Self-adaptive output tracking with applications to active binocular tracking

Sisil Kumarawadu, Keigo Watanabe, Kazuo Kiguchi, Kiyotaka Izumi

研究成果: ジャーナルへの寄稿学術誌査読

2 被引用数 (Scopus)

抄録

In this article we present a neurally-inspired self-adaptive active binocular tracking scheme and an efficient mathematical model for online computation of desired binocular-head trajectories. The self-adaptive neural network (NN) model is general and can be adopted in output tracking schemes of any partly known robotic systems. The tracking scheme ingeniously combines the conventional Resolved Velocity Control (RVC) technique and an adaptive compensating NN model constructed using SoftMax basis functions as nonlinear activation function. Desired trajectories to the servo controller are computed online by the use of a suitable linear kinematics mathematical model of the system. Online weight tuning algorithm guarantees tracking with small errors and error rates as well as bounded NN weights.

本文言語英語
ページ(範囲)129-147
ページ数19
ジャーナルJournal of Intelligent and Robotic Systems: Theory and Applications
36
2
DOI
出版ステータス出版済み - 2月 2003
外部発表はい

!!!All Science Journal Classification (ASJC) codes

  • 人工知能
  • 制御およびシステム工学

フィンガープリント

「Self-adaptive output tracking with applications to active binocular tracking」の研究トピックを掘り下げます。これらがまとまってユニークなフィンガープリントを構成します。

引用スタイル