Model Predictive Control (MPC) is a traditional technique widely employed for the control of constrained nonlinear systems [1]. It offers several advantages over classical control techniques, as it can anticipate the system behavior, naturally considering hard constraints on the optimization problem. Lately, data-driven MPC has emerged as an alternative to physics-based modelling when sufficient data is available. Nonetheless, there has been limited progress in mitigating the computational burden of MPC on real-time applications. In this work, we propose to use Imitation Learning to train a feed-forward neural network (NN) using data collected from an offline MPC simulation. The goal is to replace the constrained optimization problem by learning from an expert’s behavior, alleviating the computation burden on real-time applications [2]. The network performance is then compared to that of traditional MPC using the Van der Pol oscillator as toy example. Finally, we study the control of an open-die forging (for process monitoring) and a damageable wind turbine blade (for structural health monitoring) to validate the proposed strategy on complex problems. The NN is trained using two strategies: supervised and unsupervised learning. Results show that both learning methods yield similar closed-loop performance while reducing by a factor 10 the computation burden of the controller. Acknowledgement: this project has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation program under grant agreement No. 101002857. REFERENCES [1] Max Schwenzer, Muzaffer Ay, Thomas Bergs, and Dirk Abel. Review on model predictive control: an engineering perspective. The International Journal of Advanced Manufacturing Technology, 117:1–23, 11 2021. [2] Le Mero, L., Yi, D., Dianati, M., & Mouzakitis, A. (2022). A Survey on Imitation Learning Techniques for End-to-End Autonomous Vehicles. IEEE Transactions on Intelligent Transportation Systems, 23(9), 14128-14147. https://doi.org/10.1109/TITS.2022.3144867