Abstract

The data revolution is having a remarkable impact over the management and design of manufacturing systems. While data are fruitfully used for diagnosis and monitoring of complex manufacturing processes, the optimization and control of manufacturing systems still rely on heuristics rules when a large set of cooperating machines is considered. In this paper, we explore an alternative way to extend the intelligence of local control to the system level, while explicitly accounting for the inherent system complexity. In particular, we propose the simulation-predictive control framework and we discuss how, according to the proposed approach, simulation has to be performed to allow the control of complex systems. In this direction, we show how simulation should be thought for control purposes and how the optimization/control routines should be seamlessly integrated within the simulation procedure. In order to show the advantage of the proposed approach, we test our control framework against a traditional simulation optimization algorithm and against a retrospective optimizer, which chooses the optimal actions with awareness of the future events. The promising results motivate us to further investigate the family of the approaches proposed herein.

Original languageEnglish (US)
Title of host publication2018 IEEE 14th International Conference on Automation Science and Engineering, CASE 2018
PublisherIEEE Computer Society
Pages1310-1315
Number of pages6
Volume2018-August
ISBN (Electronic)9781538635933
DOIs
StatePublished - Dec 4 2018
Event14th IEEE International Conference on Automation Science and Engineering, CASE 2018 - Munich, Germany
Duration: Aug 20 2018Aug 24 2018

Other

Other14th IEEE International Conference on Automation Science and Engineering, CASE 2018
CountryGermany
CityMunich
Period8/20/188/24/18

Fingerprint

Large scale systems
Monitoring

Keywords

  • Control
  • Manufacturing Systems
  • Modeling
  • Simulation

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Electrical and Electronic Engineering

Cite this

Pedrielli, G., & Ju, F. (2018). Simulation-Predictive Control for Manufacturing Systems. In 2018 IEEE 14th International Conference on Automation Science and Engineering, CASE 2018 (Vol. 2018-August, pp. 1310-1315). [8560408] IEEE Computer Society. https://doi.org/10.1109/COASE.2018.8560408

Simulation-Predictive Control for Manufacturing Systems. / Pedrielli, Giulia; Ju, Feng.

2018 IEEE 14th International Conference on Automation Science and Engineering, CASE 2018. Vol. 2018-August IEEE Computer Society, 2018. p. 1310-1315 8560408.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Pedrielli, G & Ju, F 2018, Simulation-Predictive Control for Manufacturing Systems. in 2018 IEEE 14th International Conference on Automation Science and Engineering, CASE 2018. vol. 2018-August, 8560408, IEEE Computer Society, pp. 1310-1315, 14th IEEE International Conference on Automation Science and Engineering, CASE 2018, Munich, Germany, 8/20/18. https://doi.org/10.1109/COASE.2018.8560408
Pedrielli G, Ju F. Simulation-Predictive Control for Manufacturing Systems. In 2018 IEEE 14th International Conference on Automation Science and Engineering, CASE 2018. Vol. 2018-August. IEEE Computer Society. 2018. p. 1310-1315. 8560408 https://doi.org/10.1109/COASE.2018.8560408
Pedrielli, Giulia ; Ju, Feng. / Simulation-Predictive Control for Manufacturing Systems. 2018 IEEE 14th International Conference on Automation Science and Engineering, CASE 2018. Vol. 2018-August IEEE Computer Society, 2018. pp. 1310-1315
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