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

Sisil Kumarawadu, Keigo Watanabe, Kazuo Kiguchi, Kiyotaka Izumi

Research output: Contribution to conferencePaper

3 Citations (Scopus)

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 languageEnglish
Pages483-488
Number of pages6
Publication statusPublished - Jan 1 2002
Externally publishedYes
Event2002 International Joint Conference on Neural Networks (IJCNN '02) - Honolulu, HI, United States
Duration: May 12 2002May 17 2002

Other

Other2002 International Joint Conference on Neural Networks (IJCNN '02)
CountryUnited States
CityHonolulu, HI
Period5/12/025/17/02

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence

Fingerprint Dive into the research topics of 'Adaptive output tracking of partly known robotic systems using SoftMax function networks'. Together they form a unique fingerprint.

  • Cite this

    Kumarawadu, S., Watanabe, K., Kiguchi, K., & Izumi, K. (2002). Adaptive output tracking of partly known robotic systems using SoftMax function networks. 483-488. Paper presented at 2002 International Joint Conference on Neural Networks (IJCNN '02), Honolulu, HI, United States.