0

Full Content is available to subscribers

Subscribe/Learn More  >

Second Order Sliding Mode Neural Network-Based Visual Servoing of Robot Hands With Fast Manipulation, Including Transition

[+] Author Affiliations
Rodolfo García-Rodríguez, Francisco Ruíz-Sánchez

CINVESTAV-PIN

V. Parra-Vega

CINVESTAV-IPN

Paper No. IMECE2005-82382, pp. 1713-1722; 10 pages
doi:10.1115/IMECE2005-82382
From:
  • ASME 2005 International Mechanical Engineering Congress and Exposition
  • Dynamic Systems and Control, Parts A and B
  • Orlando, Florida, USA, November 5 – 11, 2005
  • Conference Sponsors: Dynamic Systems and Control Division
  • ISBN: 0-7918-4216-9 | eISBN: 0-7918-3769-6
  • Copyright © 2005 by ASME

abstract

Strictly speaking, transition tasks such as those executed by robot hands involve free, impact, and constrained motion regimes, with changing dynamics. Impulsive, unilateral constraints arises in the impact regime, which makes very difficult to design a control system. Moreover, algebraic constraints arise in the constrained regime. The trivial approach would be to avoid impact, and to commute consistently ODE- and DAE-based controller, or to impose virtual constraints to model as a DAE system all regimes. In any case, it is required to know exactly the commuting time. In this paper, a very simple control scheme is proposed based on avoiding impact regime, through zero transition velocity from free to constrained motion, therefore impulsive dynamics does not appear. This is possible because we guarantee exactly the time to commute with a novel well-posed finite time convergence scheme, to produce convergence toward any desired trajectory at any given arbitrarily time and for any initial condition. In this way, ODE and DAE dynamics/controllers commute stably. Inertial and gravitational forces are compensated by a recurrent neural network driven by image-based position and force tracking errors, with a decentralized structure for each robot. The network is tuned on line with a second order force-position sliding modes to finally guarantee exponential tracking.

Copyright © 2005 by ASME
Topics: Robots , Networks

Figures

Tables

Interactive Graphics

Video

Country-Specific Mortality and Growth Failure in Infancy and Yound Children and Association With Material Stature

Use interactive graphics and maps to view and sort country-specific infant and early dhildhood mortality and growth failure data and their association with maternal

NOTE:
Citing articles are presented as examples only. In non-demo SCM6 implementation, integration with CrossRef’s "Cited By" API will populate this tab (http://www.crossref.org/citedby.html).

Some tools below are only available to our subscribers or users with an online account.

Related Content

Customize your page view by dragging and repositioning the boxes below.

Related eBook Content
Topic Collections

Sorry! You do not have access to this content. For assistance or to subscribe, please contact us:

  • TELEPHONE: 1-800-843-2763 (Toll-free in the USA)
  • EMAIL: asmedigitalcollection@asme.org
Sign In