Open AccessAdaptive neural network control of a robotic manipulator with unknown backlash‐like hysteresis
Author(s) -
He Wei,
Ofosu Amoateng David,
Yang Chenguang,
Gong Dawei
Publication year - 2017
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.2016.1058
Subject(s) - backlash , control theory (sociology) , artificial neural network , hysteresis , observer (physics) , adaptive control , controller (irrigation) , computer science , control engineering , engineering , control (management) , artificial intelligence , physics , quantum mechanics , agronomy , biology
This study proposes an adaptive neural network controller for a 3‐DOF robotic manipulator that is subject to backlash‐like hysteresis and friction. Two neural networks are used to approximate the dynamics and the hysteresis non‐linearity. A neural network, which utilises a radial basis function approximates the robot's dynamics. The other neural network, which employs a hyperbolic tangent activation function, is used to approximate the unknown backlash‐like hysteresis. The authors also consider two cases: full state and output feedback control. For output feedback, where system states are unknown, a high gain observer is employed to estimate the states. The proposed controllers ensure the boundedness of the control signals. Simulations are also performed to show the effectiveness of the controllers.