25,892 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
Consensus with Linear Objective Maps
A consensus system is a linear multi-agent system in which agents communicate
to reach a so-called consensus state, defined as the average of the initial
states of the agents. Consider a more generalized situation in which each agent
is given a positive weight and the consensus state is defined as the weighted
average of the initial conditions. We characterize in this paper the weighted
averages that can be evaluated in a decentralized way by agents communicating
over a directed graph. Specifically, we introduce a linear function, called the
objective map, that defines the desired final state as a function of the
initial states of the agents. We then provide a complete answer to the question
of whether there is a decentralized consensus dynamics over a given digraph
which converges to the final state specified by an objective map. In
particular, we characterize not only the set of objective maps that are
feasible for a given digraph, but also the consensus dynamics that implements
the objective map. In addition, we present a decentralized algorithm to design
the consensus dynamics
Design of Optimal Sparse Feedback Gains via the Alternating Direction Method of Multipliers
We design sparse and block sparse feedback gains that minimize the variance
amplification (i.e., the norm) of distributed systems. Our approach
consists of two steps. First, we identify sparsity patterns of feedback gains
by incorporating sparsity-promoting penalty functions into the optimal control
problem, where the added terms penalize the number of communication links in
the distributed controller. Second, we optimize feedback gains subject to
structural constraints determined by the identified sparsity patterns. In the
first step, the sparsity structure of feedback gains is identified using the
alternating direction method of multipliers, which is a powerful algorithm
well-suited to large optimization problems. This method alternates between
promoting the sparsity of the controller and optimizing the closed-loop
performance, which allows us to exploit the structure of the corresponding
objective functions. In particular, we take advantage of the separability of
the sparsity-promoting penalty functions to decompose the minimization problem
into sub-problems that can be solved analytically. Several examples are
provided to illustrate the effectiveness of the developed approach.Comment: To appear in IEEE Trans. Automat. Contro
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