15,011 research outputs found
Consensus Control for Leader-follower Multi-agent Systems under Prescribed Performance Guarantees
This paper addresses the problem of distributed control for leader-follower
multi-agent systems under prescribed performance guarantees. Leader-follower is
meant in the sense that a group of agents with external inputs are selected as
leaders in order to drive the group of followers in a way that the entire
system can achieve consensus within certain prescribed performance transient
bounds. Under the assumption of tree graphs, a distributed control law is
proposed when the decay rate of the performance functions is within a
sufficient bound. Then, two classes of tree graphs that can have additional
followers are investigated. Finally, several simulation examples are given to
illustrate the results.Comment: 8 page
Robust Distance-Based Formation Control of Multiple Rigid Bodies with Orientation Alignment
This paper addresses the problem of distance- and orientation-based formation
control of a class of second-order nonlinear multi-agent systems in 3D space,
under static and undirected communication topologies. More specifically, we
design a decentralized model-free control protocol in the sense that each agent
uses only local information from its neighbors to calculate its own control
signal, without incorporating any knowledge of the model nonlinearities and
exogenous disturbances. Moreover, the transient and steady state response is
solely determined by certain designer-specified performance functions and is
fully decoupled by the agents' dynamic model, the control gain selection, the
underlying graph topology as well as the initial conditions. Additionally, by
introducing certain inter-agent distance constraints, we guarantee collision
avoidance and connectivity maintenance between neighboring agents. Finally,
simulation results verify the performance of the proposed controllers.Comment: IFAC Word Congress 201
Diffusion-Based Adaptive Distributed Detection: Steady-State Performance in the Slow Adaptation Regime
This work examines the close interplay between cooperation and adaptation for
distributed detection schemes over fully decentralized networks. The combined
attributes of cooperation and adaptation are necessary to enable networks of
detectors to continually learn from streaming data and to continually track
drifts in the state of nature when deciding in favor of one hypothesis or
another. The results in the paper establish a fundamental scaling law for the
steady-state probabilities of miss-detection and false-alarm in the slow
adaptation regime, when the agents interact with each other according to
distributed strategies that employ small constant step-sizes. The latter are
critical to enable continuous adaptation and learning. The work establishes
three key results. First, it is shown that the output of the collaborative
process at each agent has a steady-state distribution. Second, it is shown that
this distribution is asymptotically Gaussian in the slow adaptation regime of
small step-sizes. And third, by carrying out a detailed large deviations
analysis, closed-form expressions are derived for the decaying rates of the
false-alarm and miss-detection probabilities. Interesting insights are gained.
In particular, it is verified that as the step-size decreases, the error
probabilities are driven to zero exponentially fast as functions of ,
and that the error exponents increase linearly in the number of agents. It is
also verified that the scaling laws governing errors of detection and errors of
estimation over networks behave very differently, with the former having an
exponential decay proportional to , while the latter scales linearly
with decay proportional to . It is shown that the cooperative strategy
allows each agent to reach the same detection performance, in terms of
detection error exponents, of a centralized stochastic-gradient solution.Comment: The paper will appear in IEEE Trans. Inf. Theor
Opinion modeling on social media and marketing aspects
We introduce and discuss kinetic models of opinion formation on social
networks in which the distribution function depends on both the opinion and the
connectivity of the agents. The opinion formation model is subsequently coupled
with a kinetic model describing the spreading of popularity of a product on the
web through a social network. Numerical experiments on the underlying kinetic
models show a good qualitative agreement with some measured trends of hashtags
on social media websites and illustrate how companies can take advantage of the
network structure to obtain at best the advertisement of their products
Hydrodynamic models of preference formation in multi-agent societies
In this paper, we discuss the passage to hydrodynamic equations for kinetic
models of opinion formation. The considered kinetic models feature an opinion
density depending on an additional microscopic variable, identified with the
personal preference. This variable describes an opinion-driven polarisation
process, leading finally to a choice among some possible options, as it happens
e.g. in referendums or elections. Like in the kinetic theory of rarefied gases,
the derivation of hydrodynamic equations is essentially based on the
computation of the local equilibrium distribution of the opinions from the
underlying kinetic model. Several numerical examples validate the resulting
model, shedding light on the crucial role played by the distinction between
opinion and preference formation on the choice processes in multi-agent
societies.Comment: 30 pages, 15 figure
Co-Regulated Consensus of Cyber-Physical Resources in Multi-Agent Unmanned Aircraft Systems
Intelligent utilization of resources and improved mission performance in an autonomous agent require consideration of cyber and physical resources. The allocation of these resources becomes more complex when the system expands from one agent to multiple agents, and the control shifts from centralized to decentralized. Consensus is a distributed algorithm that lets multiple agents agree on a shared value, but typically does not leverage mobility. We propose a coupled consensus control strategy that co-regulates computation, communication frequency, and connectivity of the agents to achieve faster convergence times at lower communication rates and computational costs. In this strategy, agents move towards a common location to increase connectivity. Simultaneously, the communication frequency is increased when the shared state error between an agent and its connected neighbors is high. When the shared state converges (i.e., consensus is reached), the agents withdraw to the initial positions and the communication frequency is decreased. Convergence properties of our algorithm are demonstrated under the proposed co-regulated control algorithm. We evaluated the proposed approach through a new set of cyber-physical, multi-agent metrics and demonstrated our approach in a simulation of unmanned aircraft systems measuring temperatures at multiple sites. The results demonstrate that, compared with fixed-rate and event-triggered consensus algorithms, our co-regulation scheme can achieve improved performance with fewer resources, while maintaining high reactivity to changes in the environment and system
Scientific Polarization
Contemporary societies are often "polarized", in the sense that sub-groups
within these societies hold stably opposing beliefs, even when there is a fact
of the matter. Extant models of polarization do not capture the idea that some
beliefs are true and others false. Here we present a model, based on the
network epistemology framework of Bala and Goyal ["Learning from neighbors",
\textit{Rev. Econ. Stud.} \textbf{65}(3), 784-811 (1998)], in which
polarization emerges even though agents gather evidence about their beliefs,
and true belief yields a pay-off advantage. The key mechanism that generates
polarization involves treating evidence generated by other agents as uncertain
when their beliefs are relatively different from one's own.Comment: 22 pages, 5 figures, author final versio
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