how can brain learn to control a nonholonomic system?

Clicks: 5
ID: 147968
2010
Free PDF
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This article has not been analysed, so there is no overall score — reader engagement is measured and shown alongside.
AI Quality Assessment
Not analyzed
Readership in this journal
Star

Ranked #22 of 23 articles by views in asse professional development conference 2017, safety 2017

Most read Least read

Bar heights use a square-root scale.

Mint this article as an NFT
Not yet minted

Create a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.

5 SUSD one-off · no wallet required
Sign in to mint
Abstract
Humans can often conduct both linear and nonlinear control tasks after a sufficient number of trials, even if they initially do not have sufficient knowledge about the system's dynamics and the way to control it. Theoretically, it is well known that some nonlinear systems cannot be stabilized asymptotically by any linear controllers and we have reported by an f-MRI experiment that different types of information may be involved in linear and nonlinear control tasks, respectively, from a brain function mapping point of view. In this paper, from a controllability analysis, we still show a possibility that human may use a linear control scheme for such nonlinear control tasks by switching the linear controllers with a virtual constraint. It is suggested that the proposed virtual constraint can play an important role to overcome a limitation of the linear controllers and to mimic human control behavior.
Reference Key
homma2010journalhow Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors ;Noriyasu Homma;Shinpei Kato;Takakuni Goto;Ivo Bukovsky;Ryuta Kawashima;Makoto Yoshizawa
Journal asse professional development conference 2017, safety 2017
Year 2010
DOI
10.1155/2010/919306
URL
http://dx.doi.org/10.1155/2010/919306
Keywords
Mechanical engineering and machinery

Citations

No citations found. To add a citation, contact the admin at info@scimatic.org

Comments

No comments yet. Be the first to comment on this article.