2,635 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
Static Output Feedback: On Essential Feasible Information Patterns
In this paper, for linear time-invariant plants, where a collection of
possible inputs and outputs are known a priori, we address the problem of
determining the communication between outputs and inputs, i.e., information
patterns, such that desired control objectives of the closed-loop system (for
instance, stabilizability) through static output feedback may be ensured.
We address this problem in the structural system theoretic context. To this
end, given a specified structural pattern (locations of zeros/non-zeros) of the
plant matrices, we introduce the concept of essential information patterns,
i.e., communication patterns between outputs and inputs that satisfy the
following conditions: (i) ensure arbitrary spectrum assignment of the
closed-loop system, using static output feedback constrained to the information
pattern, for almost all possible plant instances with the specified structural
pattern; and (ii) any communication failure precludes the resulting information
pattern from attaining the pole placement objective in (i).
Subsequently, we study the problem of determining essential information
patterns. First, we provide several necessary and sufficient conditions to
verify whether a specified information pattern is essential or not. Further, we
show that such conditions can be verified by resorting to algorithms with
polynomial complexity (in the dimensions of the state, input and output).
Although such verification can be performed efficiently, it is shown that the
problem of determining essential information patterns is in general NP-hard.
The main results of the paper are illustrated through examples
Controllability Metrics, Limitations and Algorithms for Complex Networks
This paper studies the problem of controlling complex networks, that is, the
joint problem of selecting a set of control nodes and of designing a control
input to steer a network to a target state. For this problem (i) we propose a
metric to quantify the difficulty of the control problem as a function of the
required control energy, (ii) we derive bounds based on the system dynamics
(network topology and weights) to characterize the tradeoff between the control
energy and the number of control nodes, and (iii) we propose an open-loop
control strategy with performance guarantees. In our strategy we select control
nodes by relying on network partitioning, and we design the control input by
leveraging optimal and distributed control techniques. Our findings show
several control limitations and properties. For instance, for Schur stable and
symmetric networks: (i) if the number of control nodes is constant, then the
control energy increases exponentially with the number of network nodes, (ii)
if the number of control nodes is a fixed fraction of the network nodes, then
certain networks can be controlled with constant energy independently of the
network dimension, and (iii) clustered networks may be easier to control
because, for sufficiently many control nodes, the control energy depends only
on the controllability properties of the clusters and on their coupling
strength. We validate our results with examples from power networks, social
networks, and epidemics spreading
Structural Completeness of a Multi-channel Linear System with Dependent Parameters
It is well known that the "fixed spectrum" {i.e., the set of fixed modes} of
a multi-channel linear system plays a central role in the stabilization of such
a system with decentralized control. A parameterized multi-channel linear
system is said to be "structurally complete" if it has no fixed spectrum for
almost all parameter values. Necessary and sufficient algebraic conditions are
presented for a multi-channel linear system with dependent parameters to be
structurally complete. An equivalent graphical condition is also given for a
certain type of parameterization
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