Data-driven MPC for Real-time Control of an Open-die Forging Problem

Model Predictive Control (MPC) is a traditional technique widely employed to control constrained nonlinear systems. Recently, data-driven MPC has emerged as an alternative to explicit MPC strategies when sufficient data are available. However, there has been limited progress in approximating nonlinear MPC to alleviate the computational burden for real-time applications while ensuring constraint satisfaction. In this paper, we use a feed-forward neural network to approximate a classical MPC controller, thereby reducing computational complexity. To guarantee constraint satisfaction, we project the network's prediction onto a control invariant set. We apply the proposed strategy in a simulation of an open-die forging process, which is highly nonlinear and prone to delays.