  This paper presents a Distributed Stochastic Model Predictive Control
algorithm for networks of linear systems with multiplicative uncertainties and
local chance constraints on the states and control inputs. The chance
constraints are approximated via Cantelli's inequality by means of expected
value and covariance. The cooperative control algorithm is based on the
distributed Alternating Direction Method of Multipliers, which renders the
controller fully distributedly implementable, recursively feasible and ensures
point-wise convergence of the states. The aforementioned properties are
guaranteed through a properly selected distributed invariant set and
distributed terminal constraints for the mean and covariance. The paper closes
with an example highlighting the chance constraint satisfaction, numerical
properties and scalability of our approach.
