Quantified-state-feedback-based Nonlinear pure-feedback MIMO systems benefit from adaptive neural control

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dc.contributor.authorAbdulkareem, M.M.
dc.contributor.authorYas, Attrah, N.H.
dc.contributor.authorAlrubaee, S.H.
dc.contributor.authorBaraa, Al-Sabti, S.M.
dc.contributor.authorDheyab, A.
dc.contributor.authorMohammed, A.H.
dc.date.accessioned2024-09-29T16:20:47Z
dc.date.available2024-09-29T16:20:47Z
dc.date.issued2022
dc.departmentKarabük Üniversitesien_US
dc.description6th International Symposium on Multidisciplinary Studies and Innovative Technologies, ISMSIT 2022 -- 20 October 2022 through 22 October 2022 -- Ankara -- 184355en_US
dc.description.abstractIn the case of state quantizers for MIMO nonlinear block-Triangular pure-feedback systems, there is a certain sort of uncertainty that may be tolerated, we describe a quantized state feedback tracking mechanism. All state variables that may be measured for feedback are thought to benefit from uniform quantizers. In this study, we focus on the problem of tracking of output for a certain kind of MIMO nonlinear systems composed of interconnected modules with widely varying degrees of uncertainty. To begin, the total disturbance in the subsystems' control channels is refined to account for all uncertainties impacting the performance of the controlled outputs, such as internal unmodeled dynamics, external disturbances, and unknown nonlinear interactions between subsystems. It is shown that the total disturbance level is low enough for a real-Time estimate to be made by an extended state observer (ESO) utilising the observed outputs from the separate subsystems. Stability study of the closed-loop system with quantized state feedback is also performed using the Lyapunov stability theorem. Finally, illustrative simulation examples, such as a network of inverted pendulums, are shown to prove that the suggested control approach works as intended. © 2022 IEEE.en_US
dc.identifier.doi10.1109/ISMSIT56059.2022.9932689
dc.identifier.endpage778en_US
dc.identifier.isbn978-166547013-1
dc.identifier.scopus2-s2.0-85142819200en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.startpage774en_US
dc.identifier.urihttps://doi.org/10.1109/ISMSIT56059.2022.9932689
dc.identifier.urihttps://hdl.handle.net/20.500.14619/9329
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.relation.ispartofISMSIT 2022 - 6th International Symposium on Multidisciplinary Studies and Innovative Technologies, Proceedingsen_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectMIMO Nonlinearen_US
dc.subjectmulti-input multiple-output (MIMO)en_US
dc.subjectQuantized state feedback controlen_US
dc.titleQuantified-state-feedback-based Nonlinear pure-feedback MIMO systems benefit from adaptive neural controlen_US
dc.typeConference Objecten_US

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