Data-driven Distributionally Robust MPC: An indirect feedback approach
Optimization and Control
2021-09-21 v1
Abstract
This paper presents a distributionally robust stochastic model predictive control (SMPC) approach for linear discrete-time systems subject to unbounded and correlated additive disturbances. We consider hard input constraints and state chance constraints, which are approximated as distributionally robust (DR) Conditional Value-at-Risk (CVaR) constraints over a Wasserstein ambiguity set. The computational complexity is reduced by resorting to a tube-based MPC scheme with indirect feedback, such that the error scenarios can be sampled offline. Recursive feasibility is guaranteed by softening the CVaR constraint. The approach is demonstrated on a four-room temperature control example.
Cite
@article{arxiv.2109.09558,
title = {Data-driven Distributionally Robust MPC: An indirect feedback approach},
author = {Christoph Mark and Steven Liu},
journal= {arXiv preprint arXiv:2109.09558},
year = {2021}
}