English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

This paper studies a distributed state estimation problem for bothcontinuous- and discrete-time linear systems. A simply structured distributedestimator (comprising interconnected local estimators) is first described forestimating the state of a continuous and multi-channel linear system whosesensed outputs are distributed across a fixed multi-agent network. Theestimator is then extended to non-stationary networks whose graphs switchaccording to a switching signal. The estimator is guaranteed to solve theproblem, provided a network-widely shared high gain condition achieving a formof spectrum separation is satisfied. As an alternative to sharing a common gainacross the network, a fully distributed version of the estimator is alsostudied in which each agent adaptively adjusts a local gain, though thepracticality of this approach is subject to a robustness issue common toadaptive control. A discrete-time version of the distributed state estimationproblem is also studied, and a corresponding estimator based again on spectrumseparation, but not high gain, is proposed for time-varying networks. For eachscenario, it is explained how to construct the estimator so that the stateestimation errors in the local estimators all converge to zero exponentiallyfast at a fixed but arbitrarily chosen rate, provided the network's graph isstrongly connected for all time. The proposed estimators are inherentlyresilient to abrupt changes in the number of agents and communication links inthe inter-agent communication graph upon which the algorithms depend, providedthe network is redundantly strongly connected and redundantly jointlyobservable.

Split-Spectrum Based Distributed State Estimation for Linear Systems