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

TL;DR

This paper introduces a Model Predictive Control framework that quantifies failure risk using Priced Timed Automata to enhance the safety and robustness of manufacturing systems under uncertainty.

Contribution

It presents a novel control strategy integrating risk quantification into Model Predictive Control for manufacturing systems, balancing cost and failure risk.

Findings

Ensures fail-safe operation under uncertainties.

Balances cost minimization with risk reduction.

Demonstrates effectiveness through a manufacturing example.

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 by 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.