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This article is concerned with the non‐fragile fault estimation issue for a class of multi‐rate systems under binary encoding schemes (BES). A specific dynamics relationship with zero second‐order difference is employed to characterize the evolution of the actuator fault. The signal communication is regulated by the BESs, where the resulting random bit errors are characterized by a certain stochastic noise. The aim of this article is to design a fault estimator such that the estimation error dynamics is exponentially ultimately bounded in mean square. First, an equivalent single‐rate system is formulated via the lifting technique. Then, by virtue of the stochastic analysis method and the matrix inequality approach, sufficient conditions are developed to ensure the existence of the desired fault estimator. Finally, the effectiveness of the proposed estimator design strategy is testified through a simulation example. [ABSTRACT FROM AUTHOR]

In this paper, the proportional–integral-type estimator design problem is studied for recurrent neural networks under the encoding–decoding communication mechanism. In the process of the measurement data transmission, an encoding–decoding mechanism is introduced to improve the security of the network by encrypting the measurement data. The purpose of this paper is to design a proportional–integral-type estimation algorithm such that the estimation error dynamics is exponentially ultimately bounded in mean square. First, a sufficient condition is obtained for the existence of the desired estimator. Then, the parameters of the estimator are obtained by solving certain matrix inequality. Finally, a simulation example is given to verify the effectiveness of the designed estimation algorithm. [ABSTRACT FROM AUTHOR]

In this paper, the observer-based proportional-integral-derivative (PID) control problem is investigated for a class of actuator-saturated systems affected by multiplicative noises under the binary encoding scheme (BES). The communication between the sensors and the observers is implemented via a shared network, in which the measurement signals are transmitted by the BES. During the transmission, random bit errors may occur in the bit string due to the channel noises. The aim of this paper is to design an observer-based PID controller such that the closed-loop system is exponentially mean-square ultimately bounded with the consideration of random bit errors, actuator saturations and multiplicative noises. Sufficient conditions are presented to guarantee the expected performance of the resulted tracking error, and the controller gain matrices are obtained by solving a set of matrix inequalities. Finally, the effectiveness of the proposed PID control scheme is verified by a simulation example. [ABSTRACT FROM AUTHOR]

In this article, a resilient set-membership approach is put forward to deal with the state estimation problem for a sort of discrete-time memristive neural networks (DMNNs) with hybrid time delays under the weighted try-once-discard protocol (WTODP). The WTODP is utilized to mitigate unnecessary network congestion occurring in the channel between DMNNs and the state estimator. In order to ensure resilience against possible realization errors, the estimator gain is permitted to undergo some norm-bounded parameter drifts. Our objective is to design a resilient set-membership estimator (RSME) that is capable of resisting gain variations and unknown-but-bounded noises by confining the estimation error to certain ellipsoidal regions. By resorting to the recursive matrix inequality technique, sufficient conditions are acquired for the existence of the expected RSME and, subsequently, an optimization problem is formalized by minimizing the constraint ellipsoid (with respect to the estimation error) under WTODP. Finally, numerical simulation is carried out to validate the usefulness of RSME. [ABSTRACT FROM AUTHOR]

This paper deals with the scalable distributed ${H} _{{\infty }}$ -consensus filtering problem for a class of discrete time-varying systems subject to multiplicative noises and censored measurements over sensor networks (SNs). For the underlying SN, it is assumed that only the measurement outputs from partial sensor nodes are available. Also, the phenomenon of censored measurements is taken into account to reflect the limited capability in measuring. A new ${H} _{{\infty }}$ -consensus performance index is put forward to evaluate the disturbance rejection level of the filters against the simultaneous presence of external disturbances, initial conditions, as well as censoring effects. By utilizing the vector dissipativity theory and the recursive matrix inequality technique, sufficient conditions are established under which the prescribed ${H} _{{\infty }}$ -consensus performance index is achieved. The parameters of the desired distributed filters are calculated via solving certain matrix inequalities, where such a calculation is conducted in a local sense so as to preserve the scalability of the filter design. Finally, a numerical simulation example is provided to demonstrate the validity and applicability of the proposed filtering strategy. [ABSTRACT FROM AUTHOR]

Summary: This article is concerned with the moving horizon estimation (MHE) problem for a class of linear discrete‐time systems subject to multirate measurements and correlated noises. The sensors of the plant are grouped into two nodes with different sampling periods, which give rise to the multirate measurements. Under the effects of multirate measurements, the underlying system is then described by a periodic system with correlated noises. A MHE strategy is formulated and the corresponding analytical solution of the state estimation is derived by using the "completing the square" technique. Based on the lifting approach, the dynamics of the estimation error covariance is analyzed and the desired estimator parameters are obtained by solving a set of linear matrix inequalities. An illustrative example is given to show the effectiveness of our proposed MHE method. [ABSTRACT FROM AUTHOR]

The distributed state estimation problem is examined for a kind of nonlinear time-varying stochastic systems through sensor networks (SNs) with randomly switching topologies as well as redundant channels. The random switches of the topologies for SNs are governed by Markovian jumping parameters and the redundant channels are introduced to help improve the capability of the network communication. We are interested in designing distributed state estimators so that the estimation error dynamics is confirmed to reach a prescribed level of average ${H}_{\infty }$ performance in terms of a finite horizon. Through intensive stochastic analysis, we acquire some sufficient conditions that guarantee the existence of the expected state estimators whose gain parameters are obtained recursively by means of the solution to a series of matrix inequalities. A numerical simulation is carried out to illustrate the validity of the developed state estimation algorithm. [ABSTRACT FROM AUTHOR]

This paper addresses the distributed resilient filtering problem for a class of power systems subject to denial-of-service (DoS) attacks. A novel distributed filter is first constructed to practically reflect the impact from both cyber-attacks and gain perturbations. For all possible occurrence of DoS attacks and gain perturbations, an upper bound of filtering error covariance is derived by resorting to some typical matrix inequalities. Furthermore, the desired filter gain relying on the solution of two Riccati-like difference equations is obtained with the help of the gradient-based approach and the mathematical induction. The developed algorithm with a recursive form is independent of the global information and thus satisfies the requirements of scalability and distributed implementation online. Finally, a benchmark simulation test is exploited to check the usefulness of the designed filter. [ABSTRACT FROM AUTHOR]

This paper focuses on the filtering problem for nonlinear systems over sensor networks whose topologies are changeable subject to Round-Robin (RR) protocol in the finite horizon case. The phenomenon of switching topologies for sensor networks is considered due to the weak connections between the node and its neighbors. The Round-Robin communication strategy is employed to save the limited bandwidth and reduce the network resource consumption. It is our target to design distributed filters so that the dynamics of augmented error system satisfies the given level of average H ∞ performance. We get the sufficient conditions in order to guarantee the existence of the desired distributed filters whose parameter matrices would be attained by a recursive method in terms of a set of matrix inequalities. In the end, a numerical simulation is exploited to describe the validity of the raised filter design technique. [ABSTRACT FROM AUTHOR]

This paper is concerned with the distributed \cal H\infty filtering problem for a class of discrete-time Markovian jump nonlinear time-delay systems with deficient statistics of mode transitions. The system measurements are collected through a lossy sensor network subject to randomly occurring quantization errors and randomly occurring packet dropouts. The description of deficient statistics of mode transitions that account for known, unknown, and uncertain transition probabilities is comprehensive. A distributed filter design scheme is outlined by explicitly characterizing the filter gains in terms of some matrix inequalities. Simulation results demonstrate the effectiveness of the proposed filtering scheme. [ABSTRACT FROM PUBLISHER]

This paper addresses the robust \cal H\infty filtering problem for a class of discrete time-varying Markovian jump systems with randomly occurring nonlinearities and sensor saturation. Two kinds of transition probability matrices for the Markovian process are considered, namely, the one with polytopic uncertainties and the one with partially unknown entries. The nonlinear disturbances are assumed to occur randomly according to stochastic variables satisfying the Bernoulli distributions. The main purpose of this paper is to design a robust filter, over a given finite-horizon, such that the \cal H\infty disturbance attenuation level is guaranteed for the time-varying Markovian jump systems in the presence of both the randomly occurring nonlinearities and the sensor saturation. Sufficient conditions are established for the existence of the desired filter satisfying the \cal H\infty performance constraint in terms of a set of recursive linear matrix inequalities. Simulation results demonstrate the effectiveness of the developed filter design scheme. [ABSTRACT FROM PUBLISHER]

Abstract: This paper investigates the problem of filtering for systems with repeated scalar nonlinearities under unreliable communication links. The nonlinear system is described by a discrete-time state equation containing a repeated scalar nonlinearity as in recurrent neural networks. The communication links, existing between the plant and filter, are assumed to be imperfect and a stochastic variable satisfying the Bernoulli random binary distribution is utilized to model the phenomenon of the measurements missing. Attention is focused on the analysis and design of stable full- and reduced-order filters with the same repeated scalar nonlinearities such that the filtering error system is stochastically stable and preserves a guaranteed performance. Sufficient conditions are obtained for the existence of admissible filters. Since these conditions involve matrix equalities, the cone complementarity linearization procedure is employed to cast the nonconvex feasibility problem into a sequential minimization problem subject to linear matrix inequalities, which can be readily solved by using standard numerical software. A numerical example is given to illustrate the effectiveness of the proposed design method. [Copyright &y& Elsevier]