Adaptive Synchronization of Nonlinearly Parameterized Complex Dynamical Networks with Unknown Time-Varying Parameters

A new adaptive learning control approach is proposed for a class of nonlinearly parameterized complex dynamical networks with unknown time-varying parameters. By using the parameter separation and reparameterization technique, the adaptive learning laws of periodically time-varying and constant parameters and an adaptive control strategy are designed to ensure the asymptotic convergence of the synchronization error in the sense of square error norm. Then, a sufficient condition of the synchronization is given by constructing a composite energy function. Finally, an example of the complex network is used to verify the effectiveness of proposed approach

New Stabilization of Complex Networks with Non-delayed and Delayed Couplings over Random Exchanges

In this chapter, the stabilization problem of complex dynamical network with non-delayed and delayed couplings is realized by a new kind of stochastic pinning controller being partially delay dependent, where the topologies related to couplings may be exchanged. The designed pinning controller is different from the traditional ones, whose non-delay and delay state terms occur asynchronously with a certain probability, respectively. Sufficient conditions for the stabilization of complex dynamical network over topology exchange are derived by the robust method and are presented with liner matrix inequities (LMIs). The switching between the non-delayed and delayed couplings is modeled by the related coupling matrices containing uncertainties. It has shown that the bound of such uncertainties play very important roles in the controller design. Moreover, when the bound is inaccessible, a kind of adaptive partially delay-dependent controller is proposed to deal with this general case, where another adaptive control problem in terms of unknown probability is considered too. Finally, some numerical simulations are used to demonstrate the correctness and effectiveness of our theoretical analysis

Structural engineering of evolving complex dynamical networks

Networks are ubiquitous in nature and many natural and man-made systems can be modelled as networked systems. Complex networks, systems comprising a number of nodes that are connected through edges, have been frequently used to model large-scale systems from various disciplines such as biology, ecology, and engineering. Dynamical systems interacting through a network may exhibit collective behaviours such as synchronisation, consensus, opinion formation, flocking and unusual phase transitions. Evolution of such collective behaviours is highly dependent on the structure of the interaction network. Optimisation of network topology to improve collective behaviours and network robustness can be achieved by intelligently modifying the network structure. Here, it is referred to as "Engineering of the Network". Although coupled dynamical systems can develop spontaneous synchronous patterns if their coupling strength lies in an appropriate range, in some applications one needs to control a fraction of nodes, known as driver nodes, in order to facilitate the synchrony. This thesis addresses the problem of identifying the set of best drivers, leading to the best pinning control performance. The eigen-ratio of the augmented Laplacian matrix, that is the largest eigenvalue divided by the second smallest one, is chosen as the controllability metric. The approach introduced in this thesis is to obtain the set of optimal drivers based on sensitivity analysis of the eigen-ratio, which requires only a single computation of the eigenvector associated with the largest eigenvalue, and thus is applicable for large-scale networks. This leads to a new "controllability centrality" metric for each subset of nodes. Simulation results reveal the effectiveness of the proposed metric in predicting the most important driver(s) correctly.     Interactions in complex networks might also facilitate the propagation of undesired effects, such as node/edge failure, which may crucially affect the performance of collective behaviours. In order to study the effect of node failure on network synchronisation, an analytical metric is proposed that measures the effect of a node removal on any desired eigenvalue of the Laplacian matrix. Using this metric, which is based on the local multiplicity of each eigenvalue at each node, one can approximate the impact of any node removal on the spectrum of a graph. The metric is computationally efficient as it only needs a single eigen-decomposition of the Laplacian matrix. It also provides a reliable approximation for the "Laplacian energy" of a network. Simulation results verify the accuracy of this metric in networks with different topologies. This thesis also considers formation control as an application of network synchronisation and studies the "rigidity maintenance" problem, which is one of the major challenges in this field. This problem is to preserve the rigidity of the sensing graph in a formation during motion, taking into consideration constraints such as line-of-sight requirements, sensing ranges and power limitations. By introducing a "Lattice of Configurations" for each node, a distributed rigidity maintenance algorithm is proposed to preserve the rigidity of the sensing network when failure in a sensing link would result in loss of rigidity. The proposed algorithm recovers rigidity by activating, almost always, the minimum number of new sensing links and considers real-time constraints of practical formations. A sufficient condition for this problem is proved and tested via numerical simulations. Based on the above results, a number of other areas and applications of network dynamics are studied and expounded upon in this thesis

An Overview of Recent Progress in the Study of Distributed Multi-agent Coordination

This article reviews some main results and progress in distributed multi-agent coordination, focusing on papers published in major control systems and robotics journals since 2006. Distributed coordination of multiple vehicles, including unmanned aerial vehicles, unmanned ground vehicles and unmanned underwater vehicles, has been a very active research subject studied extensively by the systems and control community. The recent results in this area are categorized into several directions, such as consensus, formation control, optimization, task assignment, and estimation. After the review, a short discussion section is included to summarize the existing research and to propose several promising research directions along with some open problems that are deemed important for further investigations

RESH: A Secure Authentication Algorithm Based on Regeneration Encoding Self-Healing Technology in WSN

In the real application environment of wireless sensor networks (WSNs), the uncertain factor of data storage makes the authentication information be easily forged and destroyed by illegal attackers. As a result, it is hard for secure managers to conduct forensics on transmitted information in WSN. This work considers the regeneration encoding self-healing and secret sharing techniques and proposes an effective scheme to authenticate data in WSN. The data is encoded by regeneration codes and then distributed to other redundant nodes in the form of fragments. When the network is attacked, the scheme has the ability against tampering attack or collusion attack. Furthermore, the damaged fragments can be restored as well. Parts of fragments, encoded by regeneration code, are required for secure authentication of the original distributed data. Experimental results show that the proposed scheme reduces hardware communication overhead by five percent in comparison. Additionally, the performance of local recovery achieves ninety percent

A Multi-Agent Systems Approach for Analysis of Stepping Stone Attacks

Stepping stone attacks are one of the most sophisticated cyber-attacks, in which attackers make a chain of compromised hosts to reach a victim target. In this Dissertation, an analytic model with Multi-Agent systems approach has been proposed to analyze the propagation of stepping stones attacks in dynamic vulnerability graphs. Because the vulnerability configuration in a network is inherently dynamic, in this Dissertation a biased min-consensus technique for dynamic graphs with fixed and switching topology is proposed as a distributed technique to calculate the most vulnerable path for stepping stones attacks in dynamic vulnerability graphs. We use min-plus algebra to analyze and provide necessary and sufficient convergence conditions to the shortest path in the fixed topology case. A necessary condition for the switching topology case is provided. Most cyber-attacks involve an attacker launching a multi-stage attack by exploiting a sequence of hosts. This multi-stage attack generates a chain of ``stepping stones” from the origin to target. The choice of stepping stones is a function of the degree of exploitability, the impact, attacker’s capability, masking origin location, and intent. In this Dissertation, we model and analyze scenarios wherein an attacker employs multiple strategies to choose stepping stones. The problem is modeled as an Adjacency Quadratic Shortest Path using dynamic vulnerability graphs with multi-agent dynamic system approach. With this approach, the shortest stepping stone path with maximum node degree and the shortest stepping stone path with maximum impact are modeled and analyzed. Because embedded controllers are omnipresent in networks, in this Dissertation as a Risk Mitigation Strategy, a cyber-attack tolerant control strategy for embedded controllers is proposed. A dual redundant control architecture that combines two identical controllers that are switched periodically between active and restart modes is proposed. The strategy is addressed to mitigate the impact due to the corruption of the controller software by an adversary. We analyze the impact of the resetting and restarting the controller software and performance of the switching process. The minimum requirements in the control design, for effective mitigation of cyber-attacks to the control software that implies a “fast” switching period is provided. The simulation results demonstrate the effectiveness of the proposed strategy when the time to fully reset and restart the controller is faster than the time taken by an adversary to compromise the controller. The results also provide insights into the stability and safety regions and the factors that determine the effectiveness of the proposed strategy

Complex Quantum Networks: a Topical Review

These are exciting times for quantum physics as new quantum technologies are expected to soon transform computing at an unprecedented level. Simultaneously network science is flourishing proving an ideal mathematical and computational framework to capture the complexity of large interacting systems. Here we provide a comprehensive and timely review of the rising field of complex quantum networks. On one side, this subject is key to harness the potential of complex networks in order to provide design principles to boost and enhance quantum algorithms and quantum technologies. On the other side this subject can provide a new generation of quantum algorithms to infer significant complex network properties. The field features fundamental research questions as diverse as designing networks to shape Hamiltonians and their corresponding phase diagram, taming the complexity of many-body quantum systems with network theory, revealing how quantum physics and quantum algorithms can predict novel network properties and phase transitions, and studying the interplay between architecture, topology and performance in quantum communication networks. Our review covers all of these multifaceted aspects in a self-contained presentation aimed both at network-curious quantum physicists and at quantum-curious network theorists. We provide a framework that unifies the field of quantum complex networks along four main research lines: network-generalized, quantum-applied, quantum-generalized and quantum-enhanced. Finally we draw attention to the connections between these research lines, which can lead to new opportunities and new discoveries at the interface between quantum physics and network science.Comment: 103 pages + 29 pages of references, 26 figure