Search

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 article, an extended state observer‐based finite‐region control scheme is presented for two‐dimensional Markov jump systems with unknown mismatched disturbances. The mathematical model of the two‐dimensional Markov jump systems is built on the well‐known Roesser model. By establishing special recursive formulas and utilizing the 2‐D Lyapunov function theory, sufficient conditions are obtained, which prove that the resultant system is finite‐region bounded, if some linear matrix inequalities are achieved. Then, we provide an algorithm to solve the extended state observer‐based controller gains. With the proposed control scheme, the external disturbances can be actively rejected from the system outputs. To conclude, a numerical example based on the Darboux equation is provided to demonstrate the validity and effectiveness of the devised control scheme. [ABSTRACT FROM AUTHOR]

In this article, the recursive state estimation problem is investigated for a class of multi‐rate systems with multiplicative noises where the measurement outputs are collected from sensors with certain resolutions. Due to the existence of the sensor resolution, the actual measurement output of the sensor might deviate from its true value and such a deviation, if not adequately taken into account, would lead to serious degradation of the estimation performance, and we are therefore motivated to develop an effective state estimation algorithm that is insensitive to the sensor‐resolution‐induced measurement distortions. The aim of the considered estimation problem is to design a state estimator such that an upper bound on the estimation error covariance is first guaranteed and then minimized by properly choosing the estimator gain. Moreover, a simulation example with application background on moving target tracking problem is presented to verify the validity of the developed recursive state estimation algorithm. [ABSTRACT FROM AUTHOR]

In this paper, the dynamic event‐based recursive filtering problem is studied for multirate systems over sensor networks. The state update rate of the plant and the sampling rate of the sensors are allowed to be different in order to reflect the multirate sampling strategy. Moreover, the phenomenon of integral measurements is considered to cater for the real engineering practice. To reduce unnecessary data transmissions, the dynamic event‐based mechanism is implemented in the communication channels among sensor nodes. The purpose of this paper is to design a distributed recursive filtering scheme such that, under the influence of the integral measurements, the multi‐rate sampling, and the dynamic event‐based mechanism, there exists a minimal upper bound on the filtering error covariance. An upper bound on the filtering error covariance is first derived by solving a matrix Riccati equation, and then minimized at each sampling instant by choosing appropriate filter gains. Comprehensive simulations are conducted on a numerical example and a practical example to show the effectiveness and superiority of the proposed dynamic event‐based recursive filtering scheme. [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]

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]

Summary: This paper is concerned with distributed entropy filtering for a class of time‐varying systems subject to non‐Gaussian noises and denial‐of‐service attacks. A distributed Kalman‐type filter is constructed by fusing the information from neighboring sensors in the one‐step prediction estimation. The maximum correlation entropy criterion with weighted Gaussian kernels instead of the traditional least squares and minimum variance indices is then employed to evaluate the filtering performance under the more general case of non‐Gaussian noises. For the considered scenario, a new algorithm of distributed maximum correntropy criterion Kalman filters is developed by utilizing traditional fixed‐point iterative rules, and the desired filter gains are dependent on both the one‐step prediction cross‐variance and the weighted Gaussian kernel function. In light of a similar analysis process and an introduced auxiliary cost, its suboptimal version is proposed to avoid the calculation of cross‐variance and thereby satisfying the requirement of real time. Furthermore, a degenerated result is also derived under the traditional correntropy criterion (ie, with unit weighted matrices). Finally, the simulation results show the merit of proposed distributed maximum correntropy criterion Kalman filters in the presence of denial‐of‐service attacks and non‐Gaussian noises. [ABSTRACT FROM AUTHOR]

Summary: This paper provides a reliable fusion scheme over sensor networks subject to abnormal measurements and energy constraints. Two kinds of channels are employed to implement the information transmission in order to extend the lifetime. Specifically, the one has the merit of high reliability by sacrificing energy cost and the other reduces the energy cost but could result in packet loss. For the addressed problem, a χ2 detection in local state estimator is first designed to remove abnormal measurements, which could come from outliers or a malicious modification by attackers. Then, a new strategy is developed to compensate the lost local estimation transmitted by low‐reliable channels. Furthermore, by view of matrix operation and probability theory, a set of recursive formulas are developed to calculate desired error covariance matrices of local state estimation, compensated state estimation as well as fusion estimation. The optimal fusion weights are obtained analytically and the advantage of fusion estimation is disclosed by resorting to these covariance matrices. Finally, a numerical example is used to illustrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Summary: This paper is concerned with the local design of the distributed H∞‐consensus filtering problem for a class of discrete time‐varying systems subject to both multiplicative noises and deception attacks over sensor networks. The target plant and the measuring sensors are disturbed by multiplicative noises with known statistics. The malicious signal involved in deception attacks is constrained by a specific sector‐like bounded condition, which reflects certain tolerable bound on the difference between the attack signal and the true signal. Attention is paid to the design of filter gains for guaranteeing a desirable filtering performance that simultaneously characterizes the filtering accuracy and the consensus requirement. To handle the proposed filtering problem, the supply rate function is firstly chosen for each node and then the dissipation matrix is constructed as a column substochastic matrix based on the stochastic vector dissipation theory. Subsequently, sufficient conditions by means of recursive linear matrix inequalities are presented for each node such that the filtering error and the consensus error satisfy the desirable H∞‐consensus performance index over a finite horizon. Finally, an illustrative simulation is presented to demonstrate the effectiveness of the proposed filter strategy. [ABSTRACT FROM AUTHOR]

Summary: In this paper, we analyze the finite‐horizon fault estimation issue for a kind of time‐varying nonlinear systems with imperfect measurement signals under the stochastic communication protocol (SCP). The imperfect measurements result from randomly occurring sensor nonlinearities obeying sensor‐wise Bernoulli distributions. The Markov‐chain‐driven SCP is introduced to regulate the signal transmission to alleviate the communication congestion. The aim of the considered issue is to propose the design algorithm of a group of time‐varying fault estimators such that the estimation error dynamics satisfies both the H∞ and the finite‐time boundedness (FTB) performance requirements. First, sufficient conditions are set up to guarantee the existence of the satisfactory H∞ FTB fault estimators through intensive stochastic analyses and matrix operations. Then, the gains of such fault estimators are explicitly parameterized by resorting to the solution to recursive linear matrix inequalities. Finally, the correctness of the devised fault estimation approach is demonstrated by a numerical example. [ABSTRACT FROM AUTHOR]

In this paper, the H∞ state estimation problem is investigated for a class of discrete-time stochastic systems in simultaneous presence of three network-induced phenomena, namely, fading measurements, randomly varying nonlinearities and probabilistic distributed delays. The channel fading is characterized by the `th-order Rice fading model whose coefficients are mutually independent random variables with given probability density functions. Two sequences of random variables obeying the Bernoulli distribution are utilized to govern the randomly varying nonlinearities and probabilistic distributed delays. The purpose of the problem addressed is to design an H∞ state estimator such that the dynamics of the estimation errors is stochastically stable and the prespecified H∞ disturbance rejection attenuation level is guaranteed. Through intensive stochastic analysis, sufficient conditions are established under which the addressed state estimation problem is recast as a convex optimization one that can be solved via the semi-definite program method. Finally, a simulation example is provided to show the usefulness of the proposed state estimation scheme. [ABSTRACT FROM AUTHOR]

SUMMARY This paper is concerned with the distributed [ABSTRACT FROM AUTHOR]