Reliable parameter estimation of nonlinear chaotic systems and PMSMs with the stellar oscillation optimizer

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dc.contributor.authorEkinci, Serdar
dc.contributor.authorİzci, Davut
dc.contributor.authorJabari, Mostafa
dc.contributor.authorElsayed, Fahmi
dc.contributor.authorSalman, Mohammad
dc.contributor.authorBektaş Güneş, Burcu
dc.date.accessioned2026-04-16T06:58:03Z
dc.date.available2026-04-16T06:58:03Z
dc.date.issued2026
dc.departmentFakülteler, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümü
dc.description.abstractAccurate parameter identification is a critical prerequisite for reliable modeling, analysis, and control of nonlinear dynamical systems. This study introduces the stellar oscillation optimizer (SOO), a recently proposed metaheuristic inspired by the oscillatory behavior of stars, and investigates its effectiveness in estimating system parameters through a unified optimization framework. The identification problem is formulated as the minimization of a trajectory-mismatch cost function, where candidate solutions are iteratively refined by the oscillatory dynamics of SOO. To comprehensively evaluate its performance, four benchmark systems were considered: three canonical chaotic models (Lorenz, Chen, and R & ouml;ssler) and a practical engineering case represented by a permanent-magnet synchronous motor (PMSM). The outcomes were benchmarked against several state-of-the-art algorithms, including Kirchhoff's law algorithm (KLA), Tianji's horse racing optimization (THRO), puma optimizer (PO), and hiking optimization algorithm (HOA), under a standardized protocol. The results show that SOO consistently achieves numerically convergent solutions with machine-precision-level residuals under deterministic and noise-free simulation settings, while maintaining strong robustness across independent runs. In chaotic benchmarks, the reported residuals approach floating-point limits, which indicates stable numerical convergence rather than guaranteed physical identifiability under real measurement conditions. On the PMSM model, SOO demonstrates accurate and repeatable parameter estimation within the adopted simulation framework.
dc.identifier.doi10.1038/s41598-026-41940-2
dc.identifier.issn2045-2322
dc.identifier.issue1
dc.identifier.pmid41772072
dc.identifier.scopus2-s2.0-105035109832
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.1038/s41598-026-41940-2
dc.identifier.urihttps://hdl.handle.net/11501/2683
dc.identifier.volume16
dc.identifier.wosWOS:001735314100020
dc.identifier.wosqualityQ1
dc.indekslendigikaynakScopus
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakPubMed
dc.institutionauthorBektaş Güneş, Burcu
dc.institutionauthorid0000-0002-9046-1542
dc.language.isoen
dc.publisherNature Research
dc.relation.ispartofScientific Reports
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectChaotic Attractors
dc.subjectNonlinear Systems
dc.subjectParameter Identification
dc.subjectPermanent Magnet Synchronous Motor
dc.subjectStellar Oscillation Optimizer
dc.titleReliable parameter estimation of nonlinear chaotic systems and PMSMs with the stellar oscillation optimizer
dc.typeArticle

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