Djamel Ounnas

1. DC–DC converters are essential in power electronics, providing accurate voltage regulation by stepping down the input voltage to the desired level for various application requirements (Wang et a...

This paper proposes a hybrid metaheuristic optimization approach for real-time tuning of an Extended Incremental Discrete Proportional–Integral–Derivative (EID–PID) controller applied to a DC–DC buck converter. The proposed method integrates Adaptive Particle Swarm Optimization with Local Refinement (APSO–L) and the Whale Optimization Algorithm (WOA) to achieve an effective balance between global exploration and local exploitation. The EID–PID structure enhances conventional PID control through dynamic filtering and anti-windup mechanisms, providing superior robustness and adaptability for nonlinear, time-varying power converter systems. The optimization process is executed in MATLAB, which communicates optimal parameters in real time to an STM32F429I microcontroller implementing the controller law. Simulation studies using MATLAB/Simulink models and experimental validation confirm the algorithm’s fast convergence, high precision, and high robustness through input voltage and load variations. The results demonstrate that the proposed APSO–L–WOA-based EID–PID control scheme offers a powerful and scalable solution for real-time embedded control in power electronics applications.

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.

Introduction. A new global maximum power point tracking (GMPPT) control strategy for a solar photovoltaic (PV) system, based on the combination of Takagi-Sugeno (T-S) fuzzy models and an ANFIS, is presented. The novelty of this paper lies in the integration of T-S fuzzy models and the ANFIS approach to develop an efficient GMPPT controller for a PV system operating under partial shading conditions. Purpose. The new GMPPT control strategy aims to extract maximum power from the PV system under varying weather conditions or partial shading. Methods. An ANFIS algorithm is used to determine the maximum voltage, which corresponds to the actual maximum power point, based on PV voltage and current. Next, the nonlinear model of the PV system is employed to design the T-S fuzzy controller. A reference model is then derived based on the maximum voltage. Finally, a tracking controller is developed using the reference model and the T-S fuzzy controller. The stability of the overall system is evaluated using Lyapunov’s method and is represented through linear matrix inequalities expressions. The results clearly demonstrate the advantages of the proposed GMPPT-based fuzzy control strategy, showcasing its high performance in effectively reducing oscillations in various steady states of the PV system, ensuring minimal overshoot and a faster response time. In addition, a comparative analysis of the proposed GMPPT controller against conventional algorithms, such as Incremental Conductance, Perturb & Observe and Particle Swarm Optimization, shows that it offers a fast dynamic response in finding the maximum power with significantly less oscillation around the maximum power point. References 20, tables 3, figures 14.

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.

The present paper treats with the implementation of the modified P&O (perturb and observe) maximum power point tracking (MPPT) algorithm to improve its performance. Firstly, the simulation study and comparison between the modified and the conventional P &O methods are presented to offer the efficacy of the developed method. Secondly, to verify the effectiveness of the developed method, practical validation is carried out using an Arduino Mega. The experimental and simulation tests present that the tracking speed is increased and fluctuations around the MPP (maximum power point) are reduced which demonstrates that the proposed algorithm overcomes the drawbacks of conventional one, and it does not require any extra hardware components.

Design and Implementation of Takagi-Sugeno Fuzzy Tracking Control for a DC-DC Buck Converter

The aim of this paper is to present a new fuzzy tracking control for a wind energy conversion system (WECS), which consists of a permanent magnet synchronous generator based variable speed wind turbine (WT). First, a fuzzy Takagi-Sugeno (T-S) model is used to represent the PMSG-WT nonlinear system. Next, a fuzzy tracking control based on the concept of virtual desired variables (VDVs) is formulated to simplify the design of the virtual reference model and the control law. Using this concept, a two-stage design procedure is developed: i) determine the VDVs from the output equation of the nonlinear system and the generalized kinematics constraints ii) find the feedback controller gains by solving a set of linear matrix inequalities (LMIs). Finally, simulation results are provided to demonstrate the validity and the effectiveness of the proposed method.

The aim of this paper is to present a new fuzzy tracking strategy for a permanent magnet synchronous machine (PMSM) by using Takagi-Sugeno models (T-S). A feedback-based fuzzy control with h-infinity tracking performance and a concept of virtual reference model are combined to develop a fuzzy tracking controller capable to track a reference signal and ensure a minimum effect of disturbance on the PMSM system. First, a T-S fuzzy model is used to represent the PMSM nonlinear system with disturbance. Next, an integral fuzzy tracking control based on the concept of virtual desired variables (VDVs) is formulated to simplify the design of the virtual reference model and the control law. Finally, based on this concept, a two-stage design procedure is developed: i) determine the VDVs from the nonlinear system output equation and generalized kinematics constraints ii) calculate the feedback controller gains by solving a set of linear matrix inequalities (LMIs). Simulation results are provided to demonstrate the validity and the effectiveness of the proposed method.