  This paper considers the optimal distributed control problem for a linear
stochastic multi-agent system (MAS). Due to the distributed nature of MAS
network, the information available to an individual agent is limited to its
vicinity. From the entire MAS aspect, this imposes the structural constraint on
the control law, making the optimal control law computationally intractable.
This paper attempts to relax such a structural constraint by expanding the
neighboring information for each agent to the entire MAS, enabled by the
distributed estimation algorithm embedded in each agent. By exploiting the
estimated information, each agent is not limited to interact with its
neighborhood but further establishing the `virtual interactions' with the
non-neighboring agents. Then the optimal distributed MAS control problem is
cast as a synthesized control-estimation problem. An iterative optimization
procedure is developed to find the control-estimation law, minimizing the
global objective cost of MAS.
