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Patterns for distributed machine control systems

Published: 10 July 2013 Publication History

Abstract

Control systems are getting more and more complex and include growing number of features. The control systems are nowadays software controlled to high degree. All features of the machine, however, are not useful for all customers and customers do not want to pay for the features they do not need. The product, the work machine, needs to be tailored to fit the customer's needs. However, tailoring the product for each customer is not an option as there would be too many control system software versions and configurations of the control system and for example updating the control system software would became impossible. In this paper, we will present three patterns for control systems to help in addressing the aforementioned problems. These patterns generate software architecture that supports varying the control system for different customer needs.

References

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EuroPLoP '13: Proceedings of the 18th European Conference on Pattern Languages of Program
July 2013
384 pages
ISBN:9781450334655
DOI:10.1145/2739011
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 10 July 2013

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Author Tags

  1. architecture patterns
  2. configuration management
  3. distributed control systems
  4. pattern language
  5. product management

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EuroPLoP 2013

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EuroPLoP '13 Paper Acceptance Rate 24 of 36 submissions, 67%;
Overall Acceptance Rate 216 of 354 submissions, 61%

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