Published January 19, 2024 | Version v1
Conference paper Open

Real-Time Deep-Learning-Driven Parallel MPC


A novel real-time approximated MPC control policy based on deep learning is proposed to address the high computational burden of model predictive control (MPC) for large-scale systems and those with fast dynamics. This control method approximates the optimal solution of the distributed optimization problems in the ALADIN-based parallel MPC design framework, resulting in a highly effective approach that outperforms other well-known methods for solving the MPC design problem. The numerical case study shows promising results, demonstrating the potential of this approach for real-time implementation.


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FrontSeat – Fostering Opportunities Towards Slovak Excellence in Advanced Control for Smart Industries 101079342
European Commission