Marjan Mirshekari

Work place: Research and Development Department, Institute of Advance Science and Technology-SSP, Shiraz/Iran



Research Interests: Artificial Intelligence, Robotics, Process Control System


Marjan Mirshekari is currently working as a co researcher in Control and Robotic Lab at the institute of advance science and technology, IRAN SSP research and development Center. She is a Master in field of Computer Software Engineering from Islamic Azad University, IRAN. Her current research interests are in the area of nonlinear control, artificial control system and robotics.

Author Articles
Design Minimum Rule-Base Fuzzy Inference Nonlinear Controller for Second Order Nonlinear System

By Masoud Mokhtar Farzin Piltan Marjan Mirshekari Alireza Khalilian Omid Avatefipour

DOI:, Pub. Date: 8 Jun. 2014

This research is focused on proposed minimum rule base PID computed torque algorithms with application to continuum robot manipulator. The stability of the closed-loop system is proved mathematically based on the Lyapunov method. Classical Computed Torque Controller (CTC) is robust to control model partly uncertainties and external disturbances. This controller is one of the significant nonlinear methodologies; according to the nonlinear dynamic formulation. One of the main targets in this research is increase the robustness based on the artificial intelligence methodology. Classical computed torque control has difficulty in handling unstructured model uncertainties. One can overcome this problem by combining a computed torque controller and artificial intelligence (e.g. fuzzy logic). To approximate a time-varying nonlinear dynamic system, a fuzzy system requires a large amount of fuzzy rule base. This large number of fuzzy rules will cause a high computation load. To reduce the number of rule base this research is focused on the PD like fuzzy plus integral methodology. This method is applied to continuum robot manipulator to have the best performance.

[...] Read more.
Design Intelligent Model base Online Tuning Methodology for Nonlinear System

By Ali Roshanzamir Farzin Piltan Narges Gholami mozafari Azita Yazdanpanah Marjan Mirshekari

DOI:, Pub. Date: 8 Apr. 2014

In various dynamic parameters systems that need to be training on-line adaptive control methodology is used. In this paper fuzzy model-base adaptive methodology is used to tune the linear Proportional Integral Derivative (PID) controller. The main objectives in any systems are; stability, robust and reliability. However PID controller is used in many applications but it has many challenges to control of continuum robot. To solve these problems nonlinear adaptive methodology based on model base fuzzy logic is used. This research is used to reduce or eliminate the PID controller problems based on model reference fuzzy logic theory to control of flexible robot manipulator system and testing of the quality of process control in the simulation environment of MATLAB/SIMULINK Simulator.

[...] Read more.
Design PID Baseline Fuzzy Tuning ProportionalDerivative Coefficient Nonlinear Controller with Application to Continuum Robot

By Azita Yazdanpanah Farzin Piltan Ali Roshanzamir Marjan Mirshekari Narges Gholami mozafari

DOI:, Pub. Date: 8 Apr. 2014

Continuum robot manipulators are optimized to meet best trajectory requirements. Closed loop control is a key technology that is used to optimize the system output process to achieve this goal. In order to conduct research in the area of closed loop control, a control oriented cycle-to-cycle continuum robot model, containing dynamic model information for each individual continuum robot manipulator, is a necessity. In this research, the continuum robot manipulator is modeled according to information between joint variable and torque, which is represented by the nonlinear dynamic equation. After that, a multi-input-multi-output baseline computed torque control scheme is used to simultaneously control the torque load of system to regulate the joint variables to desired levels. One of the most important challenge in control theory is on-line tuning therefore fuzzy supervised optimization is used to tune the modified baseline and computed torque control coefficient. The performance of the modified baseline computed torque controller is compared with that of a baseline proportional, integral, and derivative (PID) controller.

[...] Read more.
Other Articles