scholarly journals Performance Analysis with Network-Enhanced Complexities: On Fading Measurements, Event-Triggered Mechanisms, and Cyber Attacks

2014 ◽  
Vol 2014 ◽  
pp. 1-10
Author(s):  
Derui Ding ◽  
Zidong Wang ◽  
Hongli Dong ◽  
Yurong Liu ◽  
Bashir Ahmad
Keyword(s):  
Performance Analysis ◽  
Large Scale ◽  
Filter Design ◽  
Cyber Attacks ◽  
Networked Systems ◽  
Future Research ◽  
Dynamical Characteristic ◽  
Parameter Uncertainties ◽  
Analysis And Synthesis ◽  
Event Triggered

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.

scholarly journals A Study on Distributed Optimization over Large-Scale Networked Systems

2021 ◽  
Vol 2021 ◽  
pp. 1-19
Author(s):  
Hansi K. Abeynanda ◽  
G. H. J. Lanel
Keyword(s):  
Gradient Method ◽  
Large Scale ◽  
Distributed Optimization ◽  
Alternating Direction Method ◽  
Optimization Techniques ◽  
Networked Systems ◽  
Future Research ◽  
Subgradient Methods ◽  
Method Of Multipliers ◽  
Alternating Direction

Distributed optimization is a very important concept with applications in control theory and many related fields, as it is high fault-tolerant and extremely scalable compared with centralized optimization. Centralized solution methods are not suitable for many application domains that consist of large number of networked systems. In general, these large-scale networked systems cooperatively find an optimal solution to a common global objective during the optimization process. Thus, it gives us an opportunity to analyze distributed optimization techniques that is demanded in most distributed optimization settings. This paper presents an analysis that provides an overview of decomposition methods as well as currently existing distributed methods and techniques that are employed in large-scale networked systems. A detailed analysis on gradient like methods, subgradient methods, and methods of multipliers including the alternating direction method of multipliers is presented. These methods are analyzed empirically by using numerical examples. Moreover, an example highlighting the fact that the gradient method fails to solve distributed problems in some circumstances is discussed under numerical results. A numerical implementation is used to demonstrate that the alternating direction method of multipliers can solve this particular problem, by revealing its robustness compared with the gradient method. Finally, we conclude the paper with possible future research directions.

scholarly journals Robust Event-Triggered Energy-to-Peak Filtering for Polytopic Uncertain Systems over Lossy Network with Quantized Measurements

2016 ◽  
Vol 2016 ◽  
pp. 1-15 ◽  
Author(s):  
Jidong Wang ◽  
Kezhen Han
Keyword(s):  
Performance Analysis ◽  
Linearization Method ◽  
Packet Dropout ◽  
Event Generator ◽  
Optimization Approach ◽  
Packet Losses ◽  
Worst Case ◽  
Filter Performance ◽  
Analysis And Synthesis ◽  
Event Triggered

The event-triggered energy-to-peak filtering for polytopic discrete-time linear systems is studied with the consideration of lossy network and quantization error. Because of the communication imperfections from the packet dropout of lossy link, the event-triggered condition used to determine the data release instant at the event generator (EG) can not be directly applied to update the filter input at the zero order holder (ZOH) when performing filter performance analysis and synthesis. In order to balance such nonuniform time series between the triggered instant of EG and the updated instant of ZOH, two event-triggered conditions are defined, respectively, whereafter a worst-case bound on the number of consecutive packet losses of the transmitted data from EG is given, which marginally guarantees the effectiveness of the filter that will be designed based on the event-triggered updating condition of ZOH. Then, the filter performance analysis conditions are obtained under the assumption that the maximum number of packet losses is allowable for the worst-case bound. In what follows, a two-stage LMI-based alternative optimization approach is proposed to separately design the filter, which reduces the conservatism of the traditional linearization method of filter analysis conditions. Subsequently a codesign algorithm is developed to determine the communication and filter parameters simultaneously. Finally, an illustrative example is provided to verify the validity of the obtained results.

scholarly journals A Review on Analysis and Synthesis of Nonlinear Stochastic Systems with Randomly Occurring Incomplete Information

2012 ◽  
Vol 2012 ◽  
pp. 1-15 ◽  
Author(s):  
Hongli Dong ◽  
Zidong Wang ◽  
Xuemin Chen ◽  
Huijun Gao
Keyword(s):  
Incomplete Information ◽  
Stochastic Systems ◽  
Future Research ◽  
Missing Measurements ◽  
Parameter Uncertainties ◽  
Nonlinear Stochastic Systems ◽  
Randomly Occurring Uncertainties ◽  
Analysis And Synthesis ◽  
System States ◽  
And Control

In the context of systems and control, incomplete information refers to a dynamical system in which knowledge about the system states is limited due to the difficulties in modeling complexity in a quantitative way. The well-known types of incomplete information include parameter uncertainties and norm-bounded nonlinearities. Recently, in response to the development of network technologies, the phenomenon of randomly occurring incomplete information has become more and more prevalent. Such a phenomenon typically appears in a networked environment. Examples include, but are not limited to, randomly occurring uncertainties, randomly occurring nonlinearities, randomly occurring saturation, randomly missing measurements and randomly occurring quantization. Randomly occurring incomplete information, if not properly handled, would seriously deteriorate the performance of a control system. In this paper, we aim to survey some recent advances on the analysis and synthesis problems for nonlinear stochastic systems with randomly occurring incomplete information. The developments of the filtering, control and fault detection problems are systematically reviewed. Latest results on analysis and synthesis of nonlinear stochastic systems are discussed in great detail. In addition, various distributed filtering technologies over sensor networks are highlighted. Finally, some concluding remarks are given and some possible future research directions are pointed out.

Adaptive Event-Triggered Fuzzy H Filter Design for Nonlinear Networked Systems

2019 ◽  
pp. 1-1
Author(s):  
Xin Zhao ◽  
Chong Lin ◽  
Bing Chen ◽  
Qing-Guo Wang ◽  
Zhongjing Ma
Keyword(s):  
Filter Design ◽  
Networked Systems ◽  
Event Triggered

scholarly journals Event-Triggered H ∞ Filtering for Markovian Jump Neural Networks under Random Missing Measurements and Deception Attacks

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-19
Author(s):  
Jinxia Wang ◽  
Jinfeng Gao ◽  
Tian Tan ◽  
Jiaqi Wang ◽  
Miao Ma
Keyword(s):  
Neural Networks ◽  
Communication Network ◽  
Communication Networks ◽  
Filter Design ◽  
Sufficient Conditions ◽  
Cyber Attacks ◽  
Markovian Jump ◽  
Missing Measurements ◽  
Event Triggered ◽  
Markovian Jump Neural Networks

This paper concentrates on the event-triggered H ∞ filter design for the discrete-time Markovian jump neural networks under random missing measurements and cyber attacks. Considering that the controlled system and the filtering can exchange information over a shared communication network which is vulnerable to the cyber attacks and has limited bandwidth, the event-triggered mechanism is proposed to relieve the communication burden of data transmission. A variable conforming to Bernoulli distribution is exploited to describe the stochastic phenomenon since the missing measurements occur with random probability. Furthermore, seeing that the communication networks are vulnerable to external malicious attacks, the transferred information via the shared communication network may be changed by the injected false information from the attackers. Based on the above consideration, sufficient conditions for the filtering error system to maintain asymptotically stable are provided with predefined H ∞ performance. In the end, three numerical examples are given to verify the proposed theoretical results.

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