We present a framework for distributed control where the subsystems estimate overlapping components of the state of the overall system. This enables the implementation of decentralized state feedback controllers, which depend on the overlapping state estimates. For a distributed framework, communication can be added. By chosing the amount of communication and the degree of overlap in the state estimates, a trade-off between increasing computational effort, communication, and performance can be made. This approach is especially suited for systems with strongly coupled subsystems, which are restricted in communication during their operation. The computational effort of each subsystem depends only on the chosen degree of overlap in the estimates and thus stays constant with an increasing number of subsystems, which makes the approach convenient for the operation of large systems. The design problem of the distributed control and estimation is formulated with bilinear matrix inequalities in an augmented state space. A numerical example of a cooperative manipulation task illustrates the performance of the distributed control and estimation scheme.
