Development and adaptation of IEC 61499 automation and control applications with runtime variability models (bibtex)
by Roman Froschauer, Alois Zoitl, Paul Grünbacher
Abstract:
Developers maintaining distributed control systems are facing significant problems when dealing with changing customer requirements during the development and operation of industrial production facilities. Research has so far largely focused on implementation-level engineering challenges. Model-driven approaches are seen as promising for dealing with the increasing complexity of adapting distributed control systems. We present an approach for capturing architectural variability of distributed IEC 61499 automation and control systems based on product line variability models. Our automated approach supports decision-driven derivation and adaptation of systems at runtime.
Reference:
Development and adaptation of IEC 61499 automation and control applications with runtime variability models (Roman Froschauer, Alois Zoitl, Paul Grünbacher), In Proceedings 7th IEEE Int'l Conference on Industrial Informatics (INDIN 2009), 2009.
Bibtex Entry:
@Conference{Froschauer2009,
  author    = {Roman Froschauer and Alois Zoitl and Paul Grünbacher},
  booktitle = {Proceedings 7th IEEE Int'l Conference on Industrial Informatics (INDIN 2009)},
  title     = {Development and adaptation of IEC 61499 automation and control applications with runtime variability models},
  year      = {2009},
  month     = {june},
  pages     = {905-910},
  abstract  = {Developers maintaining distributed control systems are facing significant
	problems when dealing with changing customer requirements during
	the development and operation of industrial production facilities.
	Research has so far largely focused on implementation-level engineering
	challenges. Model-driven approaches are seen as promising for dealing
	with the increasing complexity of adapting distributed control systems.
	We present an approach for capturing architectural variability of
	distributed IEC 61499 automation and control systems based on product
	line variability models. Our automated approach supports decision-driven
	derivation and adaptation of systems at runtime.},
  doi       = {10.1109/INDIN.2009.5195923},
  issn      = {1935-4576},
}
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