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Swarm-Inspired Algorithms to Optimize a Nonlinear Gaussian Adaptive PID Controller

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  • Erickson Diogo Pereira Puchta

    (Graduate Program in Production Engineering, Federal University of Technology-Paraná (UTFPR), Dr. Washington Subtil Chueire St. 330, Jardim Carvalho, Ponta Grossa 84017-22, Brazil
    These authors contributed equally to this work.)

  • Priscilla Bassetto

    (Graduate Program in Production Engineering, Federal University of Technology-Paraná (UTFPR), Dr. Washington Subtil Chueire St. 330, Jardim Carvalho, Ponta Grossa 84017-22, Brazil
    These authors contributed equally to this work.)

  • Lucas Henrique Biuk

    (Graduate Program in Electrical Engineering, Federal University of Technology-Paraná (UTFPR), Dr. Washington Subtil Chueire St. 330, Jardim Carvalho, Ponta Grossa 84017-22, Brazil
    These authors contributed equally to this work.)

  • Marco Antônio Itaborahy Filho

    (Graduate Program in Electrical Engineering, Federal University of Technology-Paraná (UTFPR), Dr. Washington Subtil Chueire St. 330, Jardim Carvalho, Ponta Grossa 84017-22, Brazil
    These authors contributed equally to this work.)

  • Attilio Converti

    (Department of Civil, Chemical and Environmental Engineering, University of Genoa, Via Balbi 5, 16126 Genoa, Italy
    These authors contributed equally to this work.)

  • Mauricio dos Santos Kaster

    (Graduate Program in Electrical Engineering, Federal University of Technology-Paraná (UTFPR), Dr. Washington Subtil Chueire St. 330, Jardim Carvalho, Ponta Grossa 84017-22, Brazil
    These authors contributed equally to this work.)

  • Hugo Valadares Siqueira

    (Graduate Program in Production Engineering, Federal University of Technology-Paraná (UTFPR), Dr. Washington Subtil Chueire St. 330, Jardim Carvalho, Ponta Grossa 84017-22, Brazil
    These authors contributed equally to this work.)

Abstract

This work deals with metaheuristic optimization algorithms to derive the best parameters for the Gaussian Adaptive PID controller. This controller represents a multimodal problem, where several distinct solutions can achieve similar best performances, and metaheuristics optimization algorithms can behave differently during the optimization process. Finding the correct proportionality between the parameters is an arduous task that often does not have an algebraic solution. The Gaussian functions of each control action have three parameters, resulting in a total of nine parameters to be defined. In this work, we investigate three bio-inspired optimization methods dealing with this problem: Particle Swarm Optimization (PSO), the Artificial Bee Colony (ABC) algorithm, and the Whale Optimization Algorithm (WOA). The computational results considering the Buck converter with a resistive and a nonlinear load as a case study demonstrated that the methods were capable of solving the task. The results are presented and compared, and PSO achieved the best results.

Suggested Citation

  • Erickson Diogo Pereira Puchta & Priscilla Bassetto & Lucas Henrique Biuk & Marco Antônio Itaborahy Filho & Attilio Converti & Mauricio dos Santos Kaster & Hugo Valadares Siqueira, 2021. "Swarm-Inspired Algorithms to Optimize a Nonlinear Gaussian Adaptive PID Controller," Energies, MDPI, vol. 14(12), pages 1-20, June.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:12:p:3385-:d:571212
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    References listed on IDEAS

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    1. Naghmash Ali & Zhizhen Liu & Hammad Armghan & Iftikhar Ahmad & Yanjin Hou, 2021. "LCC-S-Based Integral Terminal Sliding Mode Controller for a Hybrid Energy Storage System Using a Wireless Power System," Energies, MDPI, vol. 14(6), pages 1-25, March.
    2. Niknam, Taher & Mojarrad, Hassan Doagou & Nayeripour, Majid, 2010. "A new fuzzy adaptive particle swarm optimization for non-smooth economic dispatch," Energy, Elsevier, vol. 35(4), pages 1764-1778.
    3. Niknam, Taher, 2010. "A new fuzzy adaptive hybrid particle swarm optimization algorithm for non-linear, non-smooth and non-convex economic dispatch problem," Applied Energy, Elsevier, vol. 87(1), pages 327-339, January.
    4. Christos Yfoulis & Simira Papadopoulou & Spyridon Voutetakis, 2020. "Robust Linear Control of Boost and Buck-Boost DC-DC Converters in Micro-Grids with Constant Power Loads," Energies, MDPI, vol. 13(18), pages 1-21, September.
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    Cited by:

    1. Pengcheng Geng & Xiangsong Kong & Changqing Shi & Hang Liu & Jiabin Liu, 2022. "IK-SPSA-Based Performance Optimization Strategy for Steam Generator Level Control System of Nuclear Power Plant," Energies, MDPI, vol. 15(19), pages 1-22, October.
    2. Marco Antonio Itaborahy Filho & Erickson Puchta & Marcella S. R. Martins & Thiago Antonini Alves & Yara de Souza Tadano & Fernanda Cristina Corrêa & Sergio Luiz Stevan & Hugo Valadares Siqueira & Maur, 2022. "Bio-Inspired Optimization Algorithms Applied to the GAPID Control of a Buck Converter," Energies, MDPI, vol. 15(18), pages 1-22, September.

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