5 research outputs found
Consensus in multi-agent systems with non-periodic sampled-data exchange and uncertain network topology
In this paper consensus in second-order multi-agent systems with a
non-periodic sampled-data exchange among agents is investigated. The sampling
is random with bounded inter-sampling intervals. It is assumed that each agent
has exact knowledge of its own state at any time instant. The considered local
interaction rule is PD-type. Sufficient conditions for stability of the
consensus protocol to a time-invariant value are derived based on LMIs. Such
conditions only require the knowledge of the connectivity of the graph modeling
the network topology. Numerical simulations are presented to corroborate the
theoretical results.Comment: arXiv admin note: substantial text overlap with arXiv:1407.300
Consensus in multi-agent systems with second-order dynamics and non-periodic sampled-data exchange
In this paper consensus in second-order multi-agent systems with a
non-periodic sampled-data exchange among agents is investigated. The sampling
is random with bounded inter-sampling intervals. It is assumed that each agent
has exact knowledge of its own state at all times. The considered local
interaction rule is PD-type. The characterization of the convergence properties
exploits a Lyapunov-Krasovskii functional method, sufficient conditions for
stability of the consensus protocol to a time-invariant value are derived.
Numerical simulations are presented to corroborate the theoretical results.Comment: The 19th IEEE International Conference on Emerging Technologies and
Factory Automation (ETFA'2014), Barcelona (Spain
Consensus of second-order multi-agent systems with time delays and slow switching topology
In this paper we investigate the problem of deriving sufficient conditions for asymptotic consensus of second-order multi-agent systems with slow switching topology and time delays. The proposed local interaction protocol is PD-like and the stability analysis is based on the Lyapunov-Krasovski functional method. Our approach is based on the computation of a set of parameters that guarantee stability under any network topology of a given set. A significant feature of this method is that it does not require to know the possible network topologies but only a bound on their second largest eigenvalue (algebraic connectivity). Note that this is only a preliminary work in this framework and the computation of the minimum dwell time that ensures asymptotic consensus under arbitrary switching is still an open issue and will be the object of our future research in this topic
Clustering analysis using Swarm Intelligence
This thesis is concerned with the application of the swarm intelligence methods in
clustering analysis of datasets. The main objectives of the thesis are
∙ Take the advantage of a novel evolutionary algorithm, called artificial bee colony,
to improve the capability of K-means in finding global optimum clusters in
nonlinear partitional clustering problems.
∙ Consider partitional clustering as an optimization problem and an improved antbased
algorithm, named Opposition-Based API (after the name of Pachycondyla
APIcalis ants), to automatic grouping of large unlabeled datasets.
∙ Define partitional clustering as a multiobjective optimization problem. The
aim is to obtain well-separated, connected, and compact clusters and for this
purpose, two objective functions have been defined based on the concepts of
data connectivity and cohesion. These functions are the core of an efficient
multiobjective particle swarm optimization algorithm, which has been devised
for and applied to automatic grouping of large unlabeled datasets.
For that purpose, this thesis is divided is five main parts:
∙ The first part, including Chapter 1, aims at introducing state of the art of swarm
intelligence based clustering methods.
∙ The second part, including Chapter 2, consists in clustering analysis with combination
of artificial bee colony algorithm and K-means technique.
∙ The third part, including Chapter 3, consists in a presentation of clustering
analysis using opposition-based API algorithm.
∙ The fourth part, including Chapter 4, consists in multiobjective clustering analysis
using particle swarm optimization.
∙ Finally, the fifth part, including Chapter 5, concludes the thesis and addresses
the future directions and the open issues of this research
Clustering analysis using Swarm Intelligence
This thesis is concerned with the application of the swarm intelligence methods in
clustering analysis of datasets. The main objectives of the thesis are
∙ Take the advantage of a novel evolutionary algorithm, called artificial bee colony,
to improve the capability of K-means in finding global optimum clusters in
nonlinear partitional clustering problems.
∙ Consider partitional clustering as an optimization problem and an improved antbased
algorithm, named Opposition-Based API (after the name of Pachycondyla
APIcalis ants), to automatic grouping of large unlabeled datasets.
∙ Define partitional clustering as a multiobjective optimization problem. The
aim is to obtain well-separated, connected, and compact clusters and for this
purpose, two objective functions have been defined based on the concepts of
data connectivity and cohesion. These functions are the core of an efficient
multiobjective particle swarm optimization algorithm, which has been devised
for and applied to automatic grouping of large unlabeled datasets.
For that purpose, this thesis is divided is five main parts:
∙ The first part, including Chapter 1, aims at introducing state of the art of swarm
intelligence based clustering methods.
∙ The second part, including Chapter 2, consists in clustering analysis with combination
of artificial bee colony algorithm and K-means technique.
∙ The third part, including Chapter 3, consists in a presentation of clustering
analysis using opposition-based API algorithm.
∙ The fourth part, including Chapter 4, consists in multiobjective clustering analysis
using particle swarm optimization.
∙ Finally, the fifth part, including Chapter 5, concludes the thesis and addresses
the future directions and the open issues of this research