Performance Improvement of Robot Continuous-Path Operation Through Iterative Learning Using Neural Networks
Abstract
In this article, an approach to improving the performance of robot continuous-path operation is proposed. This approach utilizes a multilayer feedforward neural network to compensate for model uncertainty associated with the robotic operation. Closed-loop stability and performance are analyzed. It is shown that the closed-loop system is stable in the sense that all signals are bounded: it is further proved that the performance of the closed-loop system is improved in the sense that certain error measure of the closed-loop system decreases as the network learning process is iterated. These analytical results are confirmed by computer simulation. The effectiveness of the proposed approach is demonstrated through a laboratory experiment.
Cite
Text
Chen et al. "Performance Improvement of Robot Continuous-Path Operation Through Iterative Learning Using Neural Networks." Machine Learning, 1996. doi:10.1023/A:1018276705188Markdown
[Chen et al. "Performance Improvement of Robot Continuous-Path Operation Through Iterative Learning Using Neural Networks." Machine Learning, 1996.](https://mlanthology.org/mlj/1996/chen1996mlj-performance/) doi:10.1023/A:1018276705188BibTeX
@article{chen1996mlj-performance,
title = {{Performance Improvement of Robot Continuous-Path Operation Through Iterative Learning Using Neural Networks}},
author = {Chen, Peter C. Y. and Mills, James K. and Smith, Kenneth C.},
journal = {Machine Learning},
year = {1996},
pages = {191-220},
doi = {10.1023/A:1018276705188},
volume = {23},
url = {https://mlanthology.org/mlj/1996/chen1996mlj-performance/}
}