139 research outputs found
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
Optimal State Estimation with Measurements Corrupted by Laplace Noise
Optimal state estimation for linear discrete-time systems is considered.
Motivated by the literature on differential privacy, the measurements are
assumed to be corrupted by Laplace noise. The optimal least mean square error
estimate of the state is approximated using a randomized method. The method
relies on that the Laplace noise can be rewritten as Gaussian noise scaled by
Rayleigh random variable. The probability of the event that the distance
between the approximation and the best estimate is smaller than a constant is
determined as function of the number of parallel Kalman filters that is used in
the randomized method. This estimator is then compared with the optimal linear
estimator, the maximum a posteriori (MAP) estimate of the state, and the
particle filter
Stochastic Systems: Modeling, Optimization, and Applications
The special issue of Mathematical Problems in Engineering deals with the issues of modeling, optimization, and applications associated with stochastic systems. This special issue provides a forum for researchers and practitioners to publish quality research work on modeling, optimization approaches, and their applications in the context of theory analysis and engineering developments. The accepted papers in this special issue include stochastic stability, stabilization and control optimization, stochastic optimization, particle swarm optimization, modeling and identification methods, signal processing, and robust filtering. The issue includes thirty-nine papers out of which six consider the stability and stabilization problems of stochastic systems. Twelve papers cover the problems of the controller design and relevant optimization algorithms
Differentially Private State Estimation in Distribution Networks with Smart Meters
State estimation is routinely being performed in high-voltage power
transmission grids in order to assist in operation and to detect faulty
equipment. In low- and medium-voltage power distribution grids, on the other
hand, few real-time measurements are traditionally available, and operation is
often conducted based on predicted and historical data. Today, in many parts of
the world, smart meters have been deployed at many customers, and their
measurements could in principle be shared with the operators in real time to
enable improved state estimation. However, customers may feel reluctance in
doing so due to privacy concerns. We therefore propose state estimation schemes
for a distribution grid model, which ensure differential privacy to the
customers. In particular, the state estimation schemes optimize different
performance criteria, and a trade-off between a lower bound on the estimation
performance versus the customers' differential privacy is derived. The proposed
framework is general enough to be applicable also to other distribution
networks, such as water and gas networks
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
Resilient Learning-Based Control for Synchronization of Passive Multi-Agent Systems under Attack
In this paper, we show synchronization for a group of output passive agents
that communicate with each other according to an underlying communication graph
to achieve a common goal. We propose a distributed event-triggered control
framework that will guarantee synchronization and considerably decrease the
required communication load on the band-limited network. We define a general
Byzantine attack on the event-triggered multi-agent network system and
characterize its negative effects on synchronization. The Byzantine agents are
capable of intelligently falsifying their data and manipulating the underlying
communication graph by altering their respective control feedback weights. We
introduce a decentralized detection framework and analyze its steady-state and
transient performances. We propose a way of identifying individual Byzantine
neighbors and a learning-based method of estimating the attack parameters.
Lastly, we propose learning-based control approaches to mitigate the negative
effects of the adversarial attack
On Development of 100-Gram-Class Spacecraft for Swarm Applications
A novel space system architecture is proposed, which would enable 100-g-class spacecraft to be flown as swarms (100 s-1000 s) in low Earth orbit. Swarms of Silicon Wafer Integrated Femtosatellites (SWIFT) present a paradigm-shifting approach to distributed spacecraft development, missions, and applications. Potential applications of SWIFT swarms include sparse aperture arrays and distributed sensor networks. New swarm array configurations are introduced and shown to achieve the effective sparse aperture driven from optical performance metrics. A system cost analysis based on this comparison justifies deploying a large number of femtosatellites for sparse aperture applications. Moreover, this paper discusses promising guidance, control, and navigation methods for swarms of femtosatellites equipped with modest sensing and control capabilities
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Maximum Correntropy Filtering for Complex Networks With Uncertain Dynamical Bias: Enabling Componentwise Event-Triggered Transmission
10.13039/501100001809-National Natural Science Foundation of China (Grant Number: 62203016, U2241214, T2121002 and 61933007);;
10.13039/501100002858-China Postdoctoral Science Foundation (Grant Number: 2021TQ0009);
Royal Society, U (Grant Number: 0000DONOTUSETHIS0000.K);
Alexander von Humboldt Foundation of Germany
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