Snejana Yordanova

Work place: Technical University of Sofia, Faculty of Automation, Sofia 1000, Bulgaria



Research Interests: Neural Networks, Computer Networks, Network Architecture, Logic Calculi, Logic Circuit Theory


Snejana Yordanova was born in Sofia, Bulgaria in 1953. She received her M.Sc. degree in 1978 and Ph.D in 1986 both in process control from the Technical University of Sofia, Bulgaria.

She joined the Process Control department of the Faculty of Automation at the Technical University of Sofia in 1980. From 1998 she is an Associate Professor and from 2011 till now – a full time Professor.

Her research and academic interests and experience are application of robust, fuzzy logic, neural network and genetic algorithms approaches for modelling and control of plants mainly from ecology, power engineering, air-conditioning, and oil-refining. She has published one monographical book [22], one book chapter, and is the author and co-author of 15 texbooks and over 120 papers most of which in prestigeous reviewed journals and some with impact factor (IEEE TFS, JIFS, etc.). She has 5 Ph.D students and over 30 industrial and academic projects. She is in the editorial board of a number of journals and in the organizing committee of international conferences. She is reviewing for journals (IEEE TFS, APIN, J.Control Sc&Eng, etc.) and conferences and is a member of several automation societies - WSEAS, Union of Automatica and Informatics, etc. Her plenary paper “Design of robust fuzzy logic controllers for complex non-linear processes with time delay“ at the 8th WSEAS Int. Conf. on Artificial Intelligence, Knowledge Engineering and Data Bases – AIKED’09, Cambridge, UK, 21-23 Feb., 2009 was nominated as the Best paper Award.

Author Articles
Intelligent Approaches to Real Time Level Control

By Snejana Yordanova

DOI:, Pub. Date: 8 Sep. 2015

Liquid level control is important for ensuring energy and material balance in many installations but it also difficult as the plant is nonlinear, inertial and with model uncertainties. Fuzzy logic controllers (FLCs) are successfully applied to ensure system stability and robustness by simple means and a model-free design. This paper suggests a procedure for off-line tuning of the many FLC parameters based on optimization of a suggested multi-objective function defined on several system performance indices using genetic algorithms (GAs). First, a model-free FLC is empirically tuned, then applied for real time control of the plant and the necessary data recorded and used to GA parameter optimize a TSK plant model of an accepted structure. The validated on different set of experimental data model is employed in FLC closed loop system simulation experiments to evaluate the fitness function in the GA optimization of the FLC pre-processing and post-processing parameters. The procedure is applied for the real time PI/PID FLC level control in a laboratory-scale tank system. The improvement of the system performance indices due to the GA optimization is estimated in level real time control.

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