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In this paper, we construct cost consensus models with random opinions.
This paper investigates the guaranteed cost consensus problem for multi-agent systems with actuator faults/uncertainties.
The proposed models generalize both Ben-Arieh's minimum cost consensus model and Gong's consensus model with uncertain opinions.
Thus, the proposed model is a generalization of the existing cost consensus model and utility consensus model, respectively.
A sufficient condition for guaranteed cost consensus is given in terms of linear matrix inequalities (LMIs), based on a refined time-dependent Lyapunov functional analysis.
To investigate the energy consumption involved in a sampled-data consensus process, the problem of guaranteed cost consensus for sampled-data linear multi-agent systems is considered.
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Firstly, the leader follower guaranteed-cost consensus problem is introduced, which means that all the followers in a swarm system can track the state of the leader and some performance index should be satisfied simultaneously; that is, leader follower consensus is achieved in a suboptimal manner.
An alternative to circumvent the high communication cost of consensus algorithms is to use diffusion algorithms[16] which, contrary to the former, do not require multiple iterative inter-node communication between consecutive measurements.
By contrast, the Western adversarial system of competitive elections divides the body politic against itself at the cost of consensus and long-term continuity in governance.
With our approach, the need to remove a marker due to high cost of consensus is quite rare.
An important issue identified in the course of this work is that efficient, cost effective consensus methods for measuring protein concentration still do not exist [ 25].
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com