1,145 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
Output consensus of nonlinear multi-agent systems with unknown control directions
In this paper, we consider an output consensus problem for a general class of
nonlinear multi-agent systems without a prior knowledge of the agents' control
directions. Two distributed Nussbaumtype control laws are proposed to solve the
leaderless and leader-following adaptive consensus for heterogeneous multiple
agents. Examples and simulations are given to verify their effectivenessComment: 10 pages;2 figure
Leader-following Consensus of Multi-agent Systems over Finite Fields
The leader-following consensus problem of multi-agent systems over finite
fields is considered in this paper. Dynamics of each agent is
governed by a linear equation over , where a distributed control
protocol is utilized by the followers.Sufficient and/or necessary conditions on
system matrices and graph weights in are provided for the
followers to track the leader
State Omniscience for Cooperative Local Catalog Maintenance of Close Proximity Satellite Systems
Resiliency in multi-agent system navigation is reliant on the inherent ability of the system to withstand, overcome, or recover from adverse conditions and disturbances. In large part, resiliency is achieved through reducing the impact of critical failure points to the success and/or performance of the system. In this view, decentralized multi-agent architectures have become an attractive solution for multi-agent navigation, but decentralized architectures place the burden of information acquisition directly on the agents themselves. In fact, the design of distributed estimators has been a growing interest to enable complex multi-sensor/multi-agent tasks. In such scenarios, it is important that each local estimator converges to the true global system state - a quality known as state omniscience. Most previous related work has focused on the design of such systems under varying assumptions on the graph topology with simplified information fusion schemes. Contrarily, this work introduces characterizations of state omniscience under generalized graph topologies and generalized information fusion schemes. State omniscience is discussed analogously to observability from classical control theory; and a collection of necessary and sufficient conditions for a distributed estimator to be state omniscient are presented. This dissertation discusses this phenomena in terms of an on-orbit scenarios dubbed the local catalog maintenance problem and the cooperative local catalog maintenance problem. The goal of each agent is to maintain their catalog of all bodies (objects and agents) within this neighborhood through onboard angles-only measurements and cooperative communication with the other agents. A central supervisor selects the target body for each agent, a local controller tracks the selected target body for each agent, and a local estimator coalesces both an agent\u27s measurements and state estimates provided by neighboring agents within the communication graph. Numerical results are provided to demonstrate the supervisor\u27s ability to select an appropriate target subject to an uncertainty threshold, the controller\u27s ability to track the selected target, and the estimator\u27s ability to maintain an accurate and precise estimate of each of the bodies in the local environment
Gossip Algorithms for Distributed Signal Processing
Gossip algorithms are attractive for in-network processing in sensor networks
because they do not require any specialized routing, there is no bottleneck or
single point of failure, and they are robust to unreliable wireless network
conditions. Recently, there has been a surge of activity in the computer
science, control, signal processing, and information theory communities,
developing faster and more robust gossip algorithms and deriving theoretical
performance guarantees. This article presents an overview of recent work in the
area. We describe convergence rate results, which are related to the number of
transmitted messages and thus the amount of energy consumed in the network for
gossiping. We discuss issues related to gossiping over wireless links,
including the effects of quantization and noise, and we illustrate the use of
gossip algorithms for canonical signal processing tasks including distributed
estimation, source localization, and compression.Comment: Submitted to Proceedings of the IEEE, 29 page
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