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.

Feedback-error-learning neural network for trajectory control of a robotic manipulator

1988 ◽  
Vol 1 (3) ◽  
pp. 251-265 ◽  
Author(s):  
Hiroyuki Miyamoto ◽  
Mitsuo Kawato ◽  
Tohru Setoyama ◽  
Ryoji Suzuki
Keyword(s):  
Neural Network ◽  
Robotic Manipulator ◽  
Trajectory Control ◽  
Feedback Error Learning ◽  
Feedback Error ◽  
Error Learning

scholarly journals KBNN Based on Coarse Mesh to Optimize the EBG Structures

2017 ◽  
Vol 2017 ◽  
pp. 1-9
Author(s):  
Yi Chen ◽  
Yu-bo Tian ◽  
Fei-yan Sun
Keyword(s):  
Neural Network ◽  
A Priori ◽  
Computing Time ◽  
Microwave Devices ◽  
A Priori Knowledge ◽  
Coarse Mesh ◽  
Empirical Formulas ◽  
Electromagnetic Problems ◽  
Mesh Model ◽  
Priori Knowledge

The microwave devices are usually optimized by combining the precise model with global optimization algorithm. However, this method is time-consuming. In order to optimize the microwave devices rapidly, the knowledge-based neural network (KBNN) is used in this paper. Usually, the a priori knowledge of KBNN is obtained by the empirical formulas. Unfortunately, it is difficult to derive the corresponding formulas for the most electromagnetic problems, especially for complex electromagnetic problems; the formula derivation is almost impossible. We use precise mesh model of EM analysis as teaching signal and coarse mesh model as a priori knowledge to train the neural network (NN) by particle swarm optimization (PSO). The NN constructed by this method is simpler than traditional NN in structure which can replace precise model in optimization and reduce the computing time. The results of electromagnetic band-gap (EBG) structures optimally designed by this kind of KBNN achieve increase in the bandwidth and attenuation of the stopband and small passband ripple level which shows the advantages of the proposed KBNN method.

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