Structured Information Processing For Self-optimizing Mechatronic Systems (bibtex)
by , ,
Abstract:
Self-optimizing mechatronic systems are based on intrinsic controller systems whose complexity by far exceeds that of currently available systems. In addition to procedures taken from artificial intelligence, procedures for a reconfiguration by means of appropriate design methods have to be integrated to fully implement self-optimization features. Special importance falls to a networking of such complex controller systems for the support of collaborative and emergent self-optimization. One main challenge lies in the safety-critical nature of the systems that requires the resulting software along with the technical system to show a predictably correct behavior in spite of networking, reconfiguration, and integration of procedures from artificial intelligence. The paper presents a concept for structuring and designing reconfigurable controller systems.
Reference:
Structured Information Processing For Self-optimizing Mechatronic Systems (Thorsten Hestermeyer, Oliver Oberschelp, Holger Giese), In Proc. of 1st International Conference on Informatics in Control, Automation and Robotics (ICINCO 2004), Setubal, Portugal (Helder Araujo, Alves Vieira, Jose Braz, Bruno Encarnacao, Marina Carvalho, eds.), INSTICC Press, 2004.
Bibtex Entry:
@InProceedings{Hestermeyer+2004,
AUTHOR = {Hestermeyer, Thorsten and Oberschelp, Oliver and Giese, Holger},
TITLE = {{Structured Information Processing For Self-optimizing Mechatronic Systems}},
YEAR = {2004},
MONTH = {August},
BOOKTITLE = {Proc. of 1st International Conference on Informatics in Control, Automation and Robotics (ICINCO 2004), Setubal, Portugal},
PAGES = {230-237},
EDITOR = {Araujo, Helder and Vieira, Alves and Braz, Jose and Encarnacao, Bruno and Carvalho, Marina},
PUBLISHER = {INSTICC Press},
URL = {http://www.upb.de/cs/ag-schaefer/Veroeffentlichungen/Quellen/Papers/2004/ICINCO2004b.pdf},
ABSTRACT = {Self-optimizing mechatronic systems are based on intrinsic controller systems whose complexity by far exceeds that of currently available systems. In addition to procedures taken from artificial intelligence, procedures for a reconfiguration by means of appropriate design methods have to be integrated to fully implement self-optimization features. Special importance falls to a networking of such complex controller systems for the support of collaborative and emergent self-optimization. One main challenge lies in the safety-critical nature of the systems that requires the resulting software along with the technical system to show a predictably correct behavior in spite of networking, reconfiguration, and integration of procedures from artificial intelligence. The paper presents a concept for structuring and designing reconfigurable controller systems.}
}
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