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Snejana T. Yordanova

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

E-mail: sty@tu-sofia.bg

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Biography

Snejana T. Yordanova, (1953, Sofia) – MEng. Dr. DSc. in Process Control, Professor.
Graduates from the Technical University of Sofia (1977), Dr. with dissertation "Study of a Class of Multivariable Systems with Reduced Sensitivity" (1986), DSc (2018) with dissertation "Intelligent Process Control Systems Based on Fuzzy Logic". Professional career: a Research fellow at the Department of Industrial Automation at the Faculty of Automatics (1980), an Assistant Professor (1983), an Associate Professor (1998), Professor (2011).
Main research and academic areas: elements of industrial automation, system modelling and simulation, process control, multivariable and robust control systems, intelligent control systems based on fuzzy logic, artificial neural networks, and genetic algorithms. Main industrial application areas - power energy, environmental technologies, petrol and chemical industry, e-learning, dairy processing, measurement systems, heating, ventilation, and air conditioning systems, etc. An author and co-author of 15 textbooks and manuals, over 130 journal and conference papers most with Thomson Reuters JIF and Scopus JSR and with over 250 citations, h-index of 9 in Scopus, a monographic book and a book chapters (Springer, 2016), 5 patents. Awards: Best Plenary Paper Award (Cambridge, 2009), Golden medal of the Technical University of Sofia (2024).

Author Articles
Parameter Optimisation of Type 1 and Interval Type 2 Fuzzy Logic Controllers for Performance Improvement of Industrial Control System

By Desislava R. Stoitseva-Delicheva Snejana T. Yordanova

DOI: https://doi.org/10.5815/ijisa.2026.01.05, Pub. Date: 8 Feb. 2026

The fuzzy logic controllers (FLC) gain popularity in ensuring stable and high-performance control of nonlinear industrial plants with no reliable model, where the traditional controllers fail. Their standard expert-based design and simple algorithms that meet the demands for fast execution and economical use of computational resources ease their implementation into programmable logic controllers for wide industrial real-time control applications. This research presents a novel approach to enhancing the performance of FLC systems by compensating for the subjectivity inherent in expert-based design through optimization of the parameters of type-1 (T1) and interval type-2 (IT2) PID FLC membership functions (MF) using genetic algorithms. The approach is demonstrated for controlling the solution level in a carbonization column for soda ash production. Simulations reveal that optimization improves the system performance, measured by a newly introduced overall performance indicator for dynamic accuracy, robustness, and control smoothness, by 48% for the T1 FLC system and 30% for the IT2 FLC system. No improvement is observed in the substitute of T1 MF by IT2 MF for both the empirically designed and the optimised FLC.

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