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In this article, we consider the $H_{\infty }$ proportional-integral (PI) state estimation (SE) problem for discrete-time T–S fuzzy systems subject to transmission delays, external disturbances, and redundant channels. Multiple redundant communication channels are utilized between the sensors and the remote estimator to enhance the reliability of data transmissions. In order to characterize the transmission delays in network-based communication, a family of random variables with partly known probabilities, which are independent and identically distributed, is adopted to describe the random behavior of the transmission delays with the redundant channels. The objective of this work is to put forward a PI state estimator such that the dynamics of the estimation error is exponentially mean-square stable and satisfies the prescribed $H_{\infty }$ performance index of the disturbance attenuation/rejection. By employing the stochastic analysis approach, the error dynamics of the SE under the proposed state estimator is analyzed and sufficient conditions are obtained to ensure the existence of the required PI state estimator. Furthermore, the desired estimator parameters are derived by solving a nonlinear optimization problem. Finally, two simulation examples are exploited to demonstrate the validity of the proposed SE scheme. [ABSTRACT FROM AUTHOR]

The full information estimation (FIE) problem is addressed for discrete time-varying systems (TVSs) subject to the effects of a round-Robin (RR) protocol. A shared communication network is adopted for data transmissions between sensor nodes and the state estimator. In order to avoid data collisions in signal transmission, only one sensor node could have access to the network and communicate with the state estimator per time instant. The so-called RR protocol, which is also known as the token ring protocol, is employed to orchestrate the access sequence of sensor nodes, under which the chosen sensor node communicating with the state estimator could be modeled by a periodic function. A novel recursive FIE scheme is developed by defining a modified cost function and using a so-called “backward-propagation-constraints.” The modified cost function represents a special global estimation performance. The solution of the proposed FIE scheme is achieved by solving a minimization problem. Then, the recursive manner of such a solution is studied for the purpose of online applications. For the purpose of ensuring the estimation performance, sufficient conditions are obtained to derive the upper bound of the norm of the state estimation error (SEE). Finally, two illustrative examples are proposed to demonstrate the effectiveness of the developed estimation algorithm. [ABSTRACT FROM AUTHOR]

The finite-time state estimation issue is addressed in this paper for discrete time-delayed neural networks (NNs). More than one communication channel is utilized to improve the communication performance. The transmission delays of each channel are modeled by a family of stochastic variables which are independent and identically distributed. The main purpose of this paper is to construct an appropriate state estimation scheme under which the corresponding state estimation error dynamics is finite-time bounded in the mean square. By employing the stochastic analysis approach and introducing a special Lyapunov-like functional, we have developed certain sufficient conditions to achieve the prescribed estimation performance. Furthermore, the exact expressions of the achieved estimator parameters are given by solving a special minimization problem subject to certain inequality constraints. Finally, we propose an illustrative simulation to examine the correctness, as well as the effectiveness, of our proposed state estimation method. [ABSTRACT FROM AUTHOR]

This paper is concerned with the Tobit Kalman filtering problem for a class of discrete time-varying systems subject to censored observations, integral measurements and probabilistic sensor failures under the Round-Robin protocol (RRP). The censored observations are characterized by the Tobit observation model, the integral measurements are described as functions of system states over a certain time interval required for data acquisition, and the sensor failures are governed by a set of uncorrelated random variables. The RRP is employed to decide the transmission sequence of sensors in order to alleviate undesirable data collisions. By resorting to the augmentation technique and the orthogonality projection principle, a protocol-based Tobit Kalman filter (TKF) is developed with the coexistence of integral measurements and sensor failures that lead to a couple of augmentation-induced terms. Moreover, the performance of the proposed filter is analyzed through examining the statistical property of the error covariance of the state estimation. Further analysis shows the existence of self-propagating upper and lower bounds on the estimation error covariance. A case study on ballistic roll rate estimation is presented to illustrate the efficacy of the developed filter. [ABSTRACT FROM AUTHOR]

• A bibliographical review is provided on the moving horizon estimation problem. • The basic idea of moving horizon estimation is introduced in detail. • The recent advances of moving horizon estimation is summarized. • The applications of moving horizon estimation are presented. • The future research challenges of moving horizon estimation are outlined. Since the proposal of moving horizon (MH) estimation in 1960s, the MH estimation approach has drawn ever-increasing research interests due mainly to its inherent capability of handling complex nonlinear systems and constrained systems. Recent years have witnessed considerable progress on the theoretical and practical research of MH estimation. In this work, a bibliographical review is provided on the moving horizon estimation problem and its applications. The basic idea of MH estimation is first introduced in detail. Then recent advances of MH estimation according to the underlying systems are summarized. Furthermore, some applications of MH estimation are presented. Finally, some research challenges of MH estimation problem are outlined for the further research. [ABSTRACT FROM AUTHOR]

Train positioning is of critical importance for communication-based train control (CBTC) systems. The objective of this paper is to provide an algorithm to generate the precise estimates of the train position and velocity for CBTC systems with carrier-sense multiple access (CSMA) protocol scheduling, thereby improving the accuracy of train positioning as well as the availability of CBTC systems. First, the dynamics of a train with $N$ cars linked by couplers is described based on Newton’s motion equations. Then, the transmission model reflecting the behaviors of $p$ -persistent CSMA protocol is presented by using a Bernoulli distributed sequence whose probability distribution is dependent on the number of trains sharing with one communication channel [i.e., $N(k)$ ]. Furthermore, the value of $N(k)$ is assumed to be unknown but bounded by two known positive integers. The purpose of the problem addressed is to design an estimator, such that the estimation error is exponentially ultimately bounded (with a certain asymptotic upper bound) in mean square subject to the external resistive force. By utilizing the stochastic analysis approach, sufficient conditions are established to guarantee the ultimate boundedness of the estimation error in mean square. For the purpose of designing the desired estimator gains under different requirements (e.g., smallest ultimate bound and fastest decay rate), two optimization problems are solved in terms of linear matrix inequalities. Finally, a simulation example is given to illustrate the effectiveness of the estimator design scheme. [ABSTRACT FROM AUTHOR]

This paper is concerned with the event-triggered state estimation problem for time-varying delayed complex networks with stochastic coupling and missing measurements under uncertain occurrence probabilities. The stochastic coupling and missing measurements are modeled by two set of mutually independent Bernoulli random variables, respectively, where the uncertainties of the occurrence probabilities are characterized. In addition, the event-triggered mechanism is employed to reduce the network burden during the data transmissions. The aim of the paper is to propose a robust state estimation method for addressed dynamics networks such that sufficient conditions are obtained to ensure the existence of an optimized upper bound of the estimation error covariance. Moreover, the monotonicity analysis between the trace of obtained upper bound of the estimation error covariance and the deterministic occurrence probability of the missing measurements is conducted. Finally, a numerical example is used to verify the validity of the proposed robust state estimation strategy. [ABSTRACT FROM AUTHOR]

In this study, the fault detection (FD) problem is investigated for a class of networked multi‐rate systems (NMSs) with network‐induced fading channels and randomly occurring faults. The stochastic characteristics of the fading measurements are governed by mutually independent random channel coefficients over the known interval [0,1]. By applying the lifting technique, the system model for the observer‐based FD is established. With the aid of the stochastic analysis approach, sufficient conditions are established under which the stochastic stability of the error dynamics for the state estimation is guaranteed and the prescribed H∞ performance constraint on the error dynamics for the fault estimation is achieved. Based on the established conditions, the addressed FD problem of NMSs is recast as a convex optimisation one that can be solved via the semi‐definite program method, and the explicit expression of the desired FD filter is derived by means of the feasibility of certain matrix inequalities. The main results are specialised to the networked single‐rate systems that are a special case of the NMSs. Finally, two simulation examples are utilised to illustrate the effectiveness of the proposed FD method. [ABSTRACT FROM AUTHOR]

In this paper, the event-triggered state estimation problem is investigated for a class of complex networks with mixed time delays using sampled data information. A novel state estimator is presented to estimate the network states. A new event-triggered transmission scheme is proposed to reduce unnecessary network traffic between the sensors and the estimator, where the sampled data is transmitted to the estimator only when the so-called “event-triggered condition” is satisfied. The purpose of the problem addressed is to design an estimator for the complex network such that the estimation error is ultimately bounded in mean square. By utilizing Lyapunov theory combined with the stochastic analysis approach, sufficient conditions are established to guarantee the ultimate boundedness of the estimation error in mean square. Then, the desired estimator gain matrices are obtained via solving a convex problem. Finally, a numerical example is given to illustrate the effectiveness of the results. [ABSTRACT FROM PUBLISHER]