Dhaouadi Guiza

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.

This research focuses on developing a proportional integral derivative controller based on a single artificial neural network (PID-SANN). The proposed control strategy drives the direct current (DC-DC) boost converter output voltage to follow the desired reference value. This controller calculates the PID gains via a learning algorithm based on an artificial single-neuron network, which overcomes the computational complexity of PID gains using analytical methods and automatically adjusts the controller parameters. The developed PID-SANN method offers the boost converter the appropriate duty ratio, which permits controlling the output voltage value despite fluctuations in the resistive load or input voltage. The obtained results confirm that the developed method can successfully surmount the constraints of conventional PID controllers and direct the output voltage of the considered DC-DC converter to follow the required value precisely.