Design and control of a DC-DC buck converter using discrete Takagi-Sugeno fuzzy models
DOI:
https://doi.org/10.20998/2074-272X.2025.3.08Keywords:
discrete-time Takagi–Sugeno fuzzy models, DC-DC buck converter, linear matrix inequalitiesAbstract
Introduction. A DC-DC buck converter plays a crucial role in industrial applications by efficiently stepping down voltage levels to power various electronic components and systems. However, controlling a buck converter is challenging due to its inherently nonlinear behavior. This paper presents a novel fuzzy tracking control approach for the buck converter, based on the combination of time-discrete Takagi-Sugeno (T-S) fuzzy models and the concept of virtual desired variables (VDVs). Originality. This paper introduces an innovative fuzzy tracking control that integrates time-discrete T-S models and VDVs concept to develop an efficient digital controller. Goal. The proposed fuzzy control strategy aims to regulate the output voltage regardless of sudden change in setpoint, load variation and change in input voltage. Methodology. The proposed control strategy aims to regulate the output voltage of a DC-DC buck converter. The design starts with a discrete T-S fuzzy controller based on the nonlinear model of the buck converter. A nonlinear tracking controller is developed using a virtual reference model that incorporates the VDVs concept. System stability is analyzed via Lyapunov’s method and expressed through linear matrix inequalities. Results. Simulation tests under varying conditions validate the accuracy and effectiveness of the controller in achieving superior voltage tracking performance. Comparative analysis with a conventional PID controller highlights faster dynamic response and better tracking, showcasing the advantages of the proposed approach. Practical value. The practical value of this research lies in the development of a robust voltage control strategy for DC-DC buck converters and the establishment of reliable and efficient electrical systems using discrete-time fuzzy T-S control. This work also opens up the prospect for future implementation in experimental prototypes. References 30, table 2, figures 7.
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