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Point to Point ILC with Initial State Learning using Neural Networks
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 178 - Number 3 |
| Year of Publication: 2017 |
| Authors: Haris Anwaar, Yin YiXin, Muhammad Ammar Ashraf, Salman Ijaz |
10.5120/ijca2017915794
|
Haris Anwaar, Yin YiXin, Muhammad Ammar Ashraf, Salman Ijaz . Point to Point ILC with Initial State Learning using Neural Networks. International Journal of Computer Applications. 178, 3 ( Nov 2017), 35-38. DOI=10.5120/ijca2017915794
Abstract
Point to Point ILC involves the tracking of specific points during motion in a repetitive manner. Point to point ILC makes the assumption that initial starting position of each trial remains same. In this paper, initial starting position of point to point motion in each trial is learned using neural networks. The proposed algorithm can also track the points which are changing in respective trials. The algorithm is checked for three points tracking during a trial, which are changing in sinusoidal manner. The results are shown by simulations in the end.
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