX-Laguerre) technique. In this method, an intelligent technique is introduced for data-driven identiļ¬cation of the distillation column. The Laguerre method is used for the removal of input/output noise and decreases the system complexity. The fuzzy logic method is proposed to reduce the system’s estimation error and to accurately optimize the ARX-Laguerre parameters. The proposed method outperforms the ARX and ARX-Laguerre technique by achieving average estimation accuracy improvements of 16% and 9%, respectively.

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Control of an Uncertain Robot Manipulator Using an Observation-based Modified Fuzzy Sliding Mode Controller

By Shahnaz TayebiHaghighi Farzin Piltan Jong-Myon Kim

DOI:, Pub. Date: 8 Mar. 2018

The main contribution of this paper is the design of a robust model reference fuzzy sliding mode observation technique to control multi-input, multi-output (MIMO) nonlinear uncertain dynamical robot manipulators. A fuzzy sliding mode controller was used in this study to control the robot manipulator in the presence of uncertainty and disturbance. To address the challenges of robustness, chattering phenomenon, and error convergence under uncertain conditions, the proposed sliding mode observer was applied to the fuzzy sliding mode controller. This theory was applied to a six-degrees-of-freedom (DOF) PUMA robot manipulator to verify the power of the proposed method.

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Comparative Study between ARX and ARMAX System Identification

By Farzin Piltan Shahnaz TayebiHaghighi Nasri B. Sulaiman

DOI:, Pub. Date: 8 Feb. 2017

System Identification is used to build mathematical models of a dynamic system based on measured data. To design the best controllers for linear or nonlinear systems, mathematical modeling is the main challenge. To solve this challenge conventional and intelligent identification are recommended. System identification is divided into different algorithms. In this research, two important types algorithm are compared to identifying the highly nonlinear systems, namely: Auto-Regressive with eXternal model input (ARX) and Auto Regressive moving Average with eXternal model input (Armax) Theory. These two methods are applied to the highly nonlinear industrial motor.

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