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

Erickson Diogo Pereira Puchta, Priscilla Bassetto, Lucas Henrique Biuk, Marco Antônio Itaborahy Filho, Attilio Converti, Mauricio dos Santos Kaster and Hugo Valadares Siqueira
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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
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
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
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
Attilio Converti: Department of Civil, Chemical and Environmental Engineering, University of Genoa, Via Balbi 5, 16126 Genoa, Italy
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
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

Energies, 2021, vol. 14, issue 12, 1-20

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.

Keywords: GAPID controller; PSO; ABC; WOA; optimization (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: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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