Work place: Department of Electrical Engineering, Boushehr Branch, Islamic Azad University, Boushehr, Iran
E-mail: a.gavahian.j@gmail.com
Website:
Research Interests: Artificial Intelligence, Robotics, Process Control System, Data Structures and Algorithms, Analysis of Algorithms, Combinatorial Optimization
Biography
Atefeh Gavahian is an Electrical/Electronic researcher of research and development company SSP. Co. In 2010 she is jointed the research and development company, SSP Co, Shiraz, Iran. She is the main author of more than 6 scientific papers in refereed journals. Her main areas of research interests are nonlinear control, artificial control system, robotics, evolutionary optimization algorithms, and automation.
By Amin Jalali Farzin Piltan Atefeh Gavahian Meysam Jalali Mozhdeh Adibi
DOI: https://doi.org/10.5815/ijieeb.2013.01.08, Pub. Date: 8 May 2013
The main purpose of this paper is to design a suitable control scheme that confronts the uncertainties in a robot. Sliding mode controller (SMC) is one of the most important and powerful nonlinear robust controllers which has been applied to many non-linear systems. However, this controller has some intrinsic drawbacks, namely, the chattering phenomenon, equivalent dynamic formulation, and sensitivity to the noise. This paper focuses on applying artificial intelligence integrated with the sliding mode control theory. Proposed adaptive fuzzy sliding mode controller optimized by Particle swarm algorithm (AFSMC-PSO) is a Mamdani’s error based fuzzy logic controller (FLS) with 7 rules integrated with sliding mode framework to provide the adaptation in order to eliminate the high frequency oscillation (chattering) and adjust the linear sliding surface slope in presence of many different disturbances and the best coefficients for the sliding surface were found by offline tuning Particle Swarm Optimization (PSO). Utilizing another fuzzy logic controller as an impressive manner to replace it with the equivalent dynamic part is the main goal to make the model free controller which compensate the unknown system dynamics parameters and obtain the desired control performance without exact information about the mathematical formulation of model.
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