Network-induced Phenomena Research Articles

State Estimation for Measurement-Saturated Memristive Neural Networks with Missing Measurements and Mixed Time Delays Subject to Cyber-Attacks: A Non-Fragile Set-Membership Filtering Framework

This paper is concerned with the state estimation problem based on non-fragile set-membership filtering for a class of measurement-saturated memristive neural networks (MNNs) with unknown but bounded (UBB) noises, mixed time delays and missing measurements (MMs), subject to cyber-attacks under the framework of weighted try-once-discard protocol (WTOD protocol). Considering bandwidth-limited open networks, this paper proposes an improved set-membership filtering based on WTOD protocol to partially solve the problem that multiple sensor-related problems and multiple network-induced phenomena influence the state estimation performance of MNNs. Moreover, this paper also discusses the gain perturbations of the estimator and proposes an improved non-fragile estimation framework based on set-membership filtering, which enhances the robustness of the estimation approach. The proposed estimation framework can effectively estimate the state of MNNs with UBB noises, estimator gain perturbations, mixed time-delays, cyber-attacks, measurement saturations and MMs. This paper first utilizes mathematical induction to provide the sufficient conditions for the existence of the desired estimator, and obtains the estimator gain by solving a set of linear matrix inequalities. Then, a recursive optimization algorithm is utilized to achieve optimal estimation performance. The effectiveness of the theoretical results is verified by comparative numerical simulation examples.

ERNN: Error-Resilient RNN for Encrypted Traffic Detection towards Network-Induced Phenomena

Traffic detection systems based on machine learning have been proposed to defend against cybersecurity threats, such as intrusion attacks and malware. However, they did not take the impact of network-induced phenomena into consideration, such as packet loss, retransmission, and out-of-order. These phenomena will introduce additional misclassifications in the real world. In this paper, we present <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math>${\sf ERNN}$</tex-math></inline-formula> , a robust and end-to-end RNN model that is specially designed against network-induced phenomena. As its core, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math>${\sf ERNN}$</tex-math></inline-formula> is designed with a novel gating unit named as session gate that includes: (i) four types of actions to simulate common network-induced phenomena during model training; and (ii) the Mealy machine to update states of session gate that adjusts the probability distribution of network-induced phenomena. Taken together, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math>${\sf ERNN}$</tex-math></inline-formula> advances state-of-the-art by realizing the model robustness for network-induced phenomena in an error-resilient manner. We implement <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math>${\sf ERNN}$</tex-math></inline-formula> and evaluate it extensively on both intrusion detection and malware detection systems. By practical evaluation with dynamic bandwidth utilization and different network topologies, we demonstrate that <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math>${\sf ERNN}$</tex-math></inline-formula> can still identify 98.63% of encrypted intrusion traffic when facing about 16% abnormal packet sequences on a 10 Gbps dataplane. Similarly, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math>${\sf ERNN}$</tex-math></inline-formula> can still robustly identify more than 97% of the encrypted malware traffic in multi-user concurrency scenarios. <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math>${\sf ERNN}$</tex-math></inline-formula> can realize <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\sim$</tex-math></inline-formula> 4% accuracy more than SOTA methods. Based on the Integrated Gradients method, we interpret the gating mechanism can reduce the dependencies on local packets (termed <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">dependency dispersion</i> ). Moreover, we demonstrate that <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math>${\sf ERNN}$</tex-math></inline-formula> possesses superior stability and scalability in terms of parameter settings and feature selection.

Asynchronous Fault Detection for Memristive Neural Networks With Dwell-Time-Based Communication Protocol.

This article studies the asynchronous fault detection filter problem for discrete-time memristive neural networks with a stochastic communication protocol (SCP) and denial-of-service attacks. Aiming at alleviating the occurrence of network-induced phenomena, a dwell-time-based SCP is scheduled to coordinate the packet transmission between sensors and filter, whose deterministic switching signal arranges the proper feedback switching information among the homogeneous Markov processes (HMPs) for different scenarios. A variable obeying the Bernoulli distribution is proposed to characterize the randomly occurring denial-of-service attacks, in which the attack rate is uncertain. More specifically, both dwell-time-based SCP and denial-of-service attacks are modeled by means of compensation strategy. In light of the mode mismatches between data transmission and filter, a hidden Markov model (HMM) is adopted to describe the asynchronous fault detection filter. Consequently, sufficient conditions of stochastic stability of memristive neural networks are devised with the assistance of Lyapunov theory. In the end, a numerical example is applied to show the effectiveness of the theoretical method.

An Overview of Filtering for Sampled-Data Systems under Communication Constraints

Survey/Review Study An Overview of Filtering for Sampled-Data Systems under Communication Constraints Ye Wang 1,2, Hong-Jian Liu 1,2, and Hai-Long Tan 1,2 1 School of Mathematics-Physics and Finance, Anhui Polytechnic University, Wuhu 241000, China 2 Key Laboratory of Advanced Perception and Intelligent Control of High-end Equipment, Ministry of Education, Anhui Polytechnic University, Wuhu 241000, China Received: 5 May 2023 Accepted: 21 July 2023 Published: 26 September 2023 Abstract: The sampled-data systems have been extensively applied to practical engineering because the digital signal shows great advantages in data transmission, storage and exchange. As a result, the analysis and synthesis problems of sampled-data systems have attracted ever-growing research interest due mainly to their significant application potential. On the other hand, the filtering or state estimation (which intends to reconstruct real system states from noisy measurements) is viewed as one of the most fundamental research topics in the control community. Until now, a lot of research efforts have been devoted to the filtering problem of sampled-data systems. The objective of the survey is to exhibit a systematic review with respect to filtering and control methods for sampled-data systems under communication constraints. First, some effective filtering algorithms are given. Then, the recent advances are shown in the filtering and control of sampled-data systems subject to network-induced phenomena based on the sampling methods. Finally, some future research topics are given on state estimation of sampled-data systems.

Unreliable networks with random parameter matrices and time-correlated noises: distributed estimation under deception attacks.

This paper examines the distributed filtering and fixed-point smoothing problems for networked systems, considering random parameter matrices, time-correlated additive noises and random deception attacks. The proposed distributed estimation algorithms consist of two stages: the first stage creates intermediate estimators based on local and adjacent node measurements, while the second stage combines the intermediate estimators from neighboring sensors using least-squares matrix-weighted linear combinations. The major contributions and challenges lie in simultaneously considering various network-induced phenomena and providing a unified framework for systems with incomplete information. The algorithms are designed without specific structure assumptions and use a covariance-based estimation technique, which does not require knowledge of the evolution model of the signal being estimated. A numerical experiment demonstrates the applicability and effectiveness of the proposed algorithms, highlighting the impact of observation uncertainties and deception attacks on estimation accuracy.

Scheduling of over-actuated networked control systems

We investigate the scenario where an over-actuated plant is controlled over a network. We concentrate on the effect of two network-induced phenomena: varying transmission intervals and scheduling, in the sense that only one of the actuators receives new data at each transmission instant. We present an emulation-based solution for the controller design, i.e., the controller is first designed to stabilize the origin of the plant ignoring the network and second, the packet-based network is taken into account and conditions on the maximum allowable transmission interval (MATI) and the scheduling protocol are given to preserve the stability of the closed-loop system. Our results are tailored to over-actuated plants leading to significant improvements compared to applying off-the-shelf results available in the literature. In particular, a new model is derived and new conditions on the scheduling protocol are given, which lead to a two-measure stability property for the networked control system. We illustrate how new classes of scheduling protocols can be derived by exploiting over-actuation, which leads to larger MATI bounds compared to applying existing results, as shown on a numerical example.

Optimal LQG Control of Networked Systems Under Traffic-Correlated Delay and Dropout

Transmission delay and packet dropout are inevitable network-induced phenomena that severely compromise the control performance of network control systems. The real-time network traffic is a major dynamic parameter that directly influences delay and reliability of transmission channels, and thus, acts as an unavoidable source of induced coupling among all network sharing systems. In this letter, we analyze the effects of traffic-induced delay and dropout on the finite-horizon quality-of-control of an individual stochastic linear time-invariant system, where quality-of-control is measured by an expected quadratic cost function. We model delay and dropout of the channel as generic stochastic processes that are correlated with the real-time network traffic induced by the rest of network users. This approach provides a pathway to determine the required networking capabilities to achieve a guaranteed quality-of-control for systems operating over a shared-traffic network. Numerical evaluations are performed using realistic stochastic models for delay and dropout. As a special case, we consider exponential distribution for delay with its rate parameter being traffic-correlated, and traffic-correlated Markov-based packet drop model.

Fault detection of networked dynamical systems: a survey of trends and techniques

Fault detection of networked dynamical systems (NDSs) has attracted ever-increasing attention since it can maintain high-quality products as well as operational safety. Considering the utilisation of communication networks, it is desirable to develop engineering-oriented approaches to NDSs subject to the incompleteness from network-induced phenomena (NIP) and the sparsity from communication scheduling. As such, this paper presents a survey of trends and techniques of fault detection in NDSs. First, some typical fault detection methods are summarised based on the various residual assessment functions. Then, some interesting developments are systematically reviewed from two aspects, that is, fault detection with NIP and fault detection under various communication scheduling schemes. In addition, some frontier topics are extensively discussed on fault detection with communication protocols, cyber-attacks, and its applications. Finally, several future works of fault detection problems are investigated to motivate future research.

A survey on state estimation of complex dynamical networks

The analysis and synthesis issues have gained widespread attention for complex dynamical networks (CDNs) over the past few years. Accordingly, some challenges including protocol-based scheduling, security vulnerability, limited communication resources as well as network-induced phenomena have to be handled by developing effective analysis and design approaches. In this paper, we make an attempt to review the latest state estimation schemes for CDNs especially those over the networked environment. Firstly, the engineering background and the current attractive topics in regard to CDNs are presented. Subsequently, we provide a review of several types of state estimation methods under different performance indices, for example, state estimation, set-membership state estimation, optimal state estimation and fault estimation. Next, particular effort is devoted to show the up-to-date progresses on the protocol-based state estimation and compensation-based state estimation approaches for CDNs. Finally, some challenging problems are outlined regarding the future research to promote the theoretical developments in related fields.

Adaptive event-triggered fault detection for Markovian jump systems with network time-delays

In this paper, the adaptive event-triggered fault detection (FD) problem is investigated for Markovian jump systems (MJSs) with network time-delays and data package dropouts. First, a novel event-triggered communication strategy with an adaptive threshold is introduced to screen the sampled signals and reduce the data releasing frequency. Consequently, the limited communication resources and network bandwidth are saved as much as possible. Second, since communication links between the plant and filter are considered to be unreliable, the effects of intermittent package dropouts phenomenon and network time-delays are taken into account simultaneously. By using time-delay system method, the network-induced phenomena, adaptive event-triggered strategy and MJSs are unified into a networked MJSs time-delay system. Then, sufficient conditions are developed such that the resulting residual system is mean-square exponentially stable with the desired [Formula: see text] performance. Finally, a numerical example of the F-404 aircraft engine system is provided to illustrate the effectiveness and potential of the proposed design method.

A survey on sliding mode control for networked control systems

In the framework of the networked control systems (NCSs), the components are connected with each other over a shared band-limited network. The merits of NCSs include easy extensibility, resource sharing, high reliability and so forth. However, the insertion of the communication network brings many challenges, such as network-induced phenomena and cyber-security, which should be handled properly. On the other hand, the sliding mode control (SMC) has become an effective scheme for the synthesis of NCSs due to its strong robustness and SMC has wide applications in NCSs. In this paper, some recent advances on SMC for NCSs are reviewed. In particular, some new SMC schemes for NCSs subject to time-delay, packet losses, quantisation and uncertainty/disturbance are summarised firstly. Subsequently, the problem of SMC for NCSs under scheduling protocols is discussed, where different communication protocols are introduced for the energy saving purpose during the synthesis of NCSs. Next, some recent results on SMC for NCSs with actuator/sensor fault and cyber-attack are recalled. Finally, the conclusion is provided and the potential research challenges on SMC for NCSs are pointed out.

Finite-Horizon H∞ State Estimation for Stochastic Coupled Networks With Random Inner Couplings Using Round-Robin Protocol.

This article is concerned with the problem of finite-horizon H∞ state estimation for time-varying coupled stochastic networks through the round-robin scheduling protocol. The inner coupling strengths of the considered coupled networks are governed by a random sequence with known expectations and variances. For the sake of mitigating the occurrence probability of the network-induced phenomena, the communication network is equipped with the round-robin protocol that schedules the signal transmissions of the sensors' measurement outputs. By using some dedicated approximation techniques, an uncertain auxiliary system with stochastic parameters is established where the multiplicative noises enter the coefficient matrix of the augmented disturbances. With the established auxiliary system, the desired finite-horizon H∞ state estimator is acquired by solving coupled backward Riccati equations, and the corresponding recursive estimator design algorithm is presented that is suitable for online application. The effectiveness of the proposed estimator design method is validated via a numerical example.

Distributed filtering of networked dynamic systems with non-Gaussian noises over sensor networks: A survey

Sensor networks are regarded as a promising technology in the field of information perception and processing owing to the ease of deployment, cost-effectiveness, flexibility, as well as reliability. The information exchange among sensors inevitably suffers from various network-induced phenomena caused by the limited resource utilization and complex application scenarios, and thus is required to be governed by suitable resource-saving communication mechanisms. It is also noteworthy that noises in system dynamics and sensor measurements are ubiquitous and in general unknown but can be bounded, rather than follow specific Gaussian distributions as assumed in Kalman-type filtering. Particular attention of this paper is paid to a survey of recent advances in distributed filtering of networked dynamic systems with non-Gaussian noises over sensor networks. First, two types of widely employed structures of distributed filters are reviewed, the corresponding analysis is systematically addressed, and some interesting results are provided. The inherent purpose of adding consensus terms into the distributed filters is profoundly disclosed. Then, some representative models characterizing various network-induced phenomena are reviewed and their corresponding analytical strategies are exhibited in detail. Furthermore, recent results on distributed filtering with non-Gaussian noises are sorted out in accordance with different network-induced phenomena and system models. Another emphasis is laid on recent developments of distributed filtering with various communication scheduling, which are summarized based on the inherent characteristics of their dynamic behavior associated with mathematical models. Finally, the state-of-the-art of distributed filtering and challenging issues, ranging from scalability, security to applications, are raised to guide possible future research.

Robust distributed state estimation for Markov coupled neural networks under imperfect measurements

In this paper, the issue of robust distributed state estimation is investigated for Markov coupled neural networks in the discrete-time domain. Fully considering network-induced phenomena, the signal quantization and sensor saturation existed in actual measurement are investigated in a unified framework through the Kronecker delta function. Moreover, a Markov chain is used to describe the structural variations in the addressed systems. The main attention of this paper is devoted to designing a mode-dependent estimator to estimate the system states through available output measurements, which ensures that the resulting system is stochastically stable and satisfies strictly dissipative property concurrently. By applying Lyapunov stability theory and a modified matrix decoupling method, some sufficient criteria are derived to obtain an explicit expression of the mode-dependent estimator. Finally, an example is presented to elucidate the validity of the proposed method.

Data-Driven Adaptive Control for Distributed Energy Resources

This paper presents a new control method for power electronic interfaced distributed energy resources in DC grids. The proposed control method relies on a data-driven identification approach that is used in an adaptive control framework and does not require accurate models of the converters or offline training for control synthesis. In this paper, the proposed control method is able to guarantee a good performance and bus voltage stability without prior knowledge of the uncertainties. These uncertainties include measurement noise and network-induced phenomena from interactions with neighboring nodes, such as system reconfigurations resulting from converters connection/disconnection.

Dynamical performance analysis of communication-embedded neural networks: A survey

State estimation and synchronization of neural networks (NNs) have recently received ever-increasing research interests due to a large number of successful applications in various fields such as repetitive learning, classification of patterns, nonlinear control, adaptive control, image processing, and so forth. Owing to limited communication bandwidth, the network-induced phenomena and the adopted communication scheduling may cause inevitable negative effects or the degradation of dynamical performance of NNs. This paper, from the perspective of dynamical behavior, renders a comprehensive summary on the recent advances of communication-embedded NNs and their application in nonlinear control. First, some common network-induced phenomena and communication protocols are roughly introduced from both models and mechanisms. Then, the state estimation and synchronization analysis of various NNs with network-induced phenomena are systematically reviewed and some interesting results are separately presented. Furthermore, the latest advances for the cases with communication protocols are profoundly surveyed from three aspects: state estimation, synchronization analysis and applications in control realm. Finally, some challenged problems are listed and several potential future research directions are highlighted.

Event-based finite horizon state estimation for stochastic systems with network-induced phenomena

This paper investigates a finite horizon state estimation problem for a class of discrete-time stochastic systems with random transmission delays and out-of-order packets of data. Employing an event-driven signal-choosing scheme of logic zero-order-holder (LZOH), a system model is established synthetically in a unified form considering the network-induced phenomena, to drop out-of-order packets and improve system performance. By virtue of the established system model, a novel minimum error covariance matrix for the augmented state-space is obtained from the estimated variance constraint. With the aid of a finite horizon, the upper boundary of estimation error covariance is introduced during the information transmission from sensor to estimator, and the appropriate filter parameters are probed. To improve the estimation performance and alleviate the computation burden, an estimation-based compensation approach for random transmission delays is proposed using the received valid signals. Finally, the effectiveness and applicability of the proposed state estimation method are illustrated by a numerical simulation.

Sliding mode control for networked systems with randomly varying nonlinearities and stochastic communication delays under uncertain occurrence probabilities

Abstract In this paper, we aim to propose the robust sliding mode control (SMC) scheme for discrete networked systems subject to randomly occurring uncertainty (ROU), randomly varying nonlinearities (RVNs) and multiple stochastic communication delays (MSCDs). Here, a series of mutually independent Bernoulli distributed random variables is introduced to model the phenomena of the ROU, RVNs and MSCDs, where the occurrence probabilities of above phenomena are allowed to be uncertain. For the addressed systems, an SMC strategy is given such that, for above network-induced phenomena, the stability of the resulted sliding motion can be guaranteed by presenting a new delay-dependent sufficient criterion via the delay-fractioning method. Moreover, the discrete sliding mode controller is synthesized such that the state trajectories of the system are driven onto a neighborhood of the specified sliding surface and remained thereafter, i.e., the reachability condition in discrete-time setting is verified. Finally, the usefulness of the proposed SMC method is illustrated by utilizing a numerical example.

Robust H∞ state estimation for BAM neural networks with randomly occurring uncertainties and sensor saturations

Abstract In this paper, the robust H∞ state estimation problem is investigated for a class of discrete-time delayed bidirectional associative memory neural networks with randomly occurring uncertainties (ROUs) and sensor saturations. In order to more clearly reflect the reality in a networked environment, Bernoulli distributed white sequences with known probabilities are introduced to characterize the network-induced phenomena of the ROUs and the randomly occurring sensor saturations. The purpose of the addressed problem is to design a robust H∞ state estimator such that the dynamics of the estimation error is asymptotically stable in the mean square. By using matrix computation and stochastic analysis approaches, a sufficient condition is established to ensure the desired H∞ performance requirements, and the explicit form of the estimator gains can be obtained by solving some matrix inequalities. Finally, an illustrative example is provided to demonstrate the effectiveness of the proposed state estimation approach.

Quantized asynchronous dissipative state estimation of jumping neural networks subject to occurring randomly sensor saturations

In this work, we study the quantized asynchronous dissipative state estimation issue for Markov jumping neural networks subject to randomly occurring sensor saturations. The network-induced phenomena (e.g. the sensor saturations and the signal quantization) are assumed to be occurring randomly. A stochastic Kronecker delta function is introduced to model such phenomena. The main work aims to design an asynchronous state estimator that assures the underlying error dynamics to be strictly (X,Y,Z)−γ−dissipative. Based on the stochastic Kronecker delta function method and stochastic analysis technique, we establish some conditions for the sake of guaranteeing the existence of the desired state estimator. We finally explain the effectiveness of the proposed design approach by means of a numerical example.