Large Scale Model Predictive Control with Neural Networks and Primal Active Sets

Understand

This work presents an explicit-implicit procedure to compute a model predictive control (MPC) law with guarantees on recursive feasibility and asymptotic stability.

• The approach combines an offline-trained fully-connected neural network with an online primal active set solver.
• The neural network provides a control input initialization while the primal active set method ensures recursive feasibility and asymptotic stability.
• The neural network is trained with a primal-dual loss function, aiming to generate control sequences that are primal feasible and meet a desired level of suboptimality.