Point to Point ILC with Initial State Learning using Neural Networks | Zendy

Haris Anwaar | Zendy; Yin Yi-xin | Zendy; Muhammad Ammar | Zendy; Salman Ijaz | Zendy

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Point to Point ILC with Initial State Learning using Neural Networks

Author(s) -

Haris Anwaar,

Yin Yi-xin,

Muhammad Ammar,

Salman Ijaz

Publication year - 2017

Publication title -

international journal of computer applications

Language(s) - English

Resource type - Journals

ISSN - 0975-8887

DOI - 10.5120/ijca2017915794

Subject(s) - computer science , point (geometry) , artificial neural network , state (computer science) , artificial intelligence , algorithm , mathematics , geometry

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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