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Command filtered backstepping control of constrained flexible joint robotic manipulator.

Authors :
Arefi, Mohammad Mehdi
Vafamand, Navid
Homayoun, Behrouz
Davoodi, Mohammadreza
Source :
IET Control Theory & Applications (Wiley-Blackwell). Dec2023, Vol. 17 Issue 18, p2506-2518. 13p.
Publication Year :
2023

Abstract

Here, an adaptive radial basis function (RBF) neural network (NN) backstepping controller is proposed for a class of input‐constrained flexible joint robotic manipulators represented by strict‐feedback form with unknown terms, external stochastic disturbance, and output disturbance. The proposed approach is robust against both deterministic and stochastic uncertainties and disturbances and copes with the control input amplitude saturation. Moreover, by deploying the minimal learning parameter method and command filter technique, the computational burden of derivative terms and adaptive terms greatly decreases. Considering the mean‐value theorem assists us to avoid the need for having the input saturation bounds in prior. The suggested tracking control scheme mandates the closed‐loop system states to be semi‐globally bounded‐in‐probability. Also, a quartic Barrier Lyapunov function is utilized to force the tracking error to be confined within a pre‐chosen small region around the origin. Eventually, a numerical simulation of a flexible joint robot manipulator with a single link is performed to show the effectiveness and performance of the developed control method. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
17518644
Volume :
17
Issue :
18
Database :
Academic Search Index
Journal :
IET Control Theory & Applications (Wiley-Blackwell)
Publication Type :
Academic Journal
Accession number :
174157925
Full Text :
https://doi.org/10.1049/cth2.12528