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Data-driven Distributionally Robust MPC: An indirect feedback approach
[article]
2021
arXiv
pre-print
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
arXiv:2109.09558v1
fatcat:kbshmr3w7fcbtgsubfqek664l4