  We present a Stochastic Model Predictive Control (SMPC) framework for linear
systems subject to Gaussian disturbances. In order to avoid feasibility issues,
we employ a recent initialization strategy, optimizing over an interpolation of
the initial state between the current measurement and previous prediction. By
also considering the variance in the interpolation, we can employ variable-size
tubes, to ensure constraint satisfaction in closed-loop. We show that this
novel method improves control performance and enables following the constraint
closer, then previous methods. Using a DC-DC converter as numerical example we
illustrated the improvement over previous methods.
