Self-Triggered Network Coordination Over Noisy Communication Channels

This paper deals with the coordination problems over noisy communication channels. We consider a scenario where the communication between network nodes is corrupted by unknown-but-bounded noise. We introduce a novel coordination scheme which ensures: 1) boundedness of the state trajectories and 2) a linear map from the noise to the nodes disagreement value. The proposed scheme does not require any global information on the network parameters and/or the operating environment (the noise characteristics). Moreover, network nodes can sample at independent rates and in an aperiodic manner

Self-Triggered Network Coordination over Noisy Communication Channels

This paper investigates coordination problems over packet-based communication channels. We consider the scenario in which the communication between network nodes is corrupted by unknown-but-bounded noise. We introduce a novel coordination scheme, which ensures practical consensus in the noiseless case, while preserving bounds on the nodes disagreement in the noisy case. The proposed scheme does not require any global information about the network parameters and/or the operating environment (the noise characteristics). Moreover, network nodes can sample at independent rates and in an aperiodic manner. The analysis is substantiated by extensive numerical simulations.Comment: 15 pages, 15 figure

Performance analysis with network-enhanced complexities: On fading measurements, event-triggered mechanisms, and cyber attacks

Copyright © 2014 Derui Ding 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.Nowadays, the real-world systems are usually subject to various complexities such as parameter uncertainties, time-delays, and nonlinear disturbances. For networked systems, especially large-scale systems such as multiagent systems and systems over sensor networks, the complexities are inevitably enhanced in terms of their degrees or intensities because of the usage of the communication networks. Therefore, it would be interesting to (1) examine how this kind of network-enhanced complexities affects the control or filtering performance; and (2) develop some suitable approaches for controller/filter design problems. In this paper, we aim to survey some recent advances on the performance analysis and synthesis with three sorts of fashionable network-enhanced complexities, namely, fading measurements, event-triggered mechanisms, and attack behaviors of adversaries. First, these three kinds of complexities are introduced in detail according to their engineering backgrounds, dynamical characteristic, and modelling techniques. Then, the developments of the performance analysis and synthesis issues for various networked systems are systematically reviewed. Furthermore, some challenges are illustrated by using a thorough literature review and some possible future research directions are highlighted.This work was supported in part by the National Natural Science Foundation of China under Grants 61134009, 61329301, 61203139, 61374127, and 61374010, the Royal Society of the UK, and the Alexander von Humboldt Foundation of Germany

Coordination networks under noisy measurements and sensor biases

Large scale network systems have been constructed and utilized to provide services ranging from energy acquisition and water distribution to health monitoring and transportation. The operation of these complex systems relies on sensors and actuators to acquire and control the system states, which are commonly exchanged among the sub-parts of the systems via communication channels, due to the spatial separation of the systems. Considering the pervasiveness of these man-made complex systems and the importance of the data extraction and exchange, attention should be paid in understanding how large scale systems behave when there are uncertainties in the measurements and communications.Aside from transmission delays and information missing, noise is also a major issue in data exchange. In addition, when sensors are used to measure variables, the problem that arises commonly is that the read-out may not be exactly equal to real value. In both cases, the data error prevents the systems to get accurate state information. As the current emergence of Internet of Thing, Industry 4.0, smart city and 5G, sensors and communication mediums are playing more and more important roles in network systems. Considering these facts, this thesis focuses on analysing and addressing the issues in networksystems caused by the error in state measurement and exchange.We first consider two algorithms to deal with the data exchange error, with a particular interest in designing robust network coordination algorithms againstunknown but bounded communication noise. In chapter 3, we propose a self-triggered consensus algorithm to tackle the state drift problem of consensus dynamics caused by the communication noise. In chapter 4, we refine the resultby proposing a different algorithm. Although these two algorithms both can achieve practical consensus and guarantee boundedness of system state, the mechanisms of them are different. The first algorithm relies on an adaptive threshold, which is adjusted based on the node state, to zero the control inputs of the nodes when their disagreements are sufficiently small. The second algorithm imposes the bound on the state of each node by saturating the state received from the node neighbours.Lastly, we consider the state measurement error, and focus on estimating the sensor bias from the incorrect measurement. The sensors in the network measure the relative states of their neighbours, and the measurements may contain biases. We discuss the conditions of the measurement graphs and the number of biased sensors that allow the biases to be reconstructed from the measurements. Furthermore, we provide distributed algorithms to compute the value of the biases

On the benefits of saturating information in consensus networks with noise

In a consensus network subject to non-zero mean noise, the system state may be driven away even when the disagreement exhibits a bounded response. This is unfavourable in applications since the nodes may not work properly and even be faulty outside their operating region. In this paper, we propose a new control algorithm to mitigate this issue by assigning each node a favourite interval that characterizes the nodes desired convergence region. The algorithm is implemented in a self-triggered fashion. If the nodes do not share a global clock, the network operates in a fully asynchronous mode. By this algorithm, we show that the state evolution is confined around the favourite interval and the node disagreement is bounded by a simple linear function of the noise magnitude, without requiring any priori information on the noise. We also show that if the nodes share some global information, then the algorithm can be adjusted to make the nodes evolve into the favourite interval, improve on the disagreement bound and achieve asymptotic consensus in the noiseless case

Resilience-oriented control and communication framework for cyber-physical microgrids

Climate change drives the energy supply transition from traditional fossil fuel-based power generation to renewable energy resources. This transition has been widely recognised as one of the most significant developing pathways promoting the decarbonisation process toward a zero-carbon and sustainable society. Rapidly developing renewables gradually dominate energy systems and promote the current energy supply system towards decentralisation and digitisation. The manifestation of decentralisation is at massive dispatchable energy resources, while the digitisation features strong cohesion and coherence between electrical power technologies and information and communication technologies (ICT). Massive dispatchable physical devices and cyber components are interdependent and coupled tightly as a cyber-physical energy supply system, while this cyber-physical energy supply system currently faces an increase of extreme weather (e.g., earthquake, flooding) and cyber-contingencies (e.g., cyberattacks) in the frequency, intensity, and duration. Hence, one major challenge is to find an appropriate cyber-physical solution to accommodate increasing renewables while enhancing power supply resilience. The main focus of this thesis is to blend centralised and decentralised frameworks to propose a collaboratively centralised-and-decentralised resilient control framework for energy systems i.e., networked microgrids (MGs) that can operate optimally in the normal condition while can mitigate simultaneous cyber-physical contingencies in the extreme condition. To achieve this, we investigate the concept of "cyber-physical resilience" including four phases, namely prevention/upgrade, resistance, adaption/mitigation, and recovery. Throughout these stages, we tackle different cyber-physical challenges under the concept of microgrid ranging from a centralised-to-decentralised transitional control framework coping with cyber-physical out of service, a cyber-resilient distributed control methodology for networked MGs, a UAV assisted post-contingency cyber-physical service restoration, to a fast-convergent distributed dynamic state estimation algorithm for a class of interconnected systems.Open Acces

Robotic ubiquitous cognitive ecology for smart homes

Robotic ecologies are networks of heterogeneous robotic devices pervasively embedded in everyday environments, where they cooperate to perform complex tasks. While their potential makes them increasingly popular, one fundamental problem is how to make them both autonomous and adaptive, so as to reduce the amount of preparation, pre-programming and human supervision that they require in real world applications. The project RUBICON develops learning solutions which yield cheaper, adaptive and efficient coordination of robotic ecologies. The approach we pursue builds upon a unique combination of methods from cognitive robotics, machine learning, planning and agent- based control, and wireless sensor networks. This paper illustrates the innovations advanced by RUBICON in each of these fronts before describing how the resulting techniques have been integrated and applied to a smart home scenario. The resulting system is able to provide useful services and pro-actively assist the users in their activities. RUBICON learns through an incremental and progressive approach driven by the feed- back received from its own activities and from the user, while also self-organizing the manner in which it uses available sensors, actuators and other functional components in the process. This paper summarises some of the lessons learned by adopting such an approach and outlines promising directions for future work

Distributed Estimation and Control of Algebraic Connectivity over Random Graphs

In this paper we propose a distributed algorithm for the estimation and control of the connectivity of ad-hoc networks in the presence of a random topology. First, given a generic random graph, we introduce a novel stochastic power iteration method that allows each node to estimate and track the algebraic connectivity of the underlying expected graph. Using results from stochastic approximation theory, we prove that the proposed method converges almost surely (a.s.) to the desired value of connectivity even in the presence of imperfect communication scenarios. The estimation strategy is then used as a basic tool to adapt the power transmitted by each node of a wireless network, in order to maximize the network connectivity in the presence of realistic Medium Access Control (MAC) protocols or simply to drive the connectivity toward a desired target value. Numerical results corroborate our theoretical findings, thus illustrating the main features of the algorithm and its robustness to fluctuations of the network graph due to the presence of random link failures.Comment: To appear in IEEE Transactions on Signal Processin