Work place: Department of Computer Science, Islamic Azad University, Shiraz, Iran
E-mail: Mozhdeh_adib@yahoo.com
Website:
Research Interests: Artificial Intelligence, Robotics, Data Structures and Algorithms, Algorithm Design, Programming Language Theory
Biography
Mozhdeh Adibi is a computer engineer researcher. Her research activities deal with the programming, robotics, artificial intelligence, and web design.
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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