Risk-Averse Model Predictive Control for Priced Timed Automata

TL;DR

This paper introduces a risk-averse model predictive control framework for priced timed automata, balancing cost and risk to enhance the flexibility and robustness of cyber-physical systems under uncertainty.

Contribution

It presents a novel risk-averse PTA MPC approach that simultaneously minimizes cost and risk, improving decision-making under uncertainty in cyber-physical systems.

Findings

Better reaction to PTA changes compared to non-risk-averse methods

Effective in balancing cost and risk in control actions

Demonstrated application in manufacturing systems

Abstract

In this paper, we propose a Risk-Averse Priced Timed Automata (PTA) Model Predictive Control (MPC) framework to increase flexibility of cyber-physical systems. To improve flexibility in these systems, our risk-averse framework solves a multi-objective optimization problem to minimize the cost and risk, simultaneously. While minimizing cost ensures the least effort to achieve a task, minimizing risk provides guarantees on the feasibility of the task even during uncertainty. Our framework explores the trade-off between these two qualities to obtain risk-averse control actions. The solution of risk-averse PTA MPC dynamic decision-making algorithm reacts relatively better to PTA changes compared to PTA MPC without risk-averse feature. An example from manufacturing systems is presented to show the application of the proposed control strategy.