A model-based neural network trained with a cost-based loss predicts co-states and, via a QP, produces constrained control that matches or beats nonlinear MPC on a unicycle at far lower compute.
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Neural Co-state Regulator: A Data-Driven Paradigm for Real-time Optimal Control with Input Constraints
A model-based neural network trained with a cost-based loss predicts co-states and, via a QP, produces constrained control that matches or beats nonlinear MPC on a unicycle at far lower compute.