Data Packet Dropout Research Articles

Asymptotic Stability of Delayed Boolean Networks With Random Data Dropouts.

In real networks, communication constraints often prevent the full exchange of information between nodes, which is inevitable. This brief investigates the problem of time delay and randomly missing data in Boolean networks (BNs). A Bernoulli random variable is assigned to each node to characterize the probability of data packet dropout. Time delay and missing data are modeled by independent random variables. A novel data-sending rule that incorporates both communication constraints is proposed. An augmented system, comprising current states, delayed information, and successfully transmitted data, is established for theoretical analysis. Using the semitensor product (STP), the necessary and sufficient condition for asymptotic stability of delayed BNs with random data dropouts is derived. The convergence rate is also obtained.

Frequency regulation scheme in power system using an observer-based waiting event-triggered control with random features

Addressed in this paper is the observer-based waiting event-triggered control issue in multi-area load frequency control (LFC) power system subject to random communication delay and data packet dropout. In order to maintain the grid frequency stability of the multi-area LFC power system in an open communication network, an observer-based waiting event-triggered scheme (WETS) is proposed to deal with frequency fluctuation and extend the life of control equipments. Compared with the existing event-triggered mechanism, the proposed WETS has the merits of continuous event-triggered scheme and the periodic sampling event-triggered scheme in the both of theory and application. Then, by well considering the negative factors of data packet dropout and random time delay in data transmission, a stochastic delay-dependent multi-area LFC model is established, which involves the information of observer-based WETS, random communication delay and packet dropout in a unified structure. Further, an improved Lyapunov–Krasovskii functional (LKF) is constructed by dividing the interval of transmitted delay. Based on the constructed Lyapunov functional, Park’s theorem and Jensen’s integral inequality, some less conservative stabilization criteria are obtained. Finally, the validity of the proposed control method is verified via numerical examples.

Sequential Fusion Filter for State Estimation of Nonlinear Multi-Sensor Systems with Cross-Correlated Noise and Packet Dropout Compensation

This paper is concerned with the problem of state estimation for nonlinear multi-sensor systems with cross-correlated noise and packet loss compensation. In this case, the cross-correlated noise is modeled by the synchronous correlation of the observation noise of each sensor, and the observation noise of each sensor is correlated with the process noise at the previous moment. Meanwhile, in the process of state estimation, since the measurement data may be transmitted in an unreliable network, data packet dropout will inevitably occur, leading to a reduction in estimation accuracy. To address this undesirable situation, this paper proposes a state estimation method for nonlinear multi-sensor systems with cross-correlated noise and packet dropout compensation based on a sequential fusion framework. Firstly, a prediction compensation mechanism and a strategy based on observation noise estimation are used to update the measurement data while avoiding the noise decorrelation step. Secondly, a design step for a sequential fusion state estimation filter is derived based on an innovation analysis method. Then, a numerical implementation of the sequential fusion state estimator is given based on the third-degree spherical-radial cubature rule. Finally, the univariate nonstationary growth model (UNGM) is combined with simulation to verify the effectiveness and feasibility of the proposed algorithm.

Event-triggered nonfragile predictive control for networked control system with redundant channels

This paper is concerned with the nonfragile predictive control problem for the networked control system (NCS) with input delay and network-induced time delays (NITDs). First, the transmission strategy governed by redundant channels is introduced to reduce the occurrence of data packet dropout between the sensor and the observer. Moreover, the event-triggered mechanism (ETM) is employed to save the network resources, where the maximum triggered interval is given. Subsequently, a nonfragile networked predictive control scheme (NPCS) is designed to address the input delay and the NITDs. Accordingly, the sufficient criterion ensuring mean-square exponential stability (MSES) is derived by employing the Lyapunov–Krasovskii stability theorem. Both the observer gain matrix (OGM) and the controller gain matrix (CGM) are gained through solving the nonlinear minimization problem with convex constraints. Finally, a simulation example is utilized to demonstrate the usefulness of the proposed main results.

Average dwell time based networked predictive control for switched linear systems with data transmission time-varying delays

Abstract This paper investigates an average dwell time (ADT) based networked predictive control for discrete-time linear systems with data transmission time-varying delays. Since the data transmission time-varying delays and packet dropouts simultaneously exhibit in the sensor-to-controller and the controller-to-actuator channels, a predictor is proposed to approximate the current state with the delayed state measurement. Based on the estimation state, a synchronous switching controller is proposed, and its mode-dependent and mode-independent design methods are presented. The proposed methods not only guarantee the desired stability of the closed-loop systems but also tolerate some locally unstable subsystems. In addition, the decay rate of the closed-loop system can be pre-regulated. Moreover, given some prior activation probabilities of subsystems, the ADT can be further reduced, thus more frequent switching dynamics can be allowed. Numerical example is provided to show the effectiveness of the developed method.

Finite-time boundedness of networked control systems via hybrid predictive control based on cloud storage method

In this paper, we investigate the problem of predictive control for a class of networked control systems (NCSs) based on the cloud storage method (CSM). To improve the system’s transient performance and actively compensate for the time-varying delays (TVDs) and data packet dropouts (DPDs), a hybrid networked predictive control (NPC) method is proposed. The switched control system is established based on a class of switching signals varying with the TVDs and DPDs. Novel finite-time hybrid stabilization controllers are proposed by using NPC theory. Then, sufficient conditions are developed to ensure the finite-time boundedness (FTB) of the augmented switched systems. Two experiments illustrate the performance to verify the effectiveness of the proposed results.

H∞ State-Feedback Control of Multi-Agent Systems with Data Packet Dropout in the Communication Channels: A Markovian Approach.

The paper presents an H∞ type control procedure for multi-agent systems taking into account possible data dropout in the communication network. The data dropout is modelled using a standard homogeneous Markov chain leading to an H∞ type control problem for stochastic multi-agent systems with Markovian jumps. The considered H∞ type criterion includes, besides the components corresponding to the attenuation condition of exogenous disturbance inputs, quadratic terms aiming to acquire the consensus between the agents. It is shown that in the case of identical agents, a state-feedback controller with Markov parameters may be determined solving two specific systems of Riccati equations whose dimension does not depend on the number of agents. Iterative procedures to solve such systems are also presented together with an illustrative numerical example.

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.

Neural network-based control for RRP-based networked systems under DoS attacks with power interval

This paper is concerned with the optimal control problem for networked systems with the round-robin protocol (RRP) under the denial-of-service (DoS) attacker with power interval. In the literature, the control design is studied for the linear networked system subject to the DoS attacks with a known constant power or a known constant probability of data-packet dropouts. In this paper, the objective is to control the unknown nonlinear system in the communication network subject to the DoS attacks with a power interval and a time-varying probability of data-packet dropouts. The effects of the DoS attacks with power interval on a networked system under RRP are modeled accurately. A neural network (NN) -based observer is designed for the nonlinear system under the DoS attacks with power interval, and the relationship between the DoS attacker power interval and state estimation error is obtained. The NN actor–critic policy for the optimal control of the RRP-based networked system under the DoS attacks with power interval is found with adaptive dynamical programming, and stability of the resulting control system is analyzed. The proposed control method is demonstrated by a networked uninterruptible power supply system.

Fixed-Time Cooperative Tracking for Delayed Disturbed Multi-Agent Systems Under Dynamic Event-Triggered Control

Recently, researchers have obtained many new results about the multi-agent systems (MASs) [1]–[3]. In [1], the fixed-time cooperative control (FTCC) algorithm of linear MASs with matched disturbances was proposed. The nonholonomic chained-form dynamics case was considered in [2]. In [3], the output tracking problem with data packet dropout was solved for high-order MASs. Moreover, delay frequently occurs because of the non-ideal data transmission [4], and the corresponding FTCC algorithm of MASs with delay was given in [5].

Event-triggered fault detection for nonlinear descriptor networked control systems

This paper investigates the problem of filter-based fault detection (FD) for stochastic nonlinear descriptor networked control systems (NCSs). Based on the T-S fuzzy dynamic modeling technique, the nonlinear descriptor NCS is transformed into a combination of local linear models. Then, to save the network resources, a new adaptive event-triggered mechanism is presented to determine whether the sampled data is to be transmitted. Considering network-induced delay, data packet dropout, and asynchronous premise variables, a time-delay augmented system is established to improve the detection performance. The additional item caused by data packet dropout is regarded as a disturbance, and the effect of this part is attenuated by using the H∞ method. A sufficient condition is proposed to guarantee the admissibility of the augmented system, meanwhile, the desired filter parameters are obtained. Finally, simulation results are provided to demonstrate the feasibility of the proposed strategy.

Fixed-Time Output Consensus Tracking for High-Order Multi-Agent Systems With Directed Network Topology and Packet Dropout

This paper studies the problem of fixed-time output consensus tracking for high-order multi-agent systems (MASs) with directed network topology with consideration of data packet dropout. First, a predictive compensation based distributed observer is presented to compensate for packet dropout and estimate the leader's states. Next, stability analysis is conducted to prove fixed time convergence of the developed distributed observer. Then, adaptive fixed-time dynamic surface control is designed to counteract mismatched disturbances introduced by observation error, and stabilize the tracking error system within a fixed time, which overcomes explosion of complexity problem and singularity problem. Finally, simulation results are provided to verify the effectiveness and superiority of the consensus tracking strategy proposed. The contribution of this paper is to provide a fixed-time distributed observer design method for high-order MAS under directed graph subject to packet dropout, and a novel fixed-time control strategy which can handle mismatched disturbances and overcome explosion of complexity and singularity problem.

Feedback Stabilization for Discrete-Time Singularly Perturbed Systems via Limited Information With Data Packet Dropout

This paper is concerned with the feedback stabilization of discrete time singularly perturbed systems under information constraints. First, the designed coder and decoder are connected via a limited communication channel with data packet dropout, which is assumed to obey the independent and identically distributed Bernoulli processes. Under the above conditions, the transmission error between estimated state and input state will tend to zero exponentially. Meanwhile, the upper bound of the packet loss rate can also be obtained when the communication channel capacity is limited. Then, under the proposed coder-decoder pair, a sufficient condition for the asymptotic stability of the closed-loop system is given by linear matrix inequalities. Furthermore, the upper bound of the small perturbation parameter for the stability of systems can be explicitly estimated with a workable computational way. Finally, two examples are given to illustrate the effectiveness of the proposed method.

L1 control for Itô stochastic nonlinear networked control systems

This paper investigates L1 control problem for a class of nonlinear stochastic networked control systems (NCSs) described by Takagi-Sugeno (T-S) fuzzy model. By exploiting a delay-dependent and basis-dependent Lyapunov-Krasovskii function and by means of the Itô stochastic differential equation technique, results on stability and L1 performance are proposed for the T-S fuzzy stochastic NCS. Specially, attention is focused on the fuzzy controller design that guarantees the closed-loop T-S fuzzy stochastic NCS is mean-square asymptotically stable and satisfies a prescribed L1 noise attenuation level [Formula: see text] with respect to all persistent and amplitude-bounded disturbance input signals. To reduce the conservatism of design, the signal transmission delay, data packet dropout, and quantization have been taken into consideration in the controller design. The corresponding design problem of L1 controller is converted into a convex optimization problem by solving a set of linear matrix inequalities (LMIs). Finally, simulation examples are provided to illustrate the feasibility and effectiveness of the proposed method.

Multi-packet transmission aero-engine DCS neural network sliding mode control based on multi-kernel LS-SVM packet dropout online compensation.

In view of the strong nonlinear characteristics of the multi-packet transmission Aero-engine DCS with induced delay and random packet dropout, a neural network PID approach law sliding-mode controller using sliding window strategy and multi-kernel LS-SVM packet dropout online compensation is proposed. Firstly, the time-delay term in the system model is transformed equivalently, to establish the discrete system model of multi-packet transmission without time-delay; furthermore, the construction of multi-kernel function is transformed into kernel function coefficient optimization, and the optimization problem can be solved by the chaos adaptive artificial fish swarm algorithm, then the online predictive compensation will be made for data packet dropout of multi-packet transmission through the sliding window multi-kernel LS-SVM. After that, a sliding-mode controller design method of proportional integral differential approach law based on neural network is proposed. And online adjustment of PID approach law parameters can be achieved by nonlinear mapping of neural network. Finally, Truetime is used to simulate the method. The results shows that when the packet dropout rate is 30% and 60%, the average error of packet dropout prediction of multi-kernel LS-SVM reduces 29.21% and 44.66% compared with that of combined kernel LS-SVM, and the chattering amplitude of the proposed neural network PID approach law sliding-mode controller is decreased compared with other five approach law methods respectively. This controller can ensure a fast response speed, which shows that this method can achieve a better tracking control of the aeroengine network control system.

New stability results for delayed neural networks with data packet dropouts

This paper further investigates the stability analysis of delayed neural networks (DNNs) with data packet dropouts. Firstly, the delay-product-type function method is introduced to construct a suitable Lyapunov–Krasovskii functional (LKF) with delay-dependent matrices, which fully considers the integral terms, non-integral terms and time-delay correlation terms. Then, by applying free-matrix-base inequality (FMBI) and other valid inequalities mathematical analysis techniques, new stability criteria are established. Meanwhile, by solving a set of linear matrix inequalities (LMIs), the corresponding controllers are designed to ensure the system state stabilization. Finally, two examples are given to demonstrate the validity and feasibility of the proposed method.

Resilient state estimation for time‐varying uncertain dynamical networks with data packet dropouts and switching topology: an event‐triggered method

In this study, the recursive state estimation method based on the event-triggered protocol is given for time-varying uncertain complex networks with switching topology (ST), data packet dropouts (DPDs), and randomly occurring non-linearities (RONs). The event-triggered strategy is given to regulate the communications and then save the limited network resources when the measurement information is transmitted. The phenomena of RONs, ST, and DPDs are depicted by some random variables obeying the Bernoulli distribution. Besides, the estimator gain perturbations are dealt with. The major aim of this study is to present a new state estimation algorithm for addressed time-varying uncertain complex networks such that, for all STs, DPDs, and RONs, a minimal upper bound of state estimation error covariance matrix is derived by designing an optimal estimator gain matrix. Finally, a numerical simulation is presented to show the effectiveness of the provided time-varying state estimation method.

H ∞ Control of Networked Control System With Data Packet Dropout via Observer-Based Controller

The H ∞ control problem for networked control system with data packet dropout in both S-C link and C-A link via observer-based controller is researched in this paper. Firstly, the networked control system with data packet dropout in both S-C link and C-A link is modeled as a discrete Markov jump linear system. Secondly, the sufficient and necessary conditions on the stochastic stability of the closed-loop system is established, the solution method of the controller and observer gain matrix are also given on condition that the transition probability matrix is completely known and partly unknown, respectively. The minimal disturbance suppression performance index is also obtained. Finally, two simulation examples illustrate the validity of the proposed method.

Quantized control for nonhomogeneous Markovian jump T-S fuzzy systems with missing measurements

In this paper, in terms of the T-S fuzzy technique, the quantization control designs are resolved for a class of nonhomogeneous Markov jump systems (MJSs) with partially unknown transition probabilities. Different from the previous research, the transition probabilities are time-variant and not known exactly in the MJSs. Particularly in a network environment, it is considered that the effects of data packet dropouts and the occurrence of signal quantization simultaneously emerge in the closed-loop circuit. Furthermore, based on a fuzzy Lyapunov function and a set of linear matrix inequalities, one can achieve the desired H∞ performance and the sufficient conditions such that the corresponding closed-loop system is stochastically stable. By the cone complementarity linearisation (CCL) procedure, a sequential minimization problem is tackled efficiently to gain the solutions of the dynamic output feedback controller (DOFC). Finally, the validity of the suggested technique is showed via a simulation example.

Predictive control for networked switch flight system with packet dropout

In this study, a predictive control method is investigated for networked flight control (NFCS) system with data packet loss and unmeasurable states. By introducing state observer, the unmeasurable states of a target system and a plant system can be estimated. Then, based on networked predictive control strategy, the networked flight control system with data packet dropout is transformed into a switched system. The lost packets are compensated by a predictive scheme, which allows the plant system to track the target in NFCS. Furthermore, the robust H∞ tracking controller is proposed to track the target system via an average dwell time method. Experimental results demonstrate the effectiveness of the designed control strategies.