Optimization of Adaptive Sliding Mode Controllers Using Customized Metaheuristics in DC-DC Buck-Boost Converters

Daniel F. Zambrano-Gutierrez, Jorge M. Cruz-Duarte (), Herman Castañeda and Juan Gabriel Avina-Cervantes ()
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Daniel F. Zambrano-Gutierrez: School of Engineering and Sciences, Tecnológico de Monterrey, Av. Eugenio Garza Sada 2501, Col. Tecnológico, Monterrey 64700, Nuevo León, Mexico
Jorge M. Cruz-Duarte: School of Engineering and Sciences, Tecnológico de Monterrey, Av. Eugenio Garza Sada 2501, Col. Tecnológico, Monterrey 64700, Nuevo León, Mexico
Herman Castañeda: School of Engineering and Sciences, Tecnológico de Monterrey, Av. Eugenio Garza Sada 2501, Col. Tecnológico, Monterrey 64700, Nuevo León, Mexico
Juan Gabriel Avina-Cervantes: Telematics Research Group, Department of Electronics Engineering, University of Guanajuato, Carr. Salamanca-Valle de Santiago km 3.5 + 1.8 km, Comunidad de Palo Blanco, Salamanca 36885, Guanajuato, Mexico

Mathematics, 2024, vol. 12, issue 23, 1-25

Abstract: Metaheuristics have become popular tools for solving complex optimization problems; however, the overwhelming number of tools and the fact that many are based on metaphors rather than mathematical foundations make it difficult to choose and apply them to real engineering problems. This paper addresses this challenge by automatically designing optimization algorithms using hyper-heuristics as a master tool. Hyper-heuristics produce customized metaheuristics by combining simple heuristics, guiding a population of initially random individuals to a solution that satisfies the design criteria. As a case study, the obtained metaheuristic tunes an Adaptive Sliding Mode Controller to improve the dynamic response of a DC-DC Buck–Boost converter under various operating conditions (such as overshoot and settling time), including nonlinear disturbances. Specifically, our hyper-heuristic obtained a tailored metaheuristic composed of Genetic Crossover- and Swarm Dynamics-type operators. The goal is to build the metaheuristic solver that best fits the problem and thus find the control parameters that satisfy a predefined performance. The numerical results reveal the reliability and potential of the proposed methodology in finding suitable solutions for power converter control design. The system overshoot was reduced from 87.78% to 0.98%, and the settling time was reduced from 31.90 ms to 0.4508 ms. Furthermore, statistical analyses support our conclusions by comparing the custom metaheuristic with recognized methods such as MadDE, L-SHADE, and emerging metaheuristics. The results highlight the generated optimizer’s competitiveness, evidencing the potential of Automated Algorithm Design to develop high-performance solutions without manual intervention.

Keywords: control systems design; power electronics; optimization methods; heuristic-based tuning; automated algorithm design (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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