A Model Predictive Control Framework for Improving Risk-Tolerance of Manufacturing Systems

Mostafa Tavakkoli Anbarani, Efe C. Balta, Romulo Meira-Goes, Ilya Kovalenko

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

Abstract

The need for control strategies that can address dynamic system uncertainty is becoming increasingly important. In this work, we propose a Model Predictive Control for quantifying the risk of failure in our system model. The proposed control scheme uses a Priced Timed Automata representation of the manufacturing system to promote the fail-safe operation of systems under uncertainties. The proposed method ensures that in case of unforeseen failure(s), the optimization-based control strategy can still achieve the manufacturing system objective. In addition, the proposed strategy establishes a trade-off between minimizing the cost and reducing failure risk to allow the manufacturing system to function effectively in the presence of uncertainties. An example from manufacturing systems is presented to show the application of the proposed control strategy.

Original languageEnglish (US)
Title of host publication2023 IEEE Conference on Control Technology and Applications, CCTA 2023
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages136-142
Number of pages7
ISBN (Electronic)9798350335446
DOIs
StatePublished - 2023
Event2023 IEEE Conference on Control Technology and Applications, CCTA 2023 - Bridgetown, Barbados
Duration: Aug 16 2023Aug 18 2023

Publication series

Name2023 IEEE Conference on Control Technology and Applications, CCTA 2023

Conference

Conference2023 IEEE Conference on Control Technology and Applications, CCTA 2023
Country/TerritoryBarbados
CityBridgetown
Period8/16/238/18/23

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Control and Systems Engineering
  • Control and Optimization

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