Control and performance analyses of a DC motor using optimized PIDs and fuzzy logic controller

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dc.authoridManuel, Nelson Luis/0000-0002-7217-3387
dc.authoridLUY, Murat/0000-0002-2378-0009
dc.contributor.authorManuel, Nelson Luis
dc.contributor.authorInanc, Nihat
dc.contributor.authorLily, Murat
dc.date.accessioned2025-01-21T16:37:12Z
dc.date.available2025-01-21T16:37:12Z
dc.date.issued2023
dc.departmentKırıkkale Üniversitesi
dc.description.abstractBased on the no-free-lunch theorem, researchers have been proposing optimization algorithms for solving complex engineering problems. This paper analyzes the performance of five metaBased Optimization (TLBO), Differential Evolution (DE), and Genetic Algorithm (GA) in finetuning the gains of a Proportional-Integral-Derivative (PID) to control the speed of a DC motor. The selected metaheuristics, in addition to being from distinct classes, are well established in their respective groups. The methods and findings of this study can be summarized in three phases. First, the mathematical model of the DC motor is deduced. Second, detailed descriptions of the aforementioned algorithms are presented. Furthermore, the structures of the applied controllers are discussed. Third, comparisons based on statistical indicators and analyses in the time and frequency domains, in addition to robustness and load disturbance tests, are performed. The results revealed that if a sufficient number of runs is given for each metaheuristic, despite being in different runs, all algorithms are able to propose the same optimal gain values. TLBO presented the highest speed, while GA and DE were the slowest in finding optimal values. Additionally, the results were compared with the Opposition-Based Learning Henry Gas Solubility Optimization (OBL/HBO)-based PID, reported to have better results than some previously published works on this topic, and a Fuzzy Logic Controller (FLC). The five optimized controllers obtained approximately the same results and outperformed the OBL/HGO-based PID, but the FLC was superior compared to the metaheuristic-based PIDs.
dc.identifier.doi10.1016/j.rico.2023.100306
dc.identifier.issn2666-7207
dc.identifier.scopus2-s2.0-85173213097
dc.identifier.scopusqualityQ2
dc.identifier.urihttps://doi.org/10.1016/j.rico.2023.100306
dc.identifier.urihttps://hdl.handle.net/20.500.12587/24419
dc.identifier.volume13
dc.identifier.wosWOS:001208993700011
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherElsevier
dc.relation.ispartofResults In Control and Optimization
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_20241229
dc.subjectDC motor speed control; Metaheuristic algorithms; Equilibrium optimizer; Particle swarm optimization; Teaching-learning-based optimization; Differential evolution; Genetic algorithm
dc.titleControl and performance analyses of a DC motor using optimized PIDs and fuzzy logic controller
dc.typeArticle

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