  We analyse the conservatism and regret of distributionally robust (DR)
stochastic model predictive control (SMPC) when using moment-based ambiguity
sets for modeling unknown uncertainties. To quantify the conservatism, we
compare the deterministic constraint tightening while taking a DR approach
against the optimal tightening when the exact distributions of the stochastic
uncertainties are known. Furthermore, we quantify the regret by comparing the
performance when the distributions of the stochastic uncertainties are known
and unknown. Analysing the accumulated sub-optimality of SMPC due to the lack
of knowledge about the true distributions of the uncertainties marks the novel
contribution of this work.
