Result: Self-aware and learning structure

Title:
Self-aware and learning structure
Source:
Intelligent computing in engineering and architecture (13th EG-ICE Workshop 2006, Ascona, Switzerland, June 25-30, 2006)0EG-ICE 2006. :7-14
Publisher Information:
Berlin; New York: Springer, 2006.
Publication Year:
2006
Physical Description:
print, 25 ref 1
Original Material:
INIST-CNRS
Document Type:
Conference Conference Paper
File Description:
text
Language:
English
Author Affiliations:
Ecole Polytechnique Fédérale de Lausanne (EPFL), Applied Computing and Mechanics Laboratory, Station 18, 1015 Lausanne, Switzerland
ISSN:
0302-9743
Rights:
Copyright 2007 INIST-CNRS
CC BY 4.0
Sauf mention contraire ci-dessus, le contenu de cette notice bibliographique peut être utilisé dans le cadre d’une licence CC BY 4.0 Inist-CNRS / Unless otherwise stated above, the content of this bibliographic record may be used under a CC BY 4.0 licence by Inist-CNRS / A menos que se haya señalado antes, el contenido de este registro bibliográfico puede ser utilizado al amparo de una licencia CC BY 4.0 Inist-CNRS
Notes:
Building. Public works. Transport. Civil engineering

Computer science; theoretical automation; systems
Accession Number:
edscal.19152978
Database:
PASCAL Archive

Further Information

This study focuses on learning of control commands identification and load identification for active shape control of a tensegrity structure in situations of unknown loading event. Control commands are defined as sequences of contractions and elongations of active struts. Case-based reasoning strategies support learning. Simple retrieval and adaptation functions are proposed. They are derived from experimental results of load identification studies. The proposed algorithm leads to two types of learning: reduction of command identification time and increase of command quality over time. In the event of no retrieved case, load identification is performed. This methodology is based on measuring the response of the structure to current load and inferring the cause. It provides information in order to identify control commands through multi-objective search. Results are validated through experimental testing.