Safe and Near-Optimal Policy Learning for Model Predictive Control using Primal-Dual Neural Networks

@article{Zhang2019SafeAN,
  title={Safe and Near-Optimal Policy Learning for Model Predictive Control using Primal-Dual Neural Networks},
  author={Xiaojing Zhang and Monimoy Bujarbaruah and F. Borrelli},
  journal={2019 American Control Conference (ACC)},
  year={2019},
  pages={354-359}
}
In this paper, we propose a novel framework for approximating the explicit MPC law for linear parameter-varying systems using supervised learning. In contrast to most existing approaches, we not only learn the control policy, but also a “certificate policy”, that allows us to estimate the sub-optimality of the learned control policy online, during execution-time. We learn both these policies from data using supervised learning techniques, and also provide a randomized method that allows us to… Expand
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