A soft constraint approach to stochastic receding horizon control

This paper presents a soft constraint approach to constrained stochastic receding horizon control for linear systems with state and control multiplicative noise. We formulate an on-line optimization that penalizes constraint violations and can be solved as a semi-definite program. Additionally, we prove stability results that guarantee asymptotic stability with probability one. A simple numerical example illustrates the approach.