Feedback error learning neural network applied to a scara robot

2004 ◽  
Author(s):  
F. Passold ◽  
M.R. Stemmer
Keyword(s):  
Neural Network ◽  
Scara Robot ◽  
Feedback Error Learning ◽  
Feedback Error ◽  
Error Learning

Vibration Control of the Piping System: A Method Using Feedback-Error-Learning

1995 ◽  
Author(s):  
Yoshihiro Takita
Keyword(s):  
Neural Network ◽  
Control System ◽  
Vibration Control ◽  
Control Method ◽  
Piping System ◽  
Processing Unit ◽  
Feedback Error Learning ◽  
Feedback Error ◽  
Error Learning ◽  
Lq Control

Abstract This paper presents a vibration control method for piping systems using a feedback control system constructed with LQ-control and a neural network featuring feedback-error learning. The piping system is normally flexible, therefore, natural frequencies of the system fluctuate variably when the density of the content. This paper shows that the piping system changes dynamics according to increases or decreases of the mass effects. In order to reduce the first vibration mode of the piping system without spillover instability, the control system is designed using LQ-control with feedback-error-learning applied to an adapted nonlinear feedback controller. The effectiveness of this control method is confirmed by the neural network simulation program named NeuroLab and is experimented using data measured by the control system constructed with the digital signal processing unit.

scholarly journals Robot Manipulators Control with Guaranteed Stability Using Feedback Error Learning Neural Networks

1996 ◽  
Vol 8 (4) ◽  
pp. 383-391
Author(s):  
Ju-Jang Lee ◽  
◽  
Sung-Woo Kim ◽  
Kang-Bark Park
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Learning Rule ◽  
Neural Network Learning ◽  
Network Learning ◽  
The Neural Network ◽  
Control Schemes ◽  
Feedback Error Learning ◽  
Feedback Error ◽  
Error Learning

Among various neural network learning control schemes, feedback error learning(FEL)8),9) has been known that it has advantages over other schemes. However, such advantages are founded on the assumption that the systems is linearly parameterized and stable. Thus, FEL has difficulties in coping with uncertain and unstable systems. Furthermore, it is not clear how the learning rule of FEL is obtained in the minimization sense. Therefore, to overcome such problems, we propose neural network control schemes using FEL with guaranteed performance. The proposed strategy is to use multi-layer neural networks, to design a stabilityguaranteeing controller(SGC), and to derive a learning rule to obtain the tracking performance. Using multilayer neural networks we can fully utilize the learning capability no matter how the system is linearly parameterized or not. The SGC makes it possible for the neural network to learn without fear of instability. As a result, the more the neural network learning proceeds, the better the tracking performance becomes.

Trajectory control of robotic manipulators by using a feedback-error-learning neural network

Robotica ◽  
1995 ◽  
Vol 13 (5) ◽  
pp. 449-459 ◽  
Author(s):  
Zaryab Hamavand ◽  
Howard M. Schwartz
Keyword(s):  
Neural Network ◽  
Inverse Dynamics ◽  
Robot Manipulator ◽  
A Priori ◽  
Trajectory Control ◽  
A Priori Knowledge ◽  
Feedback Error Learning ◽  
Feedback Error ◽  
Error Learning ◽  
Priori Knowledge

SummaryThis paper presents a neural network based control strategy for the trajectory control of robot manipulators. The neural network learns the inverse dynamics of a robot manipulator without any a priori knowledge of the manipulator inertial parameters nor any a priori knowledge of the equation of dynamics. A two step feedback-error-learning process is proposed. Strategies for selection of the training trajectories and difficulties with on-line training are discussed.

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