3,117 research outputs found
Asymptotic Consensus Without Self-Confidence
This paper studies asymptotic consensus in systems in which agents do not
necessarily have self-confidence, i.e., may disregard their own value during
execution of the update rule. We show that the prevalent hypothesis of
self-confidence in many convergence results can be replaced by the existence of
aperiodic cores. These are stable aperiodic subgraphs, which allow to virtually
store information about an agent's value distributedly in the network. Our
results are applicable to systems with message delays and memory loss.Comment: 13 page
Pose consensus based on dual quaternion algebra with application to decentralized formation control of mobile manipulators
This paper presents a solution based on dual quaternion algebra to the
general problem of pose (i.e., position and orientation) consensus for systems
composed of multiple rigid-bodies. The dual quaternion algebra is used to model
the agents' poses and also in the distributed control laws, making the proposed
technique easily applicable to time-varying formation control of general
robotic systems. The proposed pose consensus protocol has guaranteed
convergence when the interaction among the agents is represented by directed
graphs with directed spanning trees, which is a more general result when
compared to the literature on formation control. In order to illustrate the
proposed pose consensus protocol and its extension to the problem of formation
control, we present a numerical simulation with a large number of free-flying
agents and also an application of cooperative manipulation by using real mobile
manipulators
Opinion Dynamics in Heterogeneous Networks: Convergence Conjectures and Theorems
Recently, significant attention has been dedicated to the models of opinion
dynamics in which opinions are described by real numbers, and agents update
their opinions synchronously by averaging their neighbors' opinions. The
neighbors of each agent can be defined as either (1) those agents whose
opinions are in its "confidence range," or (2) those agents whose "influence
range" contain the agent's opinion. The former definition is employed in
Hegselmann and Krause's bounded confidence model, and the latter is novel here.
As the confidence and influence ranges are distinct for each agent, the
heterogeneous state-dependent interconnection topology leads to a
poorly-understood complex dynamic behavior. In both models, we classify the
agents via their interconnection topology and, accordingly, compute the
equilibria of the system. Then, we define a positive invariant set centered at
each equilibrium opinion vector. We show that if a trajectory enters one such
set, then it converges to a steady state with constant interconnection
topology. This result gives us a novel sufficient condition for both models to
establish convergence, and is consistent with our conjecture that all
trajectories of the bounded confidence and influence models eventually converge
to a steady state under fixed topology.Comment: 22 pages, Submitted to SIAM Journal on Control and Optimization
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