  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.
