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We present a stochastic model predictive control (MPC) method for lineardiscrete-time systems subject to possibly unbounded and correlated additivestochastic disturbance sequences. Chance constraints are treated in analogy torobust MPC using the concept of probabilistic reachable sets for constrainttightening. We introduce an initialization of each MPC iteration which isalways recursively feasibility and thereby allows that chance constraintsatisfaction for the closed-loop system can readily be shown. Under an i.i.d.zero mean assumption on the additive disturbance, we furthermore provide anaverage asymptotic performance bound. Two examples illustrate the approach,highlighting feedback properties of the novel initialization scheme, as well asthe inclusion of time-varying, correlated disturbances in a building controlsetting.

Recursively Feasible Stochastic Model Predictive Control using Indirect Feedback