Adaptive Predefined Performance Neural Control for Robotic Manipulators with Unknown Dead Zone
Joint Authors
Shao, Shifen
Wang, Jirong
Li, Jun
Zhang, Kaisheng
Source
Mathematical Problems in Engineering
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-8, 8 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-05-12
Country of Publication
Egypt
No. of Pages
8
Main Subjects
Abstract EN
This paper proposes an adaptive predefined performance neural control scheme for robotic manipulators in the presence of nonlinear dead zone.
A neural network (NN) is utilized to estimate the model uncertainties and unknown dynamics.
An improved funnel function is designed to guarantee the transient behavior of the tracking error.
The proposed funnel function can release the assumption on the conventional funnel control.
Then, an adaptive predefined performance neural controller is proposed for robotic manipulators, while the tracking errors fall within a prescribed funnel boundary.
The closed-loop system stability is proved via Lyapunov function.
Finally, the numerical simulation results based on a 2-DOF robotic manipulator illustrate the control effect of the presented approach.
American Psychological Association (APA)
Shao, Shifen& Zhang, Kaisheng& Li, Jun& Wang, Jirong. 2020. Adaptive Predefined Performance Neural Control for Robotic Manipulators with Unknown Dead Zone. Mathematical Problems in Engineering،Vol. 2020, no. 2020, pp.1-8.
https://search.emarefa.net/detail/BIM-1196815
Modern Language Association (MLA)
Shao, Shifen…[et al.]. Adaptive Predefined Performance Neural Control for Robotic Manipulators with Unknown Dead Zone. Mathematical Problems in Engineering No. 2020 (2020), pp.1-8.
https://search.emarefa.net/detail/BIM-1196815
American Medical Association (AMA)
Shao, Shifen& Zhang, Kaisheng& Li, Jun& Wang, Jirong. Adaptive Predefined Performance Neural Control for Robotic Manipulators with Unknown Dead Zone. Mathematical Problems in Engineering. 2020. Vol. 2020, no. 2020, pp.1-8.
https://search.emarefa.net/detail/BIM-1196815
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1196815