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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]

This article is concerned with the extended dissipative fuzzy proportional–integral–derivative (PID) control problem for nonlinear systems subject to controller parameter perturbations over a class of mixed fading channels. The sensors of plant are divided into two groups according to engineering practice, where the individual sensor group transmits the measurements to the controller via a respective communication channel undergoing specific fading effects. Considering the complicated nature of the signal fading with the transmission channels, two stochastic models (i.e., the independent and identically distributed fading model and the Markov fading model) are simultaneously employed to describe the mixed fading effects of the two communication channels corresponding to the two sensor groups. The objective of this article is to design a nonfragile PID controller such that the closed-loop system is exponentially stable in mean square and extended stochastically dissipative. With the assistance of the Lyapunov stability theory and stochastic analysis method, sufficient conditions are obtained to analyze the system performance. Then, within the established theoretical framework, an iterative optimization algorithm is proposed to design the desired controller parameters by using the convex optimization technique. Finally, two simulation examples are given to verify the effectiveness of the proposed control schemes. [ABSTRACT FROM AUTHOR]

In this article, the distributed adaptive Kalman filtering is investigated for discrete-time stochastic nonlinear systems with gain perturbation as well as unknown covariance of process noises. For the adopted event-triggered communication scheduling, a distributed Kalman filter with an event timestamp is first constructed to effectively fuse the information from neighbors and itself while guaranteeing the unbiasedness. In light of stochastic analysis, the desired filter gain, achieving the suboptimality of filtering performance, is obtained recursively by solving two optimization issues with the form of Riccati-like difference equations. With the help of the fashionable weighted fusion conception combined with the well-known law of large numbers, a recursive estimation of process noise covariance is derived step by step and consequently suits for online computation. Finally, the effectiveness of the proposed filtering scheme is verified via a “lineland” system model. [ABSTRACT FROM AUTHOR]

This article is concerned with the scalable distributed H∞‐consensus filtering problem for a class of discrete time‐varying systems over sensor networks with the Round‐Robin protocol (RRP). The challenge comes from the fact that the time‐varying parameters of the network are subject to randomly occurring norm‐bounded uncertainties and the measurement outputs of the sensor nodes are saturated due to the sector nonlinearities. For preventing data collisions and saving energy, the RRP determines which neighboring node can access the shared network for information transmission at each time step. An H∞ performance index is proposed to characterize the disturbance attenuation level of the resulting filtering error dynamics. By stochastic analysis in combination with the recursive matrix inequality approach, a distributed filtering algorithm is developed for each individual sensor node to ensure the prespecified estimation performance. Finally, an illustrative simulation example is shown to verify the effectiveness and applicability of the theoretical results. [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]

In this paper, the passivity analysis problem is investigated for a class of discrete-time stochastic neural networks (DSNNs) with randomly occurring mixed time delays (ROMDs). The mixed delays comprise time-varying discrete delays, infinite-distributed delays as well as finite-distributed delays. A set of Bernoulli-distributed white sequences is used to account for the random nature of the occurrence of the mixed time delays. In addition, stochastic disturbances are taken into consideration to describe the state-dependent noises caused possibly by electronic devices and hardware implementation of neural networks. By using a combination of Lyapunov-Krasovskii functional, free-weighting matrix approach and stochastic analysis technique, we establish sufficient conditions guaranteeing the passivity performance of the underlying DSNNs. Furthermore, a delay-dependent robust passivity criterion is presented to deal with the parameter uncertainties in the DSNNs with ROMDs. A simulation example is provided to verify the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

This paper designs the event-triggered non-fragile state estimator for a class of time-delayed complex networks with randomly occurring sensor saturations (ROSSs) and estimator gain variations on the basis of measurements from partial nodes. Both the time-varying state delays and the stochastic disturbances are considered in the network model. A Bernoulli-distributed white sequence is utilized to reflect the phenomenon of ROSSs. Two sequences of Gaussian distributed random variables combined with the multiplicative norm-bounded uncertainties are used to characterize the randomly occurring gain variations in the estimators. An event generator function is employed to regulate the transmission of data from the sensor to the estimator. The aim of this paper is to design an exponentially ultimately bounded state estimator in mean square through measurement outputs from a partial of network nodes under the event-triggered mechanism. With the help of Lyapunov-Krasovskii functional and stochastic analysis techniques, sufficient conditions are acquired for the existence of the desired state estimator which ensures that the estimation error dynamics is exponentially ultimately bounded in mean square, and then the estimator gain matrices can be computed via the software Matlab. A simulation example is provided to demonstrate the effectiveness of the proposed state estimation method. [ABSTRACT FROM AUTHOR]

In this paper, the H ∞ state estimation problem is investigated for a class of uncertain discrete-time neural networks subject to infinitely distributed delays and fading channels. Randomly occurring uncertainties (ROUs) are introduced to reflect the random nature of the network condition fluctuations, and the channel fading phenomenon is considered to account for the possibly unreliable network medium on which the measurement signal is transmitted. A set of Bernoulli-distributed white sequences are employed to govern the ROUs and the L -th Rice fading model is utilized where channel coefficients are mutually independent random variables with certain probability density function on [ 0 , 1 ] . We aim to design a state estimator such that the dynamics of the estimation error is asymptotically stable while satisfying the prescribed H ∞ performance constraint. By adopting the Lyapunov–Krasovskii functional and the stochastic analysis theory, sufficient conditions are established to ensure the existence of the desired state estimators and the explicit expression of such estimators is acquired. A simulation example is provided to verify the usefulness of the proposed approach. [ABSTRACT FROM AUTHOR]

This paper is concerned with the distributed fault estimation problem for a class of uncertain stochastic systems over sensor networks. The norm-bounded uncertainty enters into the system in a random way governed by a set of Bernoulli distributed white sequence. The purpose of the addressed problem is to design distributed fault estimators, via available output measurements from not only the individual sensor, but also its neighbouring sensors, such that the fault estimation error converges to zero exponentially in the mean square while the disturbance rejection attenuation is constrained to a give level by means of theperformance index. Intensive stochastic analysis is carried out to obtain sufficient conditions for ensuring the exponential stability as well as prescribedperformance for the overall estimation error dynamics. Simulation results are provided to demonstrate the effectiveness of the proposed fault estimation technique in this paper. [ABSTRACT FROM AUTHOR]

In this paper, the fault estimation issue is dealt with for a kind of complex networks with randomly varying topologies, stochastic inner couplings and missing measurements. For the complex networks under consideration, the random occurrences of the faults, the missing measurements and the switching outer-coupling configuration matrices are governed by mutually independent Bernoulli distributed white sequences. Moreover, the Gaussian white noise sequences are utilized to account for possible stochastic disturbances in the inner couplings. Our objective is to propose a fault estimation algorithm such that the influence from the disturbance inputs to the error dynamics of the fault estimation is attenuated at a required level. By utilizing the stochastic analysis theory and the completing squares method, necessary and sufficient conditions are given for the fault estimator design ensuring that the estimation error dynamics satisfies the prescribed H ∞ constraint. The time-varying fault estimator parameters are acquired by recursively solving a set of coupled backward Riccati difference equations. One simulation example is given to verify the usefulness of the developed estimator design scheme. [ABSTRACT FROM AUTHOR]

In this paper, the protocol-based remote state estimation problem is considered for a kind of delayed artificial neural networks. The random time-varying delays fall into certain intervals with known probability distributions. For the sake of reducing the data collisions in communication channel from the sensors to the estimator, the stochastic communication protocol (SCP) is employed to decide which sensor is allowed to transmit its data to the remote estimator through the channel at each fixed instant. The scheduling principle of the SCP is governed by a Markov chain whose transition probability is allowed to be uncertain so as to reflect the possible imprecision when implementing the SCP. Through a combination of Lyapunov–Krasovskii functional method and the stochastic analysis technique, a sufficient criterion is obtained for the existence of the desired remote state estimator ensuring that the corresponding augmented estimation error dynamics is asymptotically stable with a prescribed H ∞ performance index. Furthermore, the estimator parameter is acquired by solving a convex optimization problem. Finally, the validity of the established theoretical results is demonstrated via a numerical simulation example. [ABSTRACT FROM AUTHOR]

In this paper, the envelope-constrained H ∞ filtering problem is investigated for a class of discrete time-varying stochastic systems over a finite horizon. The system under consideration involves fading measurements, randomly occurring nonlinearities (RONs) and mixed (multiplicative and additive) noises. A novel envelope-constrained performance criterion is proposed to better quantify the transient dynamics of the filtering error process over the finite horizon. The purpose of the problem addressed is to design a time-varying filter such that both the H ∞ performance and the desired envelope constraints are achieved at each time step. By utilizing the stochastic analysis techniques combined with the ellipsoid description on the estimation errors, sufficient conditions are established in the form of recursive matrix inequalities (RMIs) reflecting both the envelope information and the desired H ∞ performance index. The filter gain matrix is characterized by means of the solvability of the deduced RMIs. Finally, a simulation example is provided to show the effectiveness of the proposed filtering design scheme. [ABSTRACT FROM AUTHOR]