Adaptive output tracking of partly known robotic systems using SoftMax function networks

Sisil Kumarawadu; Keigo Watanabe; Kazuo Kiguchi; Kiyotaka Izumi

Adaptive output tracking of partly known robotic systems using SoftMax function networks

Sisil Kumarawadu, Keigo Watanabe, Kazuo Kiguchi, Kiyotaka Izumi

研究成果 › 査読

抄録

In this paper, a neural-network-based adaptive control scheme is presented to solve the output-tracking problem of a robotic system with unknown nonlinearities. The control scheme ingeniously combines the conventional Resolved Velocity Control (RVC) technique and a neurally-inspired adaptive compensating paradigm constructed using SoftMax function networks and Neural Gas (NG) algorithm. Results of simulations on our active binocular head are reported. The neural network (NN) model is constructed to have two neural subnets to separately take care of robot head's neck and eye control simplifying the design and making for faster weight tuning algorithms.

本文言語	English
ページ	483-488
ページ数	6
出版ステータス	Published - 1月 1 2002
外部発表	はい
イベント	2002 International Joint Conference on Neural Networks (IJCNN '02) - Honolulu, HI 継続期間: 5月 12 2002 → 5月 17 2002

Other

Other	2002 International Joint Conference on Neural Networks (IJCNN '02)
国/地域	United States
City	Honolulu, HI
Period	5/12/02 → 5/17/02

ASJC Scopus subject areas

ソフトウェア
人工知能

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引用スタイル

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Adaptive output tracking of partly known robotic systems using SoftMax function networks

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