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dc.contributor.authorSong X.-
dc.contributor.authorSun P.-
dc.contributor.authorSong S.-
dc.contributor.authorStojanović, Vladimir-
dc.date.accessioned2023-02-08T16:00:03Z-
dc.date.available2023-02-08T16:00:03Z-
dc.date.issued2022-
dc.identifier.issn0016-0032-
dc.identifier.urihttps://scidar.kg.ac.rs/handle/123456789/15878-
dc.description.abstractThis article investigates the adaptive neural network fixed-time tracking control issue for a class of strict-feedback nonlinear systems with prescribed performance demands, in which the radial basis function neural networks (RBFNNs) are utilized to approximate the unknown items. First, an modified fractional-order command filtered backstepping (FOCFB) control technique is incorporated to address the issue of the iterative derivation and remove the impact of filtering errors, where a fractional-order filter is adopted to improve the filter performance. Furthermore, an event-driven-based fixed-time adaptive controller is constructed to reduce the communication burden while excluding the Zeno-behavior. Stability results prove that the designed controller not only guarantees all the signals of the closed-loop system (CLS) are practically fixed-time bounded, but also the tracking error can be regulated to the predefined boundary. Finally, the feasibility and superiority of the proposed control algorithm are verified by two simulation examples.-
dc.rightsinfo:eu-repo/semantics/restrictedAccess-
dc.sourceJournal of the Franklin Institute-
dc.titleEvent-driven NN adaptive fixed-time control for nonlinear systems with guaranteed performance-
dc.typearticle-
dc.identifier.doi10.1016/j.jfranklin.2022.04.003-
dc.identifier.scopus2-s2.0-85129950833-
Налази се у колекцијама:Faculty of Mechanical and Civil Engineering, Kraljevo

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