  This paper presents a novel distributed active set method for model
predictive control of linear systems. The method combines a primal active set
strategy with a decentralized conjugate gradient method to solve convex
quadratic programs. An advantage of the proposed method compared to existing
distributed model predictive algorithms is the primal feasibility of the
iterates. Numerical results show that the proposed method can compete with the
alternating direction method of multipliers in terms of communication
requirements for a chain of masses example.
