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Learning Constrained Adaptive Differentiable Predictive Control Policies With Guarantees
[article]
2022
arXiv
pre-print
We present differentiable predictive control (DPC), a method for learning constrained neural control policies for linear systems with probabilistic performance guarantees. We employ automatic differentiation to obtain direct policy gradients by backpropagating the model predictive control (MPC) loss function and constraints penalties through a differentiable closed-loop system dynamics model. We demonstrate that the proposed method can learn parametric constrained control policies to stabilize
arXiv:2004.11184v6
fatcat:sem2kvkrt5cnlhz2hgywkpmrge