Packet Dropout Phenomenon Research Articles

Cluster synchronization of complex dynamic networks under pinning control via a limited capacity communication channel

In this paper, the cluster synchronization pinning control problem is investigated for the continuous-time-directed complex dynamic networks under the bit rate constraint. Firstly, a node selection scheme based on differences in node degrees is proposed to achieve cluster synchronization. Subsequently, a coding–decoding scheme is designed during data transmission through digital communication channels where only a limited number of encoded signal sequences are sent to the controller side. Further, regarding the unreliability of the channel, the phenomenon of packet dropouts is considered during the encoding process, which is modeled by using a series of independent Bernoulli distributed random variables. The pinning controller is then designed based on the received decoding information. By means of the convex optimization method, a sufficient condition is established to ensure that the cluster synchronization error is ultimately bounded and the desired controller gain is determined based on the solution to matrix inequalities. Finally, the effectiveness of the obtained results is validated through a simulation example.

Recursive state estimation for two-dimensional systems over decode-and-forward relay channels: A local minimum-variance approach

The paper is concerned with the recursive state estimation problem for a class of two-dimensional systems over decode-and-forward relay channels subject to packet dropouts. Taking into account the sensor's limited transmission ability, a decode-and-forward relay is deployed between the sensor and the remote estimator to enlarge the propagation distance and improve the communication quality. The packet dropout phenomenon is described by two sequences of mutually uncorrelated random variables that obey Bernoulli distributions. The main objective of this paper is to design a recursive state estimator for the underlying system with guaranteed estimation performance. An upper bound on the estimation error variance is firstly derived by resorting to some coupled difference equations, and then such an upper bound is locally optimized at each iteration through proper design of the estimator gain. Furthermore, the impacts of the packet dropouts and the decode-and-forward relay on the estimation performance are evaluated with rigorously theoretical analysis. Finally, an illustrative example is presented to validate the effectiveness of the proposed estimation scheme.

A switched system approach to exponential stabilization of interval type-2 fuzzy nonlinear systems with packet dropouts

This paper investigates the exponential stabilization problem for interval type-2 (IT2) fuzzy system with parameter uncertainties and packet dropouts. The sample-and-hold characteristic is simulated by employing an input delay methodology, and we present a model for the packet dropout phenomenon using switched approach. Some novel two-side delay-dependent looped-functionals are proposed by considering state information in the intervals from tr to t and t to tr+1 which relaxes the monotonic constraint of the Lyapunov–Krasovskii functional (LKF) and enhances the utilization of the actual sampling pattern. Additionally, some free-weighting matrices, slack matrices, the bound information in membership functions (MFs) and the signs of the first-order time derivative of fuzzy MFs are used to obtain a novel relaxed sufficient conditions for exponential stabilization of interval type-2 fuzzy systems (IT2 FS). Based on the LKF theory, new relaxed sufficient conditions with linear matrix inequalities (LMIs) constraints are derived. According to the criterion, sampled-data controller is devised to ensure that the closed-loop system is exponential stabilization. Finally, an illustrative example is presented to demonstrate the practicality and effectiveness of the method proposed. Comparative analysis highlights the simplicity and practicality of the suggested algorithm.

Particle-Filter-Based State Estimation for Delayed Artificial Neural Networks: When Probabilistic Saturation Constraints Meet Redundant Channels.

In this brief, the state estimation problem is investigated for a class of randomly delayed artificial neural networks (ANNs) subject to probabilistic saturation constraints (PSCs) and non-Gaussian noises under the redundant communication channels. A series of mutually independent Bernoulli distributed white sequences are introduced to govern the random occurrence of the time delays, the saturation constraints, and the transmission channel failures. A comprehensive redundant-channel-based communication mechanism is constructed to attenuate the phenomenon of packet dropouts so as to enhance the quality of data transmission. To compensate for the influence of randomly occurring time delays, the corresponding occurrence probability is exploited in the process of particle generation. In addition, an explicit expression of the likelihood function is established based on the statistical information to account for the impact of PSCs and redundant channels. By virtue of the modified operations of particle propagation and weight update, a particle-filter-based state estimation algorithm is proposed with mild restriction on the system type. Finally, an illustrative example with Monte Carlo simulations is provided to demonstrate the effectiveness of the developed state estimation scheme.

Finite-time tolerant containment control for IT2 T–S fuzzy network multi-agent systems with actuator faults, packet dropouts and DoS attacks

This article studies finite-time tolerant containment control issue for uncertain nonlinear networked multi-agent systems (MASs) with actuator faults, denial-of-service (DoS) attacks and packet dropouts, under the framework of interval type-2 (IT2) Takagi–Sugeno (T–S) fuzzy method. Firstly, based on establishing the actuator fault models and introducing Bernoulli random distribution to represent the packet dropouts phenomenon, the IT2 T–S fuzzy network MASs under actuator faults and packet dropouts are constructed as switchable systems according to the attack situations on the communication channels. Secondly, the slack matrix with more information of lower and upper membership functions is introduced in the stability analysis to reduce conservatism. And on basis of Lyapunov stability theory and average dwell-time method, finite-time tolerant containment control protocol is proposed, which makes the follower’ states converge to the convex hull controlled by the leaders in finite time. Finally, the effectiveness of the control protocol designed in this article is verified by numerical simulation.

Robust Control for Dynamic Train Regulation in Fully Automatic Operation System Under Uncertain Wireless Transmissions

For enhancing the operation efficiency of the fully automatic operation (FAO) system in the urban rail transit (URT), this paper investigates the robust dynamic train regulation problem with respect to frequent disruptions and imperfect wireless transmissions. To better express the characteristic of the arriving passengers, the fuzzy passenger arrival rate is adopted to address the uncertainty of the passenger flow, and a T-S fuzzy state-space model is established to express the periodical movement of the train traffic in an URT loop line. By considering the possible packet dropout phenomenon during the wireless data transmissions, which may lead to the instability of the train traffic system and degrade the performance of the regulation strategy, a robust real-time train regulation strategy is developed based on the fuzzy predictive control theory, which distinguishes existing studies in that the uncertainty dropout rate is contemplated to address the complexity of the actual operation environment. A sufficient condition for the proposed control law is presented to guarantee that the nominal train schedule is recovered from disturbed situations with a given attenuation level by means of the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$H_{\infty }$ </tex-math></inline-formula> performance index, and meanwhile the optimization of the upper bound on the objective function balancing the service efficiency and control cost is achieved. Numerical simulations based on the Beijing subway loop line 2 are presented for demonstration of the effectiveness of the introduced strategy.

Intermittent observer-based dissipative saturation control for Korteweg-de Vries-Burgers equation with stochastic noise and incomplete measurable information

In this paper, a (Q, S, R)-r˜-dissipative control rate based on intermittent observations is developed for the Korteweg-de Vries-Burgers equation (KdVB) with stochastic noise and incomplete measurable information. Different from the existing works, the measurement transmissions from the sensor to the controller and from the controller to the actuator are assumed to be imperfect (i.e., the phenomenon of data packet dropouts may occur intermittently), and an intermittent observer is proposed to track the part state of the equation rather than the existing traditional full-order Lebesgue observer. In addition, a (Q, S, R)-r˜-saturation dissipation index is selected to design a unified control for KdVB dynamics, which is more general than H∞ exponent. More importantly, the calculated (Q, S, R)-r˜-saturation dissipation performance is completed under the condition of partial state loss of KdVB, which has great research significance and value in practical engineering applications. Finally, an example is given to verify the effectiveness and superiority of the proposed method.

Stability analysis of networked control systems under DoS attacks in frequency domain via game theory strategy

In this paper, the stability analysis of networked control systems (NCSs) under Denial-of-Service (DoS) attacks is studied. Firstly, the stability analysis for NCSs free of DoS attacks is investigated. Considering the DoS attacks, the game theory is introduced to establish the non-cooperative game between defender and attacker of NCSs. At the same time, the stability of NCSs is analysed in frequency domain, and the signal-to-noise ratio (SNR) expression is given by using spectral decomposition technique and Nash equilibrium (NE) strategy. The packet dropout phenomenon caused by DoS attacks is described by Bernoulli distribution. By virtue of the NE strategy, the stability of NCSs can be optimised. In the obtained results, the influence of the traditional constraints and DoS attacks is illustrated. Finally, some numerical examples are given to show the effectiveness of the derived theoretical results.

Robust fusion steady‐state estimators for networked stochastic uncertain systems with packet dropouts and missing measurements

AbstractIn this article, the robust fusion steady‐state filtering problem is investigated for a class of multisensor networked systems with mixed uncertainties. The uncertainties include state‐dependent and noise‐dependent multiplicative noises, missing measurements, packet dropouts, and uncertain noise variances, the phenomena of missing measurements and packet dropouts occur in a random way, and are described by two Bernoulli distributed random variables with known conditional probabilities. Using a model transformation approach, which consists of augmented approach, de‐randomization approach and fictitious noise approach, the original multisensor system under study is converted into a multi‐model multisensor system with only uncertain noise variances. According to the minimax robust estimation principle, based on the worst‐case subsystems with conservative upper bounds of uncertain noise variances, the robust local steady‐state Kalman estimators (predictor, filter, and smoother) are presented in a unified framework. Applying the optimal fusion algorithm weighted by matrices, the robust distributed weighted state fusion steady‐state Kalman estimators are derived for the considered system. The robustness of the proposed estimators is proved by using a combination method consisting of augmented noise approach, decomposition approach of non‐negative definite matrix, matrix representation approach of quadratic form, and Lyapunov equation approach, such that for all admissible uncertainties, the actual steady‐state estimation error variances of the estimators are guaranteed to have the corresponding minimal upper bounds. The accuracy relations among the robust local and fused steady‐state Kalman estimators are proved. An example with application to autoregressive moving average (ARMA) signal processing is proposed, which shows that the robust local and fusion signal estimation problems can be solved by the state estimation problems. Simulation example verifies the effectiveness and correctness of the proposed results.

Stability Analysis of T–S Fuzzy Control System With Sampled-Dropouts Based on Time-Varying Lyapunov Function Method

In this paper, the stability problem of sampled data Takagi–Sugeno fuzzy control systems with packet dropouts is investigated. A sampling-dependent time-varying Lyapunov function (SDTVLF) is constructed to analyze the stability problem of the system and a switched system approach is proposed to model the packet dropouts phenomenon. On this basis, by dividing the sampling input available interval and unavailable interval into several segments, the matrix functions of the SDTVLF are chosen to be continuous piecewise linear. Then, by using the proposed SDTVLF approach, computable convex conditions are obtained for the sampling input unavailable interval and the sampling input available interval in framework of dwell time. By confining the sampling input unavailable interval with an upper bound and confining the sampling input available interval with a lower bound, the SDTVLF is always decreasing in all sampling input available and unavailable interval, which can make the sampled control system tolerate a larger packet dropout rate. A numerical example is provided to show the efficiency of the proposed results.

H ∞ filtering of networked switched systems with multiple packet dropouts via switched Lyapunov function approach

This study considers a H ∞ filtering problem for discrete-time switched systems with multiple packet dropouts. The switching signal may be lost that is similar to the measurement signal during transmission. Also, the multiple packet-dropouts phenomenon is described by Bernoulli binary sequences. With the aid of a novel switched Lyapunov function containing a random change of switching rate, sufficient conditions in the form of linear matrix inequalities for the desired filter are derived, which ensure the filtering error system is exponentially stable in the sense of mean square with prescribed H ∞ performance. Finally, a practical example is given to verify the effectiveness of the proposed approaches.

Distributed Fusion Estimation for Multisensor Multirate Systems With Packet Dropout Compensations and Correlated Noises

This article investigates distributed fusion estimation problems for multisensor multirate (MSMR) stochastic systems with correlated noises (CNs) and packet dropouts (PDs). The state updates at the fast rate while sensors uniformly sample at positive integer multiples of the state updating period. Different sensors may have different sampling rates. The system noise and measurement noises are auto- and cross-correlated at the same instant. The phenomenon of PDs randomly occurs during data transmission from a sensor to a data processor through unreliable networks. A recent developed compensation strategy that a predictor of a lost packet is employed as a compensator is adopted to optimize the tracking process. First, an optimal linear local filter (LF) for each sensor at measurement sampling points (MSPs) is presented by using an innovation analysis approach. Then, a local estimator (LE) at state updating points (SUPs) is proposed by filtering or prediction based on the LF at MSPs. Furthermore, estimation error cross-covariance matrices (CCMs) between arbitrary two LEs at SUPs are deduced, which can recursively be calculated by three joint difference equations. Finally, a distributed fusion filter (DFF) weighted by matrices in the sense of linear unbiased minimum variance (LUMV) is addressed. Period steady-state (PSS) property of the LEs, CCMs, and DFF is proved. A simulation example verifies the effectiveness of algorithms.

Gaussian Filter for Nonlinear Networked Systems With Synchronously Correlated Noises and One- Step Randomly Delayed Measurements and Multiple Packet Dropouts

In this paper, a Gaussian recursive filter is proposed for nonlinear networked systems with synchronously correlated noises and one-step randomly delayed measurements and multiple packet dropouts. Since each packet at the sensor side is sent only once and the system has one-step delay and multiple packet dropouts, the data processing center may receive zero/one/two measurements. By augmenting the system state at last epoch to the current one, employing two Bernoulli distributed random variables to describe the phenomena of delay and packet dropouts and using the prediction compensation mechanism, a novel model is derived. At the same time, to deal with the problem of correlated noise, Gaussian approximation recursive filter (GASF) is used. Based on the above processing, a nonlinear Gaussian recursive filter and corresponding numerical implementation based on cubature Kalman filter (CKF) are given. Compared with the existing results in linear systems, the proposed algorithm is more general and has higher estimation accuracy in nonlinear systems. The simulation results show the effectiveness of the proposed algorithm.

Self-Tuning Distributed Fusion Filter for Multi-Sensor Networked Systems with Unknown Packet Receiving Rates, Noise Variances, and Model Parameters.

In this study, we researched the problem of self-tuning (ST) distributed fusion state estimation for multi-sensor networked stochastic linear discrete-time systems with unknown packet receiving rates, noise variances (NVs), and model parameters (MPs). Packet dropouts may occur when sensor data are sent to a local processor. A Bernoulli distributed stochastic variable is adopted to depict phenomena of packet dropouts. By model transformation, the identification problem of packet receiving rates is transformed into that of unknown MPs for a new augmented system. The recursive extended least squares (RELS) algorithm is used to simultaneously identify packet receiving rates and MPs in the original system. Then, a correlation function method is used to identify unknown NVs. Further, a ST distributed fusion state filter is achieved by applying identified packet receiving rates, NVs, and MPs to the corresponding optimal estimation algorithms. It is strictly proven that ST algorithms converge to optimal algorithms under the condition that the identifiers for parameters are consistent. Two examples verify the effectiveness of the proposed algorithms.

Fault detection of nonlinear stochastic systems via a dynamic event-triggered strategy

This paper studies the fault detection problem of nonlinear stochastic systems with randomly occurring nonlinearity and missing measurements via a dynamic event-triggered strategy. Firstly, the dynamic event-based data transmission strategy is provided to increase network utilization efficiency, while the data transmission threshold is changed with an adaptive function. This design idea can reduce network transmission pressure more effectively than the traditional one with fixed threshold. Secondly, two independent Bernoulli stochastic sequences are provided to describe the randomly occurring nonlinearity and measured-data packet dropout phenomenon, respectively. By considering time-varying delays, dynamic event-triggered strategy, randomly occurring nonlinearity and packet dropout simultaneously, a unified dynamic model for fault detection is established to generate mean-square asymptotical stability and desirable detection performance. Three examples are finally provided to generate the practicability of proposed technique.

Fuzzy Output Tracking Control and Filtering for Nonlinear Discrete-Time Descriptor Systems Under Unreliable Communication Links.

In this paper, the problems of output tracking control and filtering are investigated for Takagi-Sugeno fuzzy-approximation-based nonlinear descriptor systems in the discrete-time domain. Especially, the unreliability of the communication links between the sensor and actuator/filter is taken into account, and the phenomenon of packet dropouts is characterized by a binary Markov chain with uncertain transition probabilities, which may reflect the reality more accurately than the existing description processes. A novel bounded real lemma (BRL), which ensures the stochastic admissibility with H∞ performance for fuzzy discrete-time descriptor systems despite the uncertain Markov packet dropouts, is presented based on a fuzzy basis-dependent Lyapunov function. By resorting to the dual conditions of the obtained BRL, a solution for the designed fuzzy output tracking controller is given. A design method for the full-order fuzzy filter is also provided. Finally, two examples are finally adopted to show the applicability of the achieved design strategies.

Global pinning synchronization of stochastic delayed complex networks

In this paper, the global pinning synchronization problem of a class of complex dynamical networks with hybrid couplings, time delays, random data packet dropouts and multiple stochastic disturbances is investigated. The hybrid couplings are portrayed in three forms: constant couplings, discrete-delay couplings and distributed-delay couplings. The phenomenon of random packet dropouts is described as a binary random variable which obeys the Bernoulli distribution taking values of 0 and 1 with a certain probability. Multiple noisy processes herein are characterized by Brownian motions, which act on all coupling terms as well as the overall dynamics equation. By applying the Lyapunov method and stochastic analysis technique, sufficient conditions are established to ensure the considered stochastic delayed complex network to globally synchronize to a reference trajectory in the mean-square sense via the developed randomly occurring pinning control strategy. The obtained criteria are within the framework of linear matrix inequations. Finally, a simulation example is presented to verify the feasibility and effectiveness of the derived theoretical results.

Resilient Control of Wireless Networked Control System Under Denial-of-Service Attacks: A Cross-Layer Design Approach.

The resilient control refers to the control methodology which provides an interdisciplinary solution to secure the control system. In this paper, the resilient control problem is investigated for a class of wireless networked control systems (WNCS) under a denial-of-service (DoS) attack. In the presence of the DoS attacker, the control command sent by the transmitter may be interfered, which can cause the degradation of the signal-to-interference-plus-noise ratio and further lead to packet dropout phenomenon. Such a packet dropout phenomenon is described by a two-state Markov-chain. A cross-layer view is adopted toward the security issue of the considered WNCS. The Nash power strategies and optimal control strategy in the delta-domain are obtained in the cyber- and physical-layer, respectively. Based on the obtained strategies, the coupled-design problem is solved which aims to drive the underlying control performance to the desired security region by dynamically manipulating the cyber-layer pricing parameters. Finally, a numerical simulation is conducted to verify the validity of the proposed methodology.

Event-triggered resilient filtering with stochastic uncertainties and successive packet dropouts via variance-constrained approach

In this paper, we discuss the event-triggered resilient filtering problem for a class of time-varying systems subject to stochastic uncertainties and successive packet dropouts. The event-triggered mechanism is employed with hope to reduce the communication burden and save network resources. The stochastic uncertainties are considered to describe the modelling errors and the phenomenon of successive packet dropouts is characterized by a random variable obeying the Bernoulli distribution. The aim of the paper is to provide a resilient event-based filtering approach for addressed time-varying systems such that, for all stochastic uncertainties, successive packet dropouts and filter gain perturbation, an optimized upper bound of the filtering error covariance is obtained by designing the filter gain. Finally, simulations are provided to demonstrate the effectiveness of the proposed robust optimal filtering strategy.

Distributed Filtering for Switched Linear Systems With Sensor Networks in Presence of Packet Dropouts and Quantization

This paper is concerned with the distributed $H_\infty $ filtering problem of discrete-time switched linear systems in sensor networks in face of packet dropouts and quantization. Specifically, due to the packet dropout phenomenon, the filters may lose access to the real-time switching signal of the plant. It is assumed that the maximal packet dropout number of switching signal is bounded. Then, a distributed filtering system is proposed by further considering the quantization effect. Based on the Lyapunov stability theory, a sufficient condition is obtained for the convergence of filtering error dynamics. The filter gain design is transformed into a convex optimization problem. In this paper, a quantitative relation between the switching rule missing rate and filtering performance is established. Furthermore, the upper bound of the switching rule missing rate is also calculated. Finally, the effectiveness of the proposed filter design is validated by a simulation study on the pulse-width-modulation-driven boost converter circuit. The impact of noise covariance, system dynamics, and network connectivity is studied, and some discussions are presented on how these parameters affect the filtering performance.