Fractional order chaotic model based enhanced equilibrium optimization algorithm for controller design of 3 DOF Hover flight system

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dc.authorscopusid57196895937
dc.authorscopusid7601439185
dc.contributor.authorAtes A.
dc.contributor.authorChen Y.
dc.date.accessioned2024-08-04T20:04:02Z
dc.date.available2024-08-04T20:04:02Z
dc.date.issued2021
dc.departmentİnönü Üniversitesien_US
dc.descriptionComputers and Information in Engineering Division;Design Engineering Divisionen_US
dc.description17th IEEE/ASME International Conference on Mechatronic and Embedded Systems and Applications, MESA 2021, Held as Part of the ASME 2021 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, IDETC-CIE 2021 -- 17 August 2021 through 19 August 2021 -- 174204en_US
dc.description.abstractIn this study, the K feedback gain vector parameters that are used for the control of three degree of freedom four rotor quadcopter system (3 DOF Hover) are optimized with the Enhanced Equilibrium Optimization Algorithm (E2O). The E2O algorithm is proposed with using parameters obtained from fractional order chaotic oscillator models instead of random variables. The Basic EO algorithm is inspired by volume-mass balance. In EO algorithm, each particle is called a motion that searches a parameter vector space. However, random coefficients derived from uniform distribution are used in the parameters updating process or in the generation of the initial population. The E2O algorithm was proposed by using vectors obtained from fractional order chaotic oscillators instead of stochastic coefficients in the basic Equilibrium optimization algorithm. Genesio Tesi, Rössler, Lotka Volterra fractional-order chaotic oscillator models were used in the E2O algorithm to optimize K feedback gain vector of 3 DOF Hover. The order and initial conditions the fractional chaotic oscillator models were experimentally adjusted for the control of 3 DOF problem. Thus, suitable fractional-order chaotic models for the problem were obtained. The E2O algorithm results are compared with the Stochastic Multi Parameter Optimization (SMDO) and Discreet Stochastic Optimization (DSO) algorithms for the system's pitch, roll and yaw angles. Copyright © 2021 by ASMEen_US
dc.identifier.doi10.1115/DETC2021-69307
dc.identifier.isbn9780791885437
dc.identifier.scopus2-s2.0-85119975978en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.urihttps://doi.org/10.1115/DETC2021-69307
dc.identifier.urihttps://hdl.handle.net/11616/92305
dc.identifier.volume7en_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherAmerican Society of Mechanical Engineers (ASME)en_US
dc.relation.ispartofProceedings of the ASME Design Engineering Technical Conferenceen_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectControlen_US
dc.subjectFractional calculusen_US
dc.subjectSwarm intelligence optimization algorithmen_US
dc.titleFractional order chaotic model based enhanced equilibrium optimization algorithm for controller design of 3 DOF Hover flight systemen_US
dc.typeConference Objecten_US

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