Random Sensor Delays Research Articles

Hybrid-attack-resistant distributed state estimation for nonlinear complex networks with random coupling strength and sensor delays

In this paper, a recursive distributed hybrid-attack-resistant state estimation (SE) scheme is proposed for a class of time-varying nonlinear complex networks (NCNs) subject to random coupling strength (RCS) and random sensor delays (RSDs) under hybrid attacks. A hybrid-attack model is considered to characterize the random occurrence of denial-of-service (DoS) attacks and deception attacks. The objective of the problem to be solved is to develop a recursive distributed estimation method such that, in the presence of RCS, RSDs and hybrid attacks, a locally optimized upper bound (UB) on the estimation error covariance (EEC) is ensured. By employing the mathematical induction method, a UB is firstly derived on the EEC. Subsequently, the obtained UB is minimized by appropriately designing the estimator gain (EG). Furthermore, a sufficient criterion guaranteeing the exponential boundedness (EB) of SE error is elaborated in the mean square sense (MSS). Finally, simulation experiments with localization applications of multiple mobile indoor robots are conducted to illustrate the applicability of the proposed SE scheme.

Distributed Resilient Fusion Filtering for Nonlinear Systems with Random Sensor Delays: Optimized Algorithm Design and Boundedness Analysis

Distributed Resilient Fusion Filtering for Nonlinear Systems with Random Sensor Delays: Optimized Algorithm Design and Boundedness Analysis

Event‐based optimal filter for a networked system with multiplicative and auto/cross‐correlated process and measurement noise

SummaryThis article presents an event triggered based optimal filter for a networked system with multiplicative noise in state and measurement matrices. The process and measurement noise are auto/cross‐correlated, which is described as a moving average process. The network in both sensor to estimator (S‐E) and controller to actuator (C‐A) channel is affected by random sensor delay and packet dropouts. These effects are modeled using two random variables for both the S‐E and C‐A channels. Sensor data are transmitted to the estimator through the S‐E channel using an event‐based mechanism. It depends on a time varying threshold to improve trade‐off between tracking performance and utilization of network bandwidth. The process and measurement noise are two‐step auto/cross‐correlated. A comprehensive augmented stochastic model is developed for the networked system by combining the effects of event triggering, network constraints and noise correlation. The filter is designed using an innovation analysis approach based on orthogonal projection. The solution of the filter is given in terms of a Lyapunov and a Riccati equation. An algorithm is presented for the implementation of developed filter. The stability properties of the proposed optimal filter are analyzed. The effectiveness of the proposed filter is shown in the simulation platform and accumulated mean square error performance is evaluated based on Monte Carlo simulation.

Partial-Nodes-Based State Estimation for Stochastic Coupled Complex Networks with Random Sensor Delay: An Event-Triggered Communication Method

Partial-Nodes-Based State Estimation for Stochastic Coupled Complex Networks with Random Sensor Delay: An Event-Triggered Communication Method

Distributed resilient fusion filtering for nonlinear systems with random sensor delay under round-robin protocol

In this paper, we address the distributed resilient fusion filtering (DRFF) problem for a class of nonlinear multi-sensor networked systems (MSNSs) with random sensor delay (RSD) under round-robin protocol (RRP). The RSD is depicted by the Bernoulli random variable with known occurrence probability. In order to relieve the network congestion, the RRP that can deal with information overload issue of the transmission process from sensor to the estimator is utilised. The major objective of this paper is that the resilient fusion filter is designed for nonlinear MSNSs with RSD and RRP in the light of the inverse covariance intersection approach, where the local upper bound regarding the filtering error covariance is obtained and then minimised by suitably exploiting the local filter gain. Finally, a simulation example that can show the validity of the provided DRFF algorithm is presented.

Robust integrated covariance intersection fusion Kalman estimators for networked mixed uncertain time-varying systems

Abstract For the multisensor time-varying networked mixed uncertain systems with random one-step sensor delays and uncertain-variance multiplicative and linearly dependent additive white noises, a new augmented state method with fictitious noises is presented, by which the original system is transformed into a standard system without delays and with uncertain-variance fictitious white noises. According to the minimax robust estimation principle and the Kalman filtering theory, based on the worst-case system with the conservative upper bounds of uncertain noise variances, the local and integrated covariance intersection (ICI) fused robust time-varying Kalman estimators (filter, predictor and smoother) are presented respectively in the sense that their actual estimation error variances are guaranteed to have the corresponding minimal upper bounds for all admissible uncertainties. Their robustness is proved by the extended Lyapunov equation method, and their accuracy relations are compared based on the traces of the variance matrices and the covariance ellipsoids, respectively. Specially, a universal ICI fusion robust Kalman filtering method of integrating the local robust estimators and their conservative cross-covariances is presented. It overcomes the drawbacks of the original covariance intersection (CI) fusion method and improves robust accuracy of the original CI fuser. A simulation example applied to two-mass spring system shows the effectiveness of the proposed methods and results.

Non-fragile dissipative filtering of cyber–physical systems with random sensor delays

This paper considers the problem of non-fragile state estimation under dissipative constraint for a class of nonlinear cyber–physical systems (CPSs) with sensor delays. The dynamics of the considered CPSs is characterized by the well-known T–S fuzzy model and system measurements are valued by wireless sensors. The communication link between the filter and the plant is described by a relatively practical model and sensor delays occurred in signal transmissions are taken into consideration. A stochastic variable which yields the standard Bernoulli distribution is exploited to model sensor delays encountered by the sensor measurements. With the help of a basis-dependent Lyapunov function and predefined performance constraint, sufficient conditions are then developed to establish the stochastic stability as well as strict dissipativity for the resultant filtering error system. The existence of the corresponding filter is guaranteed and the expression of desired filter parameters are shown explicitly. In the end, the established theoretical results are validated by a tunnel diode circuit example and corresponding simulations are also provided.

Robust Finite-Horizon Filtering for 2-D Systems With Randomly Varying Sensor Delays

This paper is concerned with the robust finite-horizon filter design problem for a class of two-dimensional (2-D) time-varying systems with norm-bounded parameter uncertainties and incomplete measurements. The incomplete measurements cover randomly occurring sensor delays and missing measurements that are presented in a unified form by resorting to a stochastic Kronecker delta function. The occurrences of the sensor delays and missing measurements are governed by stochastic variables with known probability distributions. The main aim of the addressed problem is to design a recursive filter with appropriate gain parameters that ensure the local minimum of certain upper bound on the estimation error variance at each time instant. With the aid of the inductive approach and the 2-D Riccati-like difference equations, one of the first few attempts is made to tackle the robust filter design problem for 2-D uncertain systems with random sensor delays over a finite horizon. Sufficient conditions are provided for the existence of an upper bound on the estimation error variance, an algorithm is then developed to derive such an upper bound, and finally the desired filter is designed to minimize the obtained upper bound. The filter design procedure is of a recursive form that facilitates the online calculation. A numerical simulation is carried out to show the effectiveness of the developed filtering scheme.

Robust Weighted Fusion Kalman Estimators for Networked Multisensor Mixed Uncertain Systems With Random One-Step Sensor Delays, Uncertain-Variance Multiplicative, and Additive White Noises

For networked multisensor systems with mixed uncertainties, including random one-step sensor delays, multiplicative noises and uncertain noise variances, a new augmented state approach with the fictitious noises is presented, by which the original system model is transformed into one without sensor delays and only with white noises. An extended Lyapunov equation approach with two Lyapunov equations is presented, which is applied to prove the robustness of the estimators. According to the minimax robust estimation principle, based on the worst-case system with conservative upper bounds of uncertain noise variances, the four weighted fusion robust time-varying and steady-state Kalman estimators (predictor, filter and smoother) are presented in the sense that their actual estimation error variances are guaranteed to have the corresponding minimal upper bounds for all admissible uncertainties. Their robustness and accuracy relations are proved, and their convergence in a realization is proved by the dynamic error system analysis (DESA) method. Specially, the presented modified robust covariance intersection (CI) fuser has higher robust accuracy than the original one. A simulation example applied to the uninterruptible power system (UPS) shows the effectiveness of the proposed approaches and results.

On state estimation for nonlinear dynamical networks with random sensor delays and coupling strength under event-based communication mechanism

This paper is concerned with the optimized state estimation problem for nonlinear dynamical networks subject to random coupling strength (RCS) and random sensor delays (RSDs) under the event-triggered communication criterion. Firstly, a set of random variables obeying uniform distribution over certain interval is introduced to reflect the stochasticity of the elements of coupling configuration. Furthermore, a series of Bernoulli distributed random variables is introduced to characterize the phenomenon of RSDs, where the inaccuracy of the occurrence probability is depicted. Besides, as a special communication protocol, the event-triggered communication scheme is employed to avoid the potential data collision. Subsequently, a new robust state estimation algorithm is provided for addressed nonlinear dynamical networks by fully taking the available information of RCS and RSDs into account. Moreover, a sufficient condition ensuring the boundedness of state estimation error is presented. Finally, some simulations are utilized to demonstrate the usefulness of the optimized estimation technique proposed in this paper.

Observer-based [formula omitted] control design for singular switching semi-Markovian jump systems with random sensor delays

This paper studies the problem of H∞ control design for a class of singular switching semi-Markovian jump systems, in which the transition rates of jumping process are uncertain and the output measurements suffer from random sensor delays. Firstly, a Luenberger observer is designed to estimate the system state components, based on which an error dynamic is obtained. Then, stochastic stability analysis for the overall closed-loop system is given based on the upper and lower bounds of transition rates. Further, the state feedback controller gain matrices are given by solving a set of sufficient conditions in terms of strict linear matrix inequalities (LMIs), which also guarantee the closed-loop system to be stochastically stabilizable and has a prescribed H∞ performance index γ. Finally, a numerical example is provided to verify the effectiveness of the obtained result.

Optimized State Estimation of Uncertain Linear Time-Varying Complex Networks With Random Sensor Delay Subject to Uncertain Probabilities

This paper is concerned with the optimal state estimation problem for a class of time-varying uncertain dynamical networks with mixed time-delay under uncertain probabilities. Here, the mixed time-delays include the constant time-delay and the random sensor delay, where the random sensor delay is depicted by a Bernoulli distributed random variable and the occurrence probability of the random sensor delay can be uncertain. The major novelty of the paper lies in that a new time-varying state estimation algorithm is given such that, for all parameter uncertainties, mixed time-delays and uncertain probabilities, a locally optimal upper bound of the estimation error covariance is obtained and the desirable estimator parameter of easy-to-implement feature is designed. Moreover, the performance evaluation problem of the presented estimation algorithm is solved, where the monotonicity analysis is shown regarding the trace of the upper bound and the deterministic occurrence probability of random sensor delay. At last, the simulations are given to show the validity and correctness of the proposed time-varying estimation method. In particular, the comparisons are given to show the relationship of the upper bound and occurrence probability.

Fault Detection for Linear Discrete Time-Varying Systems Subject to Random Sensor Delay: A Riccati Equation Approach

This paper mainly studies the fault detection (FD) problem for linear discrete time-varying systems subject to random sensor delay. By assuming that the measurement channel is with Transmission Control Protocol (TCP), an observer-based FD filter (FDF) is provided as a residual generator via embedding the packet indicator into the filter. To construct this FDF, its design issue is formulated into two sub-problems. One is to maximize the $\mathcal {H}_{-}/\mathcal {H}_{\infty }$ or $\mathcal {H}_{\infty }/\mathcal {H}_{\infty }$ FD performance index, which aims to enhance the ratio of fault sensitivity/disturbance attenuation. The other one is to find the filter parameter matrices such that the error between the residual and the fault is minimized in the $\mathcal {H}_{\infty }$ sense. By employing stochastic analysis and introducing some adjoint operator based optimization approaches, analytical solutions to the aforementioned FDF design problem are derived via solving recursive Riccati equations. An illustrative example is given to show the effectiveness of the proposed methodologies.

Non-fragile finite-time [formula omitted] state estimation for discrete-time neural networks with semi-Markovian switching and random sensor delays based on Abel lemma approach

This paper focuses the non-fragile state estimation problem for a class of discrete-time neural networks with semi-Markov switching and unreliable communication links in finite time l2−l∞ sense that are caused due to the randomly occurring sensor nonlinearity, randomly occurring time delays and packet dropouts. By employing semi-Markovian switching with time-varying transition rates, a broader class of dynamical systems than the traditional Markovian jump linear systems is described. Then, based on the Abel lemma approach on finite sum inequalities, a non-fragile state estimator is obtained to ensure that the resulting error system is mean-square stochastically finite-time stable with a prescribed l2−l∞ performance. Sufficient conditions for the gain of the state estimator are obtained through solving a set of linear matrix inequalities. Finally a numerical example is provided to substantiate the theoretical results.

Particle filtering for networked nonlinear systems subject to random one-step sensor delay and missing measurements

Abstract In this paper, the particle filtering problem is investigated for a class of networked nonlinear systems with random one-step sensor delay and missing measurements. The phenomena of missing measurements and random one-step sensor delay are modeled by two random variables, both obeying the Bernoulli distribution. Here, we derive an explicit expression for the likelihood function when the possible occurrence of one-step sensor delay and measurement loss is taken into consideration. Based on this likelihood function, we propose a novel particle filtering algorithm to treat the nonlinear estimation problem in the simultaneous presence of random sensor delay and measurement loss. Finally, a simulation example is given to illustrate the feasibility and advantages of the proposed filtering scheme compared with traditional particle filtering algorithm.

Observer-based sliding mode control of Markov jump systems with random sensor delays and partly unknown transition rates

ABSTRACTIn this paper, the sliding mode control problem of Markov jump systems (MJSs) with unmeasured state, partly unknown transition rates and random sensor delays is probed. In the practical engineering control, the exact information of transition rates is hard to obtain and the measurement channel is supposed to subject to random sensor delay. Design a Luenberger observer to estimate the unmeasured system state, and an integral sliding mode surface is constructed to ensure the exponential stability of MJSs. A sliding mode controller based on estimator is proposed to drive the system state onto the sliding mode surface and render the sliding mode dynamics exponentially mean-square stable with H∞ performance index. Finally, simulation results are provided to illustrate the effectiveness of the proposed results.

Decentralized adaptive fuzzy control for nonlinear large-scale systems with random packet dropouts, sensor delays and nonlinearities

This paper investigates the adaptive fuzzy decentralized control problem for a class of uncertain nonlinear large-scale systems. Different from the conventional backstepping-based adaptive output feedback control technique, the output measurements are imperfect and considered to suffer random packet dropouts (RPDs), random sensor delays (RSDs) and random sensor nonlinearities (RSNs), which result typically from a network environment such as sensor networks. A novel sensor model is established for describing the random phenomena within a unified representation by introducing a multi-Markovian variable. Based on this sensor model, two main difficulties arise: first is that the system output cannot be used directly for controller design due to the randomly occurring phenomena; second is how to deal with the complex nonlinear stochastic terms with unknown interconnections, unknown time-varying delays and unknown sensor nonlinearities entangled together. With the help of new coordinate transformations and mean value theorem, by using appropriate Lyapunov–Krasovskii functionals, a new adaptive decentralized memoryless output feedback controller is designed. It is proved that the constructed controller ensures the boundedness in probability of all the closed-loop signals in presence of RPDs, RSDs and RSNs. The simulation results are presented to show the effectiveness of the proposed scheme.

Robust extended Kalman filtering for nonlinear stochastic systems with random sensor delays, packet dropouts and correlated noises

In this paper, the robust filtering problem is investigated for nonlinear stochastic systems with random sensor delays, packet dropouts and correlated noises. The stochastic multiplicative noises which enter into both state equation and measurement equation are modeled as random variables with bounded variance, and a Bernoulli distributed random sequence is introduced to describe the random delays and packet dropouts. Then, the system is converted to the stochastic parameterized one through introducing a group of new variables. Moreover, the two-step prediction framework is employed to achieve the goal of noise decoupling. The objective of the addressed estimation problem is to design a filter, such that in the presence of random delays, packet dropouts, multiplicative noises and correlated noises, the upper bounds for the prediction error and estimation error covariance can be guaranteed. Subsequently, the upper bounds are minimized by designing the optimal prediction gain and filter gain. Finally, the attitude estimation example is used to demonstrate the effectiveness of the proposed robust extended Kalman filter.

On sliding mode control for networked control systems with semi-Markovian switching and random sensor delays

This paper focuses on the problem of sliding mode control for networked control systems with semi-Markovian switching and random measurement, where the measurement channel is assumed to suffer from random sensor delays. A Luenberger observer is designed to generate the estimation of system states, and the integral sliding surface is constructed to guarantee exponential stability of networked semi-Markovian switching systems in the mean square sense. Then a proper controller is synthesized to ensure that the trajectory of the closed-loop networked semi-Markovian switching systems can be driven onto the prescribed sliding surface. Finally, numerical examples and simulations are provided to illustrate the effectiveness of the integral sliding mode control scheme.

Optimal filtering for systems with finite-step autocorrelated process noises, random one-step sensor delay and missing measurements

The optimal filtering problem is investigated for a class of discrete stochastic systems with finite-step autocorrelated process noises, random one-step sensor delay and missing measurements. The random disturbances existing in the system are characterized by the multiplicative noises and the phenomena of sensor delay and missing measurements occur in a random way. The random sensor delay and missing measurements are described by two Bernoulli distributed random variables with known conditional probabilities. By using the state augmentation approach, the original system is converted into a new discrete system where the random one-step sensor delay and missing measurements exist in the sensor output. The new process noises and observation noises consist of the original stochastic terms, and the process noises are still autocorrelated. Then, based on the minimum mean square error (MMSE) principle, a new linear optimal filter is designed such that, for the finite-step autocorrelated process noises, random one-step sensor delay and missing measurements, the estimation error is minimized. By solving the recursive matrix equation, the filter gain is designed. Finally, a simulation example is given to illustrate the feasibility and effectiveness of the proposed filtering scheme.