25,916 research outputs found
Unified Approach to Convex Robust Distributed Control given Arbitrary Information Structures
We consider the problem of computing optimal linear control policies for
linear systems in finite-horizon. The states and the inputs are required to
remain inside pre-specified safety sets at all times despite unknown
disturbances. In this technical note, we focus on the requirement that the
control policy is distributed, in the sense that it can only be based on
partial information about the history of the outputs. It is well-known that
when a condition denoted as Quadratic Invariance (QI) holds, the optimal
distributed control policy can be computed in a tractable way. Our goal is to
unify and generalize the class of information structures over which quadratic
invariance is equivalent to a test over finitely many binary matrices. The test
we propose certifies convexity of the output-feedback distributed control
problem in finite-horizon given any arbitrarily defined information structure,
including the case of time varying communication networks and forgetting
mechanisms. Furthermore, the framework we consider allows for including
polytopic constraints on the states and the inputs in a natural way, without
affecting convexity
Reducing Cascading Failure Risk by Increasing Infrastructure Network Interdependency
Increased coupling between critical infrastructure networks, such as power
and communication systems, will have important implications for the reliability
and security of these systems. To understand the effects of power-communication
coupling, several have studied interdependent network models and reported that
increased coupling can increase system vulnerability. However, these results
come from models that have substantially different mechanisms of cascading,
relative to those found in actual power and communication networks. This paper
reports on two sets of experiments that compare the network vulnerability
implications resulting from simple topological models and models that more
accurately capture the dynamics of cascading in power systems. First, we
compare a simple model of topological contagion to a model of cascading in
power systems and find that the power grid shows a much higher level of
vulnerability, relative to the contagion model. Second, we compare a model of
topological cascades in coupled networks to three different physics-based
models of power grids coupled to communication networks. Again, the more
accurate models suggest very different conclusions. In all but the most extreme
case, the physics-based power grid models indicate that increased
power-communication coupling decreases vulnerability. This is opposite from
what one would conclude from the coupled topological model, in which zero
coupling is optimal. Finally, an extreme case in which communication failures
immediately cause grid failures, suggests that if systems are poorly designed,
increased coupling can be harmful. Together these results suggest design
strategies for reducing the risk of cascades in interdependent infrastructure
systems
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