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A data-driven robust optimization approach to stochastic model predictive control. (arXiv:1807.05146v1 [math.OC])
来源于:arXiv
Stochastic model predictive control (SMPC) has been a promising solution to
complex control problems under uncertain disturbances. However, traditional
SMPC approaches either require exact knowledge of probabilistic distributions,
or rely on massive scenarios that are generated to represent uncertainties. In
this paper, a novel SMPC approach is proposed by actively learning a
data-driven uncertainty set from available data with machine learning
techniques. A systematical procedure is then proposed to further calibrate the
uncertainty set, which gives appropriate probabilistic guarantee. The resulting
data-driven uncertainty set is more compact than traditional norm-based sets,
and can help reducing conservatism of control actions. Meanwhile, the proposed
method requires less data samples than traditional scenario-based SMPC
approaches, thereby enhancing the practicability of SMPC. Finally the optimal
control problem is cast as a single-stage robust optimization problem, which
can be so 查看全文>>