Robust tracking of robot manipulator with nonlinear friction using time delay control with gradient estimator

2010 ◽  
Vol 24 (8) ◽  
pp. 1743-1752 ◽  
Author(s):  
Dong Ki Han ◽  
Pyung-hun Chang
Keyword(s):  
Time Delay ◽  
Robot Manipulator ◽  
Nonlinear Friction ◽  
Robust Tracking ◽  
Time Delay Control ◽  
Delay Control

Robust Tracking Under Nonlinear Friction Using Time-Delay Control With Internal Model

2009 ◽  
Vol 17 (6) ◽  
pp. 1406-1414 ◽  
Author(s):  
Gun Rae Cho ◽  
Pyung Hun Chang ◽  
Sang Hyun Park ◽  
Maolin Jin
Keyword(s):  
Time Delay ◽  
Internal Model ◽  
Nonlinear Friction ◽  
Robust Tracking ◽  
Time Delay Control ◽  
Delay Control

scholarly journals Feedback linearization based predictor for time delay control of a high-DOF robot manipulator

Automatica ◽  
2019 ◽  
Vol 108 ◽  
pp. 108485 ◽  
Author(s):  
Mostafa Bagheri ◽  
Peiman Naseradinmousavi ◽  
Miroslav Krstić
Keyword(s):  
Time Delay ◽  
Feedback Linearization ◽  
Robot Manipulator ◽  
Time Delay Control ◽  
Delay Control

scholarly journals Switched time delay control based on neural network for fault detection and compensation in robot

2021 ◽  
Vol 10 (2) ◽  
pp. 91
Author(s):  
Maincer Dihya ◽  
Mansour Moufid ◽  
Boudjedir Chemseddine ◽  
Bounabi Moussaab
Keyword(s):  
Neural Network ◽  
Time Delay ◽  
Fault Detection ◽  
Sliding Mode ◽  
Robot Manipulator ◽  
Neural Network Learning ◽  
Mlp Neural Network ◽  
Time Delay Control ◽  
The Neural Network ◽  
Delay Control

Fault detection in robotic manipulators is necessary for their monitoring and represents an effective support to use them as independent systems. This present study investigates an enhanced method for representation of the faultless system behavior in a robot manipulator based on a multi-layer perceptron (MLP) neural network learning model which produces the same behavior as the real dynamic manipulator. The study was based on generation of residue by contrasting the actual output of the manipulator with those of the neural network; Then, a time delay control (TDC) is applied to compensate the fault, in which a typical sliding mode command is used to delete the time delay estimate produced by the belated signal in order to obtain strong performances. The results of the simulations performed on a model of the SCARA arm manipulator, showed a good trajectory tracking and fast convergence speed in the presence of faults on the sensors. In addition, the command is completely model independent, for both TDC and MLP neural network, which represents a major advantage of the proposed command.

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