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

Research output: Contribution to conference › Paper › peer-review

Abstract

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.

Original language	English
Pages	483-488
Number of pages	6
Publication status	Published - Jan 1 2002
Externally published	Yes
Event	2002 International Joint Conference on Neural Networks (IJCNN '02) - Honolulu, HI, United States Duration: May 12 2002 → May 17 2002

Other

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

ASJC Scopus subject areas

Software
Artificial Intelligence

Cite this

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title = "Adaptive output tracking of partly known robotic systems using SoftMax function networks",

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AU - Kumarawadu, Sisil

AU - Watanabe, Keigo

AU - Kiguchi, Kazuo

AU - Izumi, Kiyotaka

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N2 - 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.

AB - 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.

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T2 - 2002 International Joint Conference on Neural Networks (IJCNN '02)

Y2 - 12 May 2002 through 17 May 2002

ER -

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

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