Adaptive control of A class of nonlinear systems with guaranteed parameter estimation: A concurrent learning based approach

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dc.authoridTatlicioglu, Enver/0000-0001-5623-9975
dc.authoridZergeroglu, Erkan/0000-0002-1211-0448
dc.contributor.authorObuz, Serhat
dc.contributor.authorZergeroglu, Erkan
dc.contributor.authorTatlicioglu, Enver
dc.date.accessioned2025-03-17T12:25:50Z
dc.date.available2025-03-17T12:25:50Z
dc.date.issued2024
dc.departmentTarsus Üniversitesi
dc.description.abstractRecent advances in concurrent learning based adaptive controllers have relaxed the persistency of excitation condition required to achieve exponential tracking and parameter estimation error convergence. This was made possible via the use of additional concurrent learning stacks in the parameter estimation algorithm. However, the proposed concurrent learning components, that is, the history stacks, needed to be filled with selected values dependent on the actual system states. Therefore, the previously proposed concurrent learning adaptive controllers required the system to be stable initially for a finite time so that the corresponding history stacks can be filled (finite excitation condition). In this work, motivated to remove the finite excitation condition, a novel desired system state based concurrent learning adaptive controller is proposed. In order to remove the system state dependencies in the controller and estimation algorithms, a filtered version of the dynamics and a novel prediction error formulation have been designed. The overall exponential stability, parameter error convergence and boundedness of the system states during closed loop operations are ensured via Lyapunov based arguments. The main advantages of the proposed method are its dependence on the desired system states and the overall stability results that paved the way in removing the need for finite excitation condition. Numerical studies performed on a two link robotic device are also presented to illustrate the feasibility of the proposed method. Significant research is achieved by ensuring output tracking along with accurate identification of uncertain parametric uncertainties. In a novel departure from the existing literature, the need for persistency of excitation condition is eliminated. image
dc.description.sponsorshipTrkiye Bilimsel ve Teknolojik Arascedil;timath;rma Kurumu [121E383]
dc.description.sponsorshipTurkiye Bilimsel ve Teknolojik Arast & imath;rma Kurumu,Grant/Award Number: 121E383
dc.identifier.doi10.1049/cth2.12668
dc.identifier.endpage1337
dc.identifier.issn1751-8644
dc.identifier.issn1751-8652
dc.identifier.issue10
dc.identifier.scopus2-s2.0-85190846559
dc.identifier.scopusqualityQ1
dc.identifier.startpage1328
dc.identifier.urihttps://doi.org/10.1049/cth2.12668
dc.identifier.urihttps://hdl.handle.net/20.500.13099/1899
dc.identifier.volume18
dc.identifier.wosWOS:001206701300001
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherWiley
dc.relation.ispartofIet Control Theory and Applications
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_WOS_20250316
dc.subjectadaptive control
dc.subjectadaptive estimation
dc.subjectidentification
dc.subjectLyapunov methods
dc.titleAdaptive control of A class of nonlinear systems with guaranteed parameter estimation: A concurrent learning based approach
dc.typeArticle

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