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Reliable online quality prediction of these chemical processes with distributed outputs is important but challenging.
This paper presents an architecture for agent-based distributed state estimation of linear plants with distributed outputs.
Moreover, an ensemble JKL (EJKL) modeling approach is developed to obtain more reliable prediction performance of the distributed outputs.
In contrast, the investigation of parameter evolution and distributed outputs' variability appeared to be a valuable tool to uncover the interdependencies of data and model uncertainties.
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We consider the control of a linear, time-invariant plant via a distributed output feedback.
A simple example with an in-domain pointwise and distributed output illustrates the theoretical results.
In this paper, distributed output feedback control of Markov jump multi-agent systems (MASs) is investigated.
Second distributed output feedback (i.e. without using full state feedback) controllers are developed for cooperative synchronization.
The distributed output data was successfully acquired at high-speed by using an on-chip testing method.
By Markov jump optimal filtering theory and the mean field approach, distributed output feedback control laws are presented.
With some fundamental assumptions, two necessary and sufficient conditions are given for the distributed output regulation problem to be solvable.
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