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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]

In this paper, the H ∞ filtering problem is addressed for a class of nonlinear stochastic discrete-time systems subject to Round-Robin protocol, measurement outliers, randomly occurring parameter uncertainties and randomly occurring nonlinearities. First of all, the considered system is influenced by the parameter uncertainties, nonlinear dynamics and external disturbances. Next, mutually independent Bernoulli-distributed white sequences are employed to describe the phenomena of randomly occurring parameter uncertainties and nonlinearities, respectively. Besides, the Round-Robin protocol is introduced to prevent the occurrence of data collisions during the signal transmission. Moreover, a filter with adaptive saturation is constructed to weaken the impact of the measurement outliers. Through constructing a suitable Lyapunov function, sufficient conditions are established to ensure that the error system dynamics is exponentially mean-square stable and satisfies the H ∞ performance. Subsequently, the filter gains are designed by solving a set of linear matrix inequalities. Finally, a simulation example is given to show the effectiveness of the filtering scheme proposed in this paper. [ABSTRACT FROM AUTHOR]

This paper is concerned with the Tobit Kalman filtering problem for a class of linear discrete-time system with random parameters. The elements of both the system matrix and the measurement matrix are allowed to be random variables in order to reflect the reality. The information matrix is employed to 1) derive the covariance between any two random variables; and 2) establish a novel weighting covariance formula to address the quadratic terms associated with the random matrices. A set of Bernoulli random variables is introduced to govern the censoring phenomenon on the measurement output. The conditional expectation, as a basic tool, is utilized to deal with the dependence among the random variables. Within the framework of the traditional Kalman filtering, the proposed filtering algorithm includes the information from both the random parameters and the censored measurements. A simulation example is presented to illustrate the effectiveness and applicability of the designed algorithm. [ABSTRACT FROM AUTHOR]

This paper is concerned with the nonfragile $H_\infty$ filtering problem for a class of discrete-time Takagi-Sugeno (T-S) fuzzy systems with both randomly occurring gain variations (ROGVs) and channel fadings. the phenomenon of the ROGVs is introduced into the system model so as to account for the parameter fluctuations occurring during the filter implementation. Two sequences of random variables obeying the Bernoulli distribution are employed to describe the phenomenon of the ROGVs bounded by prescribed norms. In addition, the Rice fading model is utilized to describe the phenomena of channel fadings, where the occurrence probabilities of the random channel coefficients are allowed to time varying. Through stochastic analysis and Lyapunov functional approach, sufficient conditions are established under which the filtering error dynamics is exponentially mean-square stable with a prespecified $H_\infty$ performance. The set of the desired nonfragile $H_\infty$ filters is characterized by solving a convex optimization problem via the semidefinite programming method. An illustrative example is given to show the usefulness and effectiveness of the proposed design method in this paper. [ABSTRACT FROM PUBLISHER]

This paper is concerned with the problem of designing a non-fragile state estimator for a class of uncertain discrete-time neural networks with time-delays. The norm-bounded parameter uncertainties enter into all the system matrices, and the network output is of a general type that contains both linear and nonlinear parts. The additive variation of the estimator gain is taken into account that reflects the possible implementation error of the neuron state estimator. The aim of the addressed problem is to design a state estimator such that the estimation performance is non-fragile against the gain variations and also robust against the parameter uncertainties. Sufficient conditions are presented to guarantee the existence of the desired non-fragile state estimators by using the Lyapunov stability theory and the explicit expression of the desired estimators is given in terms of the solution to a linear matrix inequality. Finally, a numerical example is given to demonstrate the effectiveness of the proposed design approach. [ABSTRACT FROM AUTHOR]

In this paper, the non-fragile state estimation problem is investigated for a class of discrete-time neural networks subject to Markovian jumping parameters and time delays. In terms of a Markov chain, the mode switching phenomenon at different times is considered in both the parameters and the discrete delays of the neural networks. To account for the possible gain variations occurring in the implementation, the gain of the estimator is assumed to be perturbed by multiplicative norm-bounded uncertainties. We aim to design a non-fragile state estimator such that, in the presence of all admissible gain variations, the estimation error converges to zero exponentially. By adopting the Lyapunov–Krasovskii functional and the stochastic analysis theory, sufficient conditions are established to ensure the existence of the desired state estimator that guarantees the stability of the overall estimation error dynamics. The explicit expression of such estimators is parameterized by solving a convex optimization problem via the semi-definite programming method. A numerical simulation example is provided to verify the usefulness of the proposed methods. [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]

Abstract: This paper is concerned with the fault detection problem for a class of discrete-time systems with randomly occurring nonlinearities, mixed stochastic time-delays as well as measurement quantizations. The nonlinearities are assumed to occur in a random way. The mixed time-delays comprise both the multiple discrete time-delays and the infinite distributed delays that occur in a random way as well. A sequence of stochastic variables is introduced to govern the random occurrences of the nonlinearities, discrete time-delays and distributed time-delays, where all the stochastic variables are mutually independent but obey the Bernoulli distribution. The main purpose of this paper is to design a fault detection filter such that, in the presence of measurement quantization, the overall fault detection dynamics is exponentially stable in the mean square and, at the same time, the error between the residual signal and the fault signal is made as small as possible. Sufficient conditions are first established via intensive stochastic analysis for the existence of the desired fault detection filters, and then the explicit expression of the desired filter gains is derived by means of the feasibility of certain matrix inequalities. Also, the optimal performance index for the addressed fault detection problem can be obtained by solving an auxiliary convex optimization problem. A practical example is provided to show the usefulness and effectiveness of the proposed design method. [Copyright &y& Elsevier]

This paper is concerned with the recursive quadratic filtering problem for a class of linear discrete-time systems subject to non-Gaussian noises. Considering its robustness against channel noises, the binary encoding scheme is utilized in the process of data transmission from sensors to the filter. Under such a scheme, the original signal is first encoded into a bit string, and then transmitted via memoryless binary symmetric channels (with certain crossover probabilities). Subsequently, the received bit string is recovered by a decoder at the receiver end. The primary purpose of this paper is to design a recursive quadratic filter for the underlying non-Gaussian systems with a minimized upper bound on the filtering error covariance. For this purpose, an augmented system is first constructed by aggregating the original vectors and their second-order Kronecker powers. Accordingly, an upper bound on the filtering error covariance is obtained in the form of solutions to certain Riccati-like difference equations, and the obtained bound is then minimized by properly choosing the filter parameter. Moreover, sufficient conditions are established to guarantee the boundedness of filtering error covariance. Finally, a numerical example is provided to demonstrate the effectiveness of the proposed quadratic filtering algorithm. [ABSTRACT FROM AUTHOR]

Abstract: This paper deals with the consensus control problem for a class of discrete time-varying multi-agent systems with both missing measurements and parameter uncertainties. A directed graph is used to represent the communication topology of the multi-agent network, and a binary switching sequence satisfying a conditional probability distribution is employed to describe the missing measurements. The purpose of the addressed problem is to design a time-varying controller such that, for all probabilistic missing observations and admissible parameter uncertainties, the consensus performance is guaranteed over a given finite horizon for the closed-loop networked multi-agent systems. According to the given topology, the measurement output available for the controller is not only from the individual agent but also from its neighboring agents. By using the completing squares method and stochastic analysis techniques, necessary and sufficient conditions are derived for the consensus to be ensured, and then the time-varying controller parameters are designed by solving coupled backward recursive Riccati difference equations (RDEs). A simulation example is utilized to illustrate the usefulness of the proposed control protocol. [Copyright &y& Elsevier]

This paper is concerned with the remote state estimation problem for a class of linear discrete time-varying stochastic systems under communication constraints. In order to mitigate the undesired phenomena of data collisions, a Try-Once-Discard (TOD) protocol is utilized to regulate the signal transmissions over the sensor-to-estimator communication channel, where the TOD protocol is based on the scheduling rule described by a specific switching function. Under the introduced TOD protocol, the approximate minimum mean-square error (MMSE) state estimation problem is investigated and a recursive algorithm is developed for the MMSE estimator design whose computational complexity is comparable to that of the conventional Kalman filter. Furthermore, some conditions are established for the boundedness of the estimation error covariance. A numerical example is presented to illustrate the effectiveness of the proposed MMSE estimator. [ABSTRACT FROM AUTHOR]

• The purpose of this article is to find an outlier-resistant observer-based H ∞ PID controller for discrete-time systems under the SCP scheduling. In this paper, the motivations of this paper are outlined as follows: 1) saturation constraint, as an active measure, can effectively suppress the influence of outliers on the system performance by targeting the measurement outliers caused by sensor failure and cyber attacks. However, the novel outlier-resistant observer-based H ∞ PID control problem can not be solved by the existing control method, which needs to be solved urgently; 2) due to the data congestion caused by the limitation of network bandwidth, SCP is used to schedule the transmission sequence of network nodes to avoid data congestion. Nevertheless, the design of H ∞ PID controller under the stochastic communication protocol has not gained adequate attention from scholars, it has great research significance to fill in such a gap. • The main contributions of this article are highlighted in the following aspects: 1) according to our review of the existing results, the outlier-resistant observer-based H ∞ PID controller design problem is firstly dealt with for a type of discrete-time systems under the SCP scheduling; 2) an observer model with saturation function is constructed to suppress the negative influence of measurement outliers, where the saturation level is dynamically adjusted in term of the previous observation errors; and 3) the stability and H ∞ disturbance attenuation performance are analyzed in the simultaneous presence of abnormal measurements and SCP, in which a Markov chain is used to characterize the stochastic switching property of SCP. This paper addresses the H ∞ proportional-integral-derivative control problem based on the outlier-resistant observer for discrete-time systems under the schedule of stochastic communication protocol (SCP). A novel observer with saturation constraint is structured in order to reduce the negative influence of abnormal measurement outputs, where the saturation limit is dynamically adjusted in the light of the observation errors. For the sake of avoiding the phenomenon of data congestion, the SCP is deployed to orchestrate the order of data transmission in the sensor-to-observer channel. In view of the Lyapunov stability theory and the orthogonal decomposition method, sufficient conditions are obtained to make sure that the exponential mean-square stability is met and the H ∞ performance index is satisfied. Eventually, a simulation example is provided to confirm the effectiveness and correctness of the presented controller design algorithm. [ABSTRACT FROM AUTHOR]

This paper is concerned with the consensusability problem for a class of discrete-time multi-agent systems (DT-MASs). A binary encoding scheme (BES) is employed during the data transmission, and only a finite-length binary bit string is transmitted due primarily to the limited network bandwidth in practice. Meanwhile, the binary bit string, transmitted via memoryless binary symmetric channels, might suffer from random bit errors with a certain probability. The purpose of this paper is to derive some consensusability conditions, under which there must exist a distributed controller such that the mean-square bounded consensus is achieved by the considered DT-MASs with BESs subject to random bit errors. To this end, the statistical properties are first revealed for the BES-induced quantization errors and the random bit errors. Then, by resorting to the solvability analysis of a modified Riccati inequality, some sufficient conditions are, respectively, derived to ensure the mean-square bounded consensus under the undirected communication topology in two scenarios: (1) identical bit-error rates (BERs); and (2) non-identical BERs. In particular, a necessary and sufficient condition is established for a single input case with identical BERs. Furthermore, the ultimate upper bound of the consensus errors in the mean-square sense is established to examine the effects of the length of bit string and the BERs. Finally, an illustrative simulation example is provided to validate the effectiveness of the theoretical results. [ABSTRACT FROM AUTHOR]