19 research outputs found
Nonlinear analysis of dynamical complex networks
Copyright © 2013 Zidong Wang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.Complex networks are composed of a large number of highly interconnected dynamical units and therefore exhibit very complicated dynamics. Examples of such complex networks include the Internet, that is, a network of routers or domains, the World Wide Web (WWW), that is, a network of websites, the brain, that is, a network of neurons, and an organization, that is, a network of people. Since the introduction of the small-world network principle, a great deal of research has been focused on the dependence of the asymptotic behavior of interconnected oscillatory agents on the structural properties of complex networks. It has been found out that the general structure of the interaction network may play a crucial role in the emergence of synchronization phenomena in various fields such as physics, technology, and the life sciences
Multi-agent linear systems with noise. Solving decoupling problem
Dynamical multi-agent systems are being extensively studied by researchers in the field of control theory. It is due to the multi-agents appear in different study subjects as for example in the consensus problem of communication networks, formation control of mobile robots or cooperative control of unmanned aerial vehicles.
The disturbance decoupling problem for linear dynamical systems with noise was the starting point for the development of a geometric approach to systems theory. The problem consists in that the disturbance not interfere with the solution of the linear dynamical system; in other words, to find a compensator such that the closed loop transfer matrix from disturbance to output is 0.
Several multiagents linear systems are affected by noises, nevertheless almost all the existing results in consensus problem, do not take into account the effects of these noises. The goal of this paper is to advance in the study of the consensus problems under noise disturbances using linear algebra techniques.Postprint (author's final draft
Stability of a class of multi-agent tracking systems with unstable subsystems
In this work, we pre-deploy a large number of
smart agents to monitor an area of interest. This area could
be divided into many Voronoi cells by using the knowledge of
Voronoi diagram and every Voronoi site agent is responsible
for monitoring and tracking the target in its cell. Then, a
cooperative relay tracking strategy is proposed such that during
the tracking process, when a target enters a new Voronoi cell,
this event triggers the switching of both tracking agents and
communication topology. This is significantly different from the
traditional switching topologies. In addition, during the tracking
process, the topology and tracking agents switch, which may lead
the tracking system to be stable or unstable. The system switches
either among consecutive stable subsystems and consecutive
unstable subsystems or between stable and unstable subsystems.
The objective of this paper is to design a tracking strategy
guaranteeing overall successful tracking despite the existence of
unstable subsystems. We also address extended discussions on the
case where the dynamics of agents are subject to disturbances
and the disturbance attenuation level is achieved. Finally, the
proposed tracking strategy is verified by a set of simulations
Privacy-Preserving Stealthy Attack Detection in Multi-Agent Control Systems
This paper develops a glocal (global-local) attack detection framework to
detect stealthy cyber-physical attacks, namely covert attack and zero-dynamics
attack, against a class of multi-agent control systems seeking average
consensus. The detection structure consists of a global (central) observer and
local observers for the multi-agent system partitioned into clusters. The
proposed structure addresses the scalability of the approach and the privacy
preservation of the multi-agent system's state information. The former is
addressed by using decentralized local observers, and the latter is achieved by
imposing unobservability conditions at the global level. Also, the
communication graph model is subject to topology switching, triggered by local
observers, allowing for the detection of stealthy attacks by the global
observer. Theoretical conditions are derived for detectability of the stealthy
attacks using the proposed detection framework. Finally, a numerical simulation
is provided to validate the theoretical findings.Comment: to appear in IEEE CD
New synchronization criteria for an array of neural networks with hybrid coupling and time-varying delays
This paper is concerned with the global exponential synchronization for an array of hybrid coupled neural networks with time-varying leakage delay, discrete and distributed delays. Applying a novel Lyapunov functional and the property of outer coupling matrices of the neural networks, sufficient conditions are obtained for the global exponential synchronization of the system. The derived synchronization criteria are closely related with the time-varying delays and the coupling structure of the networks. The maximal allowable upper bounds of the time-varying delays can be obtained guaranteeing the global synchronization for the neural networks. The method we adopt in this paper is different from the commonly used linear matrix inequality (LMI) technique, and our synchronization conditions are new, which are easy to check in comparison with the previously reported LMI-based ones. Some examples are given to show the effectiveness of the obtained theoretical results
Invariance Principles and Observability in Switched Systems with an Application in Consensus
Using any nonnegative function with a nonpositive derivative along
trajectories to define a virtual output, the classic LaSalle invariance
principle can be extended to switched nonlinear time-varying (NLTV) systems, by
considering the weak observability (WO) associated with this output. WO is what
the output informs about the limiting behavior of state trajectories (hidden in
the zero locus of the output). In the context of switched NLTV systems, WO can
be explored using the recently established framework of limiting zeroing-output
solutions. Adding to this, an extension of the integral invariance principle
for switched NLTV systems with a new method to guarantee uniform global
attractivity of a closed set (without assuming uniform Lyapunov stability or
dwell-time conditions) is proposed. By way of illustrating the proposed method,
a leaderless consensus problem for nonholonomic mobile robots with a switching
communication topology is addressed, yielding a new control strategy and a new
convergence result
Multi-Agent Systems
A multi-agent system (MAS) is a system composed of multiple interacting intelligent agents. Multi-agent systems can be used to solve problems which are difficult or impossible for an individual agent or monolithic system to solve. Agent systems are open and extensible systems that allow for the deployment of autonomous and proactive software components. Multi-agent systems have been brought up and used in several application domains
Toward Digital Twin Oriented Modeling of Complex Networked Systems and Their Dynamics: A Comprehensive Survey
This paper aims to provide a comprehensive critical overview on how entities and their interactions in Complex Networked Systems (CNS) are modelled across disciplines as they approach their ultimate goal of creating a Digital Twin (DT) that perfectly matches the reality. We propose four complexity dimensions for the network representation and five generations of models for the dynamics modelling to describe the increasing complexity level of the CNS that will be developed towards achieving DT (e.g. CNS dynamics modelled offline in the 1st generation v.s. CNS dynamics modelled simultaneously with a two-way real time feedback between reality and the CNS in the 5th generation). Based on that, we propose a new framework to conceptually compare diverse existing modelling paradigms from different perspectives and create unified assessment criteria to evaluate their respective capabilities of reaching such an ultimate goal. Using the proposed criteria, we also appraise how far the reviewed current state-of-the-art approaches are from the idealised DTs. Finally, we identify and propose potential directions and ways of building a DT-orientated CNS based on the convergence and integration of CNS and DT utilising a variety of cross-disciplinary techniques