COMPUTING AMBIGUITY IN COMPLEX SYSTEMS WITH FUZZY LOGIC

2009 ◽  
Vol 14 (01) ◽  
Author(s):  
Luca Iandoli ◽  
Elio Marchione ◽  
Cristina Ponsiglione ◽  
Giuseppe Zollo
Keyword(s):  
Fuzzy Logic ◽  
Complex Systems

Coupling Mechanistic Approaches and Fuzzy Logic to Model and Simulate Complex Systems

2020 ◽  
Vol 28 (8) ◽  
pp. 1748-1759 ◽  
Author(s):  
Simone Spolaor ◽  
Marco S. Nobile ◽  
Giancarlo Mauri ◽  
Paolo Cazzaniga ◽  
Daniela Besozzi
Keyword(s):  
Fuzzy Logic ◽  
Complex Systems

scholarly journals Anti-Swing Fuzzy Controller Applied in a 3D Crane System

2012 ◽  
Vol 2 (2) ◽  
pp. 196-200 ◽  
Author(s):  
D. Antic ◽  
Z. Jovanovic ◽  
S. Peric ◽  
S. Nikolic ◽  
M. Milojkovic ◽  
...  
Keyword(s):  
Fuzzy Logic ◽  
Complex Systems ◽  
Mathematical Models ◽  
Fuzzy Controller ◽  
Main Difficulty ◽  
Trial And Error ◽  
Nonlinear Mathematical Models ◽  
Highly Nonlinear ◽  
Experimental Laboratory ◽  
Input Variables

It is well known that fuzzy logic can be used in the control of complex systems described by highly nonlinear mathematical models. However, the main difficulty in the design of a fuzzy controller comes with the adjustment of the controller’s parameters that are usually determined by human experts’ knowledge or trial and error methods. In this paper, we describe an implementation of fuzzy logic in order to reduce oscillations during the positioning of a 3D crane system. The fuzzy controller’s structure is quite simple, requiring only two input variables. The proposed fuzzy controller has been applied to an experimental laboratory framework and results show that oscillations are significantly reduced.

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