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

19 Jun 2019Xiaojing ZhangMonimoy BujarbaruahFrancesco Borrelli

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... (read more)

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