Fast Fractional-Order Terminal Sliding Mode Control With RBFNN Based Sliding Perturbation Observer for 7-DOF Robot Manipulator

A new perturbation estimator, using radial basis function (RBF) neural networks (RBFNN) to modify the sliding perturbation observer (SPO), is proposed with the fast fractional-order terminal sliding mode control (FFOTSMC). It aims to control a seven-degree-of-freedom (7-DOF) robot manipulator. The n...

Ausführliche Beschreibung

Gespeichert in:

Bibliographische Detailangaben
Veröffentlicht in:	IEEE access 2021, Vol.9, p.67117-67128
Hauptverfasser:	Jie, Wang, Cheol, Lee Min, Jaehyung, Kim, Hee, Kim Hyun
Format:	Artikel
Sprache:	eng
Schlagworte:	Asymptotic stability Computer Science Computer Science, Information Systems Control stability Controllers Degrees of freedom Engineering Engineering, Electrical & Electronic fractional-order Liapunov functions Low-pass filters Manipulators Neural networks Perturbation Perturbation methods Radial basis function Robot arms Robot control robot manipulator Robots robust control Science & Technology Sliding mode control Technology Telecommunications Uncertainty
Online-Zugang:	Volltext
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!

Schreiben Sie den ersten Kommentar!