Open AccessAdaptive neural network control of coordinated robotic manipulators with output constraint
Author(s) -
Zhang Shuang,
Lei Minjie,
Dong Yiting,
He Wei
Publication year - 2016
Publication title -
iet control theory and applications
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.059
H-Index - 108
eISSN - 1751-8652
pISSN - 1751-8644
DOI - 10.1049/iet-cta.2016.0009
Subject(s) - control theory (sociology) , constraint (computer aided design) , controller (irrigation) , lyapunov function , artificial neural network , robot manipulator , bounded function , computer science , stability (learning theory) , control engineering , adaptive control , control (management) , engineering , mathematics , artificial intelligence , nonlinear system , mechanical engineering , mathematical analysis , agronomy , physics , quantum mechanics , machine learning , biology
In this study, the authors aim to solve the tracking control problem of coordinated robotic manipulators. In order to handle with the uncertainties and instability of coordinated robotic manipulators and improve the performance of the system with output constraint, they design a controller by using radial basis function neural network which has the ability to approximate any bounded and continuous functions effectively. A barrier Lyapunov function is also introduced to prevent the violation of output constraint. The stability analysis of the closed‐loop system is provided and the performance of the controller is verified through simulation.