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dc.coverage.spatialBogotá, Colombiaspa
dc.creatorZouari, Farouk
dc.creatorIbeas, Asier
dc.creatorBoulkroune, Abdesselem
dc.creatorCao, Jinde
dc.creatorMehdi Arefi, Mohammad
dc.date.accessioned2020-04-16T14:52:01Z
dc.date.available2020-04-16T14:52:01Z
dc.date.created2018
dc.identifier.otherhttps://doi.org/10.1016/j.neunet.2018.05.014spa
dc.identifier.urihttp://hdl.handle.net/20.500.12010/8813
dc.description.abstractThis study addresses the issue of the adaptive output tracking control for a category of uncertain nonstrict-feedback delayed incommensurate fractional-order systems in the presence of nonaffine structures, unmeasured pseudo-states, unknown control directions, unknown actuator nonlinearities and output constraints. Firstly, the mean value theorem and the Gaussian error function are introduced to eliminate the difficulties that arise from the nonaffine structures and the unknown actuator nonlinearities, respectively. Secondly, the immeasurable tracking error variables are suitably estimated by constructing a fractional-order linear observer. Thirdly, the neural network, the Razumikhin Lemma, the variable separation approach, and the smooth Nussbaum-type function are used to deal with the uncertain nonlinear dynamics, the unknown time-varying delays, the nonstrict feedback and the unknown control directions, respectively. Fourthly, asymmetric barrier Lyapunov functions are employed to overcome the violation of the output constraints and to tune online the parameters of the adaptive neural controller. Through rigorous analysis, it is proved that the boundedness of all variables in the closed-loop system and the semi global asymptotic tracking are ensured without transgression of the constraints. The principal contributions of this study can be summarized as follows: (1) based on a uto’s definitions and new lemmas, methods concerning the controllability, observability and stability analysis of integer-order systems are extended to fractional-order ones, (2) the output tracking objective for a relatively large class of uncertain systems is achieved with a simple controller and less tuning parameters. Finally, computersimulation studies from the robotic field are given to demonstrate the effectiveness of the proposed controller.spa
dc.format.extent61 páginasspa
dc.format.mimetypeimage/jepgspa
dc.publisherUniversidad de Bogotá Jorge Tadeo Lozanospa
dc.subjectAdaptive output-feedback controlspa
dc.subjectNonstrict-feedback fractional-order systemsspa
dc.subjectBarrier Lyapunov functionspa
dc.subjectActuator nonlinearitiesspa
dc.subjectNeural networkspa
dc.subjectNussbaum-type functionspa
dc.subjectVariable separation approachspa
dc.subjectRazumikhin Lemmaspa
dc.titleAdaptive neural output-feedback control for nonstrict- feedback time-delay fractional-order systems with output constraints and actuator nonlinearitiesspa
dc.type.localArtículospa
dc.subject.lembRobótica -- Investigacionesspa
dc.subject.lembRedes neuronales (Computadores)spa
dc.rights.accessrightsinfo:eu-repo/semantics/openAccessspa
dc.type.hasversioninfo:eu-repo/semantics/acceptedVersionspa
dc.rights.localAbierto (Texto Completo)spa
dc.identifier.doihttps://doi.org/10.1016/j.neunet.2018.05.014spa
dc.identifier.instnameinstname:Universidad de Bogotá Jorge Tadeo Lozanospa
dc.identifier.reponamereponame:Repositorio Institucional de la Universidad de Bogotá Jorge Tadeo Lozanospa


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