Optimal Sliding-Mode Control of Semi-Bridgeless Boost Converters Considering Power Factor Corrections

José R. Ortiz-Castrillón, Sergio D. Saldarriaga-Zuluaga, Nicolás Muñoz-Galeano (), Jesús M. López-Lezama, Santiago Benavides-Córdoba and Juan B. Cano-Quintero
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José R. Ortiz-Castrillón: Research Group on Efficient Energy Management (GIMEL), Department of Electrical Engineering, Universidad de Antioquia (UdeA), Medellín 050010, Colombia
Sergio D. Saldarriaga-Zuluaga: Facultad de Ingeniería, Departamento de Eléctrica, Institución Universitaria Pascual Bravo, Calle 73 No. 73A-226, Medellín 050036, Colombia
Nicolás Muñoz-Galeano: Research Group on Efficient Energy Management (GIMEL), Department of Electrical Engineering, Universidad de Antioquia (UdeA), Medellín 050010, Colombia
Jesús M. López-Lezama: Research Group on Efficient Energy Management (GIMEL), Department of Electrical Engineering, Universidad de Antioquia (UdeA), Medellín 050010, Colombia
Santiago Benavides-Córdoba: Research Group on Efficient Energy Management (GIMEL), Department of Electrical Engineering, Universidad de Antioquia (UdeA), Medellín 050010, Colombia
Juan B. Cano-Quintero: Research Group on Efficient Energy Management (GIMEL), Department of Electrical Engineering, Universidad de Antioquia (UdeA), Medellín 050010, Colombia

Energies, 2023, vol. 16, issue 17, 1-24

Abstract: Sliding-mode control (SMC) is a robust technique used in power electronics (PE) for controlling the behavior of power converters. This paper presents simulations and experimental results of an optimal SMC strategy applied to Semi-Bridgeless Boost Converters (SBBC), which includes Power Factor Correction (PFC). As the main contribution, the optimal coefficients of the SMC strategy are obtained using two metaheuristic approaches, namely the Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). The main objective is to obtain the sliding coefficients that ensure the best converter response in terms of the input current and output voltage, both during start-up and under disturbances (including changes in load, source, and references). The fitness function to be minimized includes two coefficients, namely the Integrative Absolute Error (IAE) and the Integral Time Absolute Error (ITAE), for both the input current and output voltage. These coefficients measure the converter’s effort to follow the control references. The IAE penalizes errors during start-up, whereas the ITAE penalizes errors in the steady state. The tests carried out demonstrated the effectiveness of the GA and PSO techniques in the optimization process; nonetheless, the GA outperformed the PSO approach, providing sliding coefficients that allowed for a reduction in the input current overshoot during start-up of up to 24.15% and a reduction in the setting time of the output voltage of up to 99%. The experimental results were very similar when tuning with the GA and PSO techniques; nevertheless, tuning with the GA technique produced a better response in the face of disturbances compared to the PSO technique.

Keywords: genetic algorithm; non-linear control; particle swarm optimization; sliding mode control; semi-bridgeless boost converter (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
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