1,305 research outputs found
On the genericity properties in networked estimation: Topology design and sensor placement
In this paper, we consider networked estimation of linear, discrete-time
dynamical systems monitored by a network of agents. In order to minimize the
power requirement at the (possibly, battery-operated) agents, we require that
the agents can exchange information with their neighbors only \emph{once per
dynamical system time-step}; in contrast to consensus-based estimation where
the agents exchange information until they reach a consensus. It can be
verified that with this restriction on information exchange, measurement fusion
alone results in an unbounded estimation error at every such agent that does
not have an observable set of measurements in its neighborhood. To over come
this challenge, state-estimate fusion has been proposed to recover the system
observability. However, we show that adding state-estimate fusion may not
recover observability when the system matrix is structured-rank (-rank)
deficient.
In this context, we characterize the state-estimate fusion and measurement
fusion under both full -rank and -rank deficient system matrices.Comment: submitted for IEEE journal publicatio
Engineering Emergence: A Survey on Control in the World of Complex Networks
Complex networks make an enticing research topic that has been increasingly attracting researchers from control systems and various other domains over the last two decades. The aim of this paper was to survey the interest in control related to complex networks research over time since 2000 and to identify recent trends that may generate new research directions. The survey was performed for Web of Science, Scopus, and IEEEXplore publications related to complex networks. Based on our findings, we raised several questions and highlighted ongoing interests in the control of complex networks.publishedVersio
Model Reduction Methods for Complex Network Systems
Network systems consist of subsystems and their interconnections, and provide
a powerful framework for analysis, modeling and control of complex systems.
However, subsystems may have high-dimensional dynamics, and the amount and
nature of interconnections may also be of high complexity. Therefore, it is
relevant to study reduction methods for network systems. An overview on
reduction methods for both the topological (interconnection) structure of the
network and the dynamics of the nodes, while preserving structural properties
of the network, and taking a control systems perspective, is provided. First
topological complexity reduction methods based on graph clustering and
aggregation are reviewed, producing a reduced-order network model. Second,
reduction of the nodal dynamics is considered by using extensions of classical
methods, while preserving the stability and synchronization properties.
Finally, a structure-preserving generalized balancing method for simplifying
simultaneously the topological structure and the order of the nodal dynamics is
treated.Comment: To be published in Annual Review of Control, Robotics, and Autonomous
System
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