Recursively Feasible Stochastic Predictive Control Using an Interpolating Initial State Constraint

We present a stochastic model predictive control (SMPC) framework for linear systems subject to possibly unbounded disturbances. State of the art SMPC approaches with closed-loop chance constraint satisfaction recursively initialize nominal state based on previously predicted or measured under some case distinction. improve these initialization strategies by allowing continuous optimization over initial in an interpolation two extremes. The resulting scheme can be implemented as one standard quadratic program and is more flexible compared state-of-the-art strategies. As main technical contribution, we show that proposed also ensures constraints suitable performance bounds.