Distributional Robustness Regularized Scenario Optimization with Application to Model Predictive Control
Systems and Control
2021-10-27 v1 Systems and Control
Optimization and Control
Abstract
We provide a functional view of distributional robustness motivated by robust statistics and functional analysis. This results in two practical computational approaches for approximate distributionally robust nonlinear optimization based on gradient norms and reproducing kernel Hilbert spaces. Our method can be applied to the settings of statistical learning with small sample size and test distribution shift. As a case study, we robustify scenario-based stochastic model predictive control with general nonlinear constraints. In particular, we demonstrate constraint satisfaction with only a small number of scenarios under distribution shift.
Cite
@article{arxiv.2110.13588,
title = {Distributional Robustness Regularized Scenario Optimization with Application to Model Predictive Control},
author = {Yassine Nemmour and Bernhard Schölkopf and Jia-Jie Zhu},
journal= {arXiv preprint arXiv:2110.13588},
year = {2021}
}