Session Co-Chairs:
Prof. Riccardo Caponetto, University of Catania, Italy
Prof. Maria Gabriella Xibilia, University of Messina, Italy
Prof. Arturo Buscarino, University of Catania, Italy

Session description:
The impact of fractional order circuits and systems over a wide range of fields is rapidly becoming evident. Fractional order models, in fact, appear to be more accurate in reproducing the behavior of physical processes than classical integer order models. Examples can be found in rheology, mechanics, chemistry, physics, bioengineering, robotics and many others scientific fields. As a consequence, a large literature describing the advantages of fractional calculus has been introduced in the last few decades.
At the same time, fractional integrals and derivatives are also applied to the theory of control of dynamical systems, when the controlled system and/or the controller is described by fractional differential equations.
The main goal of this Special Session is to present timely and novel applications and implementations of fractional order circuits and systems. Modelling issues related to real-life cases will be deeply considered, as well as fractional order controller theory and realization. Examples of biomedical fractional order models will be also considered, highlighting their fundamental importance in the definition of new healthcare systems. Aspects related to the design, implementation and application of
fractional order controllers will be addressed. Papers from the field of fractional order neural networks will be also appreciated.

The Special Session will benefit of the tight connection between researchers based on the COST Action CA15225 Fractional Systems involving universities and research groups from the whole European territory and their collaborations with international universities.

The topics of interest include, but are not limited to:
 Theory of fractional calculus
 Fractional order linear and nonlinear systems modeling
 Fractional order control
 Fractional order linear and nonlinear systems implementations
 Applications of fractional order modeling
 Fractional order neural networks