34,237 research outputs found
Centralized Versus Decentralized Team Games of Distributed Stochastic Differential Decision Systems with Noiseless Information Structures-Part II: Applications
In this second part of our two-part paper, we invoke the stochastic maximum
principle, conditional Hamiltonian and the coupled backward-forward stochastic
differential equations of the first part [1] to derive team optimal
decentralized strategies for distributed stochastic differential systems with
noiseless information structures. We present examples of such team games of
nonlinear as well as linear quadratic forms. In some cases we obtain closed
form expressions of the optimal decentralized strategies.
Through the examples, we illustrate the effect of information signaling among
the decision makers in reducing the computational complexity of optimal
decentralized decision strategies.Comment: 39 pages Submitted to IEEE Transaction on Automatic Contro
Optimal Control for LQG Systems on Graphs---Part I: Structural Results
In this two-part paper, we identify a broad class of decentralized
output-feedback LQG systems for which the optimal control strategies have a
simple intuitive estimation structure and can be computed efficiently. Roughly,
we consider the class of systems for which the coupling of dynamics among
subsystems and the inter-controller communication is characterized by the same
directed graph. Furthermore, this graph is assumed to be a multitree, that is,
its transitive reduction can have at most one directed path connecting each
pair of nodes. In this first part, we derive sufficient statistics that may be
used to aggregate each controller's growing available information. Each
controller must estimate the states of the subsystems that it affects (its
descendants) as well as the subsystems that it observes (its ancestors). The
optimal control action for a controller is a linear function of the estimate it
computes as well as the estimates computed by all of its ancestors. Moreover,
these state estimates may be updated recursively, much like a Kalman filter
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