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This article is concerned with the finite‐time fault detection (FTFD) problem for discrete networked systems subject to nonlinearities and time‐varying delays under the dynamic event‐triggered mechanism. The dynamic event‐based communication scheme is adopted to improve the efficiency of the bandwidth utilization, reduce the transmission of redundant signals and decrease the transmission pressure of the network. Then, by constructing a new Lyapunov functional and utilizing the appropriate inequality techniques, some sufficient conditions are derived under which the addressed augmented system achieves the finite‐time stability and attains a satisfactory H∞$$ {H}_{\infty } $$ performance index. Additionally, the expression forms of the fault detection filter (FDF) parameters are presented based on the solutions to certain matrix inequalities. In the end, a numerical simulation is employed to demonstrate the effectiveness and feasibility of the proposed FTFD strategy. [ABSTRACT FROM AUTHOR]

In this paper, the finite-time fault detection (FTFD) problem is investigated for a class of discrete-time nonlinear systems subject to the probabilistic interval time-varying delay and the randomly occurring faults (ROFs) under the annulus-event-based communication strategy (AEBCS). A Bernoulli stochastic variable is used to depict the probability interval time-varying delay phenomenon, where the time delay is bounded and its probability distributions can be observed. The phenomena of the ROFs are characterized by the Markov chain with two states. Furthermore, the AEBCS is applied to determine whether the measured signals can be transmitted to the filter or not. For the fault detection (FD) problem, we propose some sufficient conditions to ensure that the error dynamics system is finite-time stochastically stable with the specified H ∞ performance index. Meanwhile, the gains of the filter can be calculated via the feasible solution to certain linear matrix inequalities. In the end, two numerical examples are proposed to verify the effectiveness of newly proposed FTFD method via the AEBCS. [ABSTRACT FROM AUTHOR]

This paper is coped with the robust sliding-mode-based control problem for a class of discrete nonlinear systems in the presence of mixed-delays and packet losses with uncertain missing probability. Both the time-varying state delays and the infinite distributed state delays are considered. Also, the data packet losses are modeled by a Bernoulli distributed stochastic variable with uncertain missing probability and an update rule is employed to characterize the signal transmitted to controller side. A sliding function is first constructed and the desired stochastic mean-square stability of sliding motion is ensured by providing a sufficient condition based on the matrix inequality technique. Besides, a new discrete SMC strategy is designed to guarantee that the state trajectories are driven onto the bounded band of predesigned sliding surface and maintain them therein during subsequent time. Finally, the effectiveness of the developed sliding-mode control technique is verified by some simulations with comparative results. [ABSTRACT FROM AUTHOR]

In this paper, the finite-time memory fault detection filter (MFDF) is designed for nonlinear discrete systems with randomly occurring deception attacks, where the phenomenon of the randomly occurring deception attacks is characterised by a Bernoulli distributed random variable with known probability. To be specific, the finite-horizon data are employed to construct the MFDF. The main purpose of this paper is to design the MFDF such that, for all nonlinearities, external disturbances and randomly occurring deception attacks, the resultant augmented system is finite-time stable and attains the H ∞ performance. Accordingly, some sufficient conditions are developed to guarantee the existence of the desired fault detection filter parameters, where the solvability of the addressed problem is verified by the feasibility of certain matrix inequalities. Moreover, in order to show that the MFDF has better detection performance, a non-memory fault detection filter (NMFDF) is constructed to compare with the MFDF. Finally, two numerical simulations are utilised to illustrate the effectiveness of the proposed fault detection strategies and explain the superiority of the proposed MFDF. [ABSTRACT FROM AUTHOR]

This paper is concerned with the robust fault detection (FD) problem for discrete uncertain systems with data drift, randomly occurring faults (ROFs) and randomly occurring nonlinearities (RONs) under weighted try-once-discard (WTOD) protocol. The phenomena of the RONs are characterized by the Bernoulli distributed variable. The Markov chain with two states is introduced to describe the ROFs in the system. The data drift is depicted by an array of mutually independent stochastic variables with individual probabilistic distribution functions. Moreover, the WTOD protocol is utilized to lessen the incidence of data collisions and enhance the communication efficiency. An FD filter is established, and sufficient conditions are proposed under which the resultant filtering error system is stochastically stable and achieves a prescribed H ∞ performance irrespective of the simultaneous presence of the WTOD protocol mechanism, ROFs, RONs and data drift. Besides, the form of the desired filter parameters is presented in terms of the solutions to certain matrix inequalities. Finally, two examples are exploited to demonstrate the feasibility of the obtained robust FD algorithm. [ABSTRACT FROM AUTHOR]

In this paper, the state estimation problem is investigated for a class of discrete nonlinear systems with randomly occurring uncertainties and distributed sensor delays. The norm-bounded uncertainties enter into the system in a randomly way, and such randomly occurring uncertainties (ROUs) obey certain Bernoulli distributed white noise sequence with known conditional probability. By constructing a new Lyapunov–Krasovskii functional, sufficient conditions are proposed to guarantee the convergence of the estimation error for all discrete time-varying delays, ROUs and distributed sensor delays. Subsequently, the explicit form of the estimator parameter is derived by solving two linear matrix inequalities (LMIs) which can be easily tested by using standard numerical software. Finally, a simulation example is given to illustrate the feasibility and effectiveness of the proposed estimation scheme. [ABSTRACT FROM PUBLISHER]

In this paper, the extended Kalman filtering scheme in a distributed manner is presented for state-saturated nonlinear systems (SSNSs), where the randomly occurring cyberattacks (ROCAs) with uncertain occurring probabilities (UOPs) are taken into account. In particular, a novel cyberattack model is constructed by the consideration of false data-injection attacks (FDIAs) and denial-of-service attacks (DoSAs) simultaneously. The ROCAs are described by a series of Bernoulli distributed stochastic variables, where the so-called UOPs are considered and described by the nominal mathematical expectations and error bounds. The major effort is to develop a novel DEKF strategy for SSNSs with consideration of state delay and ROCAs with UOPs. In what follows, an upper bound with respect to the filtering error covariance is derived and minimized by selecting the suitable filter parameter. Besides, the concrete expression of the filter parameter is formed by solving matrix difference equations (MDEs). Meanwhile, a sufficient condition under certain constraints is proposed to testify the boundedness regarding the given upper bound. Finally, we use the experiments and corresponding comparisons to verify the feasibility of the designed extended Kalman filtering approach in a distributed way. [ABSTRACT FROM AUTHOR]

This study is focused on the probability-guaranteed distributed set-membership (PDSM) filtering problem for a class of nonlinear systems with distributed delays, uniform quantization and stochastic communication protocol (SCP) over sensor networks. Also, the system noise is allowed to be unknown-but-bounded (UBB). To minimize and prevent the collision of data, SCP can be effectively implemented in sensor networks to regulate the data transmission between sensor nodes, which allows each node to randomly get information from its neighbors. The main purpose of this paper is to present a novel PDSM filter-design approach such that the filtering error is constrained to the specified ellipsoidal set with a predetermined probability. First, the recursive linear matrix inequality (RLMI) technique is applied to propose a sufficient criterion for the existence of the desired filter. Moreover, the optimal PDSM filter parameters are derived via solving the convex optimization problem of minimizing the sequence of ellipsoidal sets. In the end, a simulation example is put forward to verify the availability and feasibility of the addressed filtering scheme. [ABSTRACT FROM AUTHOR]

• A novel distributed filtering compensation algorithm is presented in terms of the available transmitted data. • The expression form of the minimized UB of filtering error covariance matrix and the desirable filter gain are established. • The filtering algorithm performances are analyzed by providing the theoretical proofs regarding the boundedness and the monotonicity. The saturated-constrained distributed filtering problem is discussed for a class of discrete nonlinear systems with random access protocol (RAP) and missing measurements (MMs) under uncertain missing probabilities (UMPs). In order to prevent data congestions, the RAP is adopted to regulate the information transmission, where only one sensor is scheduled to have the communication right and transmit the information at each time step through a shared network. In addition, the phenomenon of MMs is considered, in which the UMPs are characterized by means of the nominal means and error bounds. The main purpose of this paper is to design a distributed filter under saturation constraint such that, for both RAP and MMs under UMPs, there exists an upper bound (UB) matrix of filtering error covariance and the corresponding distributed filter gain is constructed to minimize the trace of the received UB matrix by resorting to the matrix simplifying technique. Besides, the filtering algorithm performances are discussed regarding the boundedness and monotonicity of the presented distributed filtering scheme from the mathematical perspective. Finally, some comparisons are provided to illustrate the validity of the proposed time-varying distributed filtering algorithm. [ABSTRACT FROM AUTHOR]

This paper studies the sliding-mode-based control problem for a class of delayed Markovian jump repeated scalar nonlinear systems (RSNSs) subject to packet dropouts (PDs) and randomly occurring uncertainties (ROUs). Here, the phenomenon of the Markovian PDs is modeled by utilizing a random variable obeying Markov process. By using the Bernoulli distributed random variables, the ROUs under uncertain occurrence probabilities (UOPs) are characterized from the mathematical perspective. In addition, the time-delay considered is bounded time-varying with known upper and lower bounds. The attention is on designing a sliding-mode-based control method for Markovian jump RSNSs in the simultaneous presence of Markovian PDs, ROUs and time-varying delays. Specifically, the robustly asymptotically mean-square stability is ensured for corresponding sliding mode dynamics by proposing new sufficient criterion. Moreover, the reachability in discrete setting is analyzed, i.e., the state trajectories of the system from any initial condition are globally driven onto a small region nearby the pre-designed sliding surface in mean square sense by synthesizing a robust sliding mode control (SMC) law. Finally, the effectiveness of developed control method is verified by some simulations. [ABSTRACT FROM AUTHOR]