  This paper is concerned with the design of cooperative distributed Model
Predictive Control (MPC) for linear systems. Motivated by the special structure
of the distributed models in some existing literature, we propose to apply a
state transformation to the original system and global cost function. This has
major implications on the closed-loop stability analysis and the mechanism of
the resultant cooperative framework. It turns out that the proposed framework
can be implemented without cooperative iterations being performed in the local
optimizations, thus allowing one to compute the local inputs in parallel and
independently from each other while requiring only partial plant-wide state
information. The proposed framework can also be realized with cooperative
iterations, thereby keeping the advantages of the technique in the former
reference. Under certain conditions, closed-loop stability for both
implementation procedures can be guaranteed a priori by appropriate selections
of the original local cost functions. The strengths and benefits of the
proposed method are highlighted by means of two numerical examples.
