TY - GEN
T1 - A Model Predictive Control Framework for Improving Risk-Tolerance of Manufacturing Systems
AU - Anbarani, Mostafa Tavakkoli
AU - Balta, Efe C.
AU - Meira-Goes, Romulo
AU - Kovalenko, Ilya
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85173817940&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85173817940&partnerID=8YFLogxK
U2 - 10.1109/CCTA54093.2023.10253433
DO - 10.1109/CCTA54093.2023.10253433
M3 - Conference contribution
AN - SCOPUS:85173817940
T3 - 2023 IEEE Conference on Control Technology and Applications, CCTA 2023
SP - 136
EP - 142
BT - 2023 IEEE Conference on Control Technology and Applications, CCTA 2023
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2023 IEEE Conference on Control Technology and Applications, CCTA 2023
Y2 - 16 August 2023 through 18 August 2023
ER -