For the precise data analysis of wireless sensor networks in confined spaces, considering the characteristics of a non-ideal transmission channel and nodes' limited power supply, the consensus-based data aggregation and energy consumption optimization are considered in this paper. First, the process of wireless data transmission is analyzed, a probabilistic transmission model under non-ideal communication channels is constructed, and a data aggregation method based on average consensus is proposed. Second, the convergence of the aggregation method is analyzed using the theory of random systems, a distributed design of the aggregation gain under mean square error convergence is given, and a method for determining the probability convergence rate is provided when the probabilities of communication links are the same. Also, the upper bound of the probability convergence time of the proposed random aggregation method is analyzed. Third, a network aggregation energy consumption optimization model is established, and the optimization is studied from two aspects: the control of transmission power and the retransmission of states. The methods for determining the optimal transmission power are given when the aggregation gain is fixed and optimized, and the range of link probability values when multiple retrans missions of node state data have better energy efficiency. Finally, the effectiveness of the proposed methods is verified through numerical simulations.

In this paper, the coding-decoding-based fusion estimation is investigated for a class of parameter-varying linear repetitive processes measured by two groups of sensors with different sampling rates. To economize the limited network bandwidth, the encoding-decoding mechanism based on a uniform quantizer is proposed to regulate the communication between the multirate sensors and remote estimators. The objective of the tackled issue is to design a set of zonotopes that cover all process states when faced with coding-decoding mechanisms, multirate measurements and unknown-but-bounded disturbances. By virtue of zonotopic properties, a unified 2-D zonotopic estimation framework is offered, within which a zonotope is first obtained covering all possible system states, and subsequently, the zonotope’s size is minimized by designing an appropriate correlation matrix. Furthermore, the boundedness of the acquired zonotope is also analyzed. Finally, two simulation examples are provided to verify the effectiveness of the proposed zonotopic estimation strategy.

This article investigates the control problem for a sort of repetitive discrete-time nonlinear systems subject to random packet dropouts and limited communication bandwidth. In order to compensate the impacts from the constraints on bandwidth, this work designs a communication protocol by designing a two-description coding scheme in combination with the scalar uniform quantization technique. The proposed protocol makes use of two independent channels to transmit data separately, thereby improving the channel utilization efficiency and reducing the probability of packet dropout. Then, with the proposed protocol and the iterative dynamic linearization approach, an adaptive iterative learning controller associated with a parameter estimation strategy is provided for the nonlinear system under investigation. The control law is data-driven, which therefore does not require knowledge of the model. Subsequently, the sufficient condition is derived under which the tracking error is forced to convergent. Finally, with the purpose to show the correctness of our theoretical results, we carry out two numerical simulations to test the effectiveness of the proposed control strategy.

System measurements and monitoring with information security become vital for microgrids because of the vulnerability of communication networks. The focus of this paper is on privacy-preserving distributed state estimation with measurement data in the face of bit-rate constraints. A new dynamic encryption mechanism designed based on time-varying linear transformations is created to facilitate the encryption of measurements of microgrids. In light of the proposed encryption rule combined with bit-rate constraints, an iterative recursive scheme in a distributed way, receiving the desired estimator gains, is proposed by optimising the upper bound of the estimation error covariance. In addition, the impact of the allocated bit rate is made available to distributed state estimators, and the privacy of encryption strategies adopted is systematically evaluated. Lastly, an engineering-based method is devised to find the linear transformation matrix for a simplified encryption rule. Simulation experiments validate the significant advantages of the established state estimator.

In this paper, an observer-based H_∞ control problem is studied for a class of switched cyber-physical systems over amplify-and-forward relay networks with double fading channels. An AF relay is deployed between sensor and controller with hope to enhance the communication range. In this relay-based network, the relay first receives the data from the sensor and then transfers it to the controller. Further, double fading channels are considered in both the sensor to relay channel and the relay to controller channel, whose coefficients are described by two series of random variables. The application of Lyapunov stability combing with average dwell time technique first leads to the derivation of a sufficient condition for guaranteeing the stability and H_∞ performance of the controlled system. Subsequently, the desired gains of the observer-based controller are parameterized by solving a set of matrix inequalities. Finally, two examples are offered to showcase the applicability of the presented control strategy.

This paper explores the H∞ state estimation problem for a category of discrete-time complex-valued memristive neural networks (CVMNNs). Regarding the studied CVMNNs, the phenomena of the distributed delay and time-varying delay are taken into account so as to describe the system more practically. Firstly, for further effective analysis, the examined CVMNNs are converted to an augmented system that integrates both the real and imaginary dynamics about the initial CVMNNs. To alleviate the communication burden, a representative dynamic event-triggered scheme is employed, for the first time, in the state estimator design of discrete-time CVMNNs. By establishing the Lyapunov functional, a sufficient condition is derived to assure the asymptotical stability of the estimation error system. Subsequently, the explicit expression of the desired estimator is obtained by resolving several matrix inequalities. Ultimately, the efficacy of the designed state estimator is substantiated through a simulation example.

In this paper, the moving horizon estimation problem is considered for discrete-time systems with the filter-and-forward full-duplex relay (FF-FDR). To mitigate the effects of signal fading, the FF-FDR amplifies and forwards the signal transmitted by the sensor to the remote estimator. However, due to its simultaneous transmission and reception capabilities, the full-duplex relay may receive its own transmitted signal, resulting in the self-interference (SI). Moreover, a filter-and-forward relay scheme is adopted where the signal is preprocessed by a moving horizon estimator to alleviate the effects of channel noises. The objective of this paper is to design moving horizon estimators for both the FF-FDR and the discrete-time system against fading channels and relay SI. Novel moving horizon estimators are established where the influences of fading channels and delays induced by SI are taken into account. Finally, a simulation example is presented to verify the effectiveness of the proposed moving horizon estimators.

In this paper, the problem of cubature Kalman fusion filtering (CKFF) is addressed for multi-sensor systems under amplify-and-forward (AaF) relays. For the purpose of facilitating data transmission, AaF relays are utilized to regulate signal communication between sensors and filters. Here, the randomly varying channel parameters are represented by a set of stochastic variables whose occurring probabilities are permitted to exhibit bounded uncertainty. Employing the spherical-radial cubature principle, a local filter under AaF relays is initially constructed. This construction ensures and minimizes an upper bound of the filtering error covariance by designing an appropriate filter gain. Subsequently, the local filters are fused through the application of the covariance intersection fusion rule. Furthermore, the uniform boundedness of the filtering error covariance's upper bound is investigated through establishing certain sufficient conditions. The effectiveness of the proposed CKFF scheme is ultimately validated via a simulation experiment concentrating on a three-phase induction machine.

This paper considers the regional synchronization problem for discrete-time dynamical networks containing node delays and disturbance inputs subject to saturating actuators. In order to decrease the usage of communication resources, an event-triggered scheme with dynamic mechanism is adopted to regulate the data transmission. By incorporating the modified sector condition, the Lyapunov-Krasovskii functional and the discrete Jensen inequality, a sufficient condition is first established such that the synchronization error dynamics has the desirable properties including boundedness, H infinity performance and regional stability. Then, the design parameters are dedicatedly characterized based on the solvability of linear matrix inequalities. Furthermore, certain optimization problems concerning the event-triggered rate and the initial condition set are formulated. Finally, two numerical examples are presented to illustrate the feasibility and advantages of the proposed results.

This work investigates a class of discrete-time memristive neural networks with heterogeneous delays under FlexRay protocol constraints. The protocol implementation effectively mitigates redundant network congestion in sensor-estimator communication channels. Employing Lyapunov-Krasovskii functional analysis, delay-dependent stability criteria are derived to guarantee estimator feasibility. The optimal estimator gain matrix is subsequently determined through convex optimization techniques. Numerical simulations validate the proposed methodology's effectiveness in terms of convergence rate and estimation accuracy

This article is concerned with the distributed set-membership fusion estimation problem for a class of artificial neural networks (ANNs), where the dynamic event-triggered mechanism (ETM) is utilized to schedule the signal transmission from sensors to local estimators to save resource consumption and avoid data congestion. The main purpose of this article is to design a distributed set-membership fusion estimation algorithm that ensures the global estimation error resides in a zonotope at each time instant and, meanwhile, the radius of the zonotope is ultimately bounded. By means of the zonotope properties and the linear matrix inequality (LMI) technique, the zonotope restraining the prediction error is first calculated to improve the prediction accuracy and subsequently, the zonotope enclosing the local estimation error is derived to enhance the estimation performance. By taking into account the side-effect of the order reduction technique (utilized in designing the local estimation algorithm) of the zonotope, a sufficient condition is derived to guarantee the ultimate boundedness of the radius of the zonotope that encompasses the local estimation error. Furthermore, parameters of the local estimators are obtained via solutions to certain bilinear matrix inequalities. Moreover, the zonotope-based distributed fusion estimator is obtained through minimizing certain upper bound of the radius of the zonotope (that contains the global estimation error) according to the matrix-weighted fusion rule. Finally, the effectiveness of the proposed distributed fusion estimation method is illustrated via a numerical example.

Dear Editor, This letter deals with the distributed recursive set-membership filtering (DRSMF) issue for state-saturated systems under encryption-decryption mechanism. To guarantee the data security, the encryption-decryption mechanism is considered in the signal transmission process. Specifically, a novel DRSMF scheme is developed such that, for both state saturation and encryption-decryption mechanism, the filtering error (FE) is limited to the ellipsoid domain. Then, the filtering error constraint matrix (FECM) is computed and a desirable filter gain is derived by minimizing the FECM. Besides, the bound-edness evaluation of the FECM is provided.

This article addresses the distributed filtering problem for a class of discrete time-varying systems over a binary sensor network (BSN) with amplify-and-forwarded (AF) relays, where the plant and binary measurements are subject to random noises that have known statistical information. The formulation of the topology of BSN with AF relays is first carried out. To reflect the dithering of the threshold of the binary sensor, a random variable with zero mean is utilized, and then useful information for estimation purposes is extracted by employing the joint distribution functions of measurement noises and the new random variable. Signal transmissions among neighbors are realized over fading channels, with a part of sensors communicating via relays. The desired distributed filter for each sensor is constructed using the available information from itself and its neighbors such that the resulting filtering error dynamics satisfies the desired exponential boundedness. To combat the fading effect, the relay matrix and the encoder matrix are designed in the sense of minimum mean square error so as to guarantee the effectiveness of the transmitted signal. The sufficient criterion for each sensor (with or without a relay) is established within the framework of the local performance analysis. The desired filter gains for each sensor are derived by solving a constrained optimization problem. Finally, an illustrative simulation example is used to demonstrate the applicability and effectiveness of the developed distributed filtering scheme.

This paper is concerned with the protocol-based fault detection (FD) issue for state-saturated delayed nonlinear systems with both redundant channels and sensor nonlinearities. Specifically, in pursuit of relieving the communication pressure, the stochastic communication protocol is adopted, which can regulate the transmission of output signal during the communication process. Moreover, for the purpose of decreasing the negative impact of one channel failure on network communication and enhancing data reliability, the redundant channels transmission is introduced. Then, the protocol-based FD filter is constructed such that the resultant FD dynamics system attains the global asymptotic stability in mean-square sense and satisfies the H∞ performance index. Moreover, an implement-to-easy algorithm is developed to facilitate the solvability of the desirable FD filtering method. In the end, two illustrative examples are utilized to demonstrate the validity of the developed FD filtering algorithm.

In this article, the cooperative fault-tolerant tracking control (FTTC) is investigated for discrete time multi-agent systems (MASs) with time-varying delays (TVDs) under multiple description encoding schemes (MDESs). First, a uniform channel model is proposed to describe the employed MDES subject to the effect of packet dropouts by introducing two independent random variables obeying the Bernoulli distribution and three indicator variables. Subsequently, a novel intermediate estimator is designed to estimate both system states and a fictitious intermediate variable (an integration of faults and leader's inputs) based on relatively measured outputs. In terms of the Lyapunov stability theory, some sufficient conditions are acquired to guarantee that the closed-loop system is exponentially ultimately bounded in the mean-square sense. Furthermore, the desired gain matrices are obtained by resorting to both the graph feature and singular value decomposition. Finally, the effectiveness and superiority are tested by two simulation examples for the proposed tracking protocol.

In this article, the set-membership state estimation problem is investigated for a class of nonlinear complex networks under the FlexRay protocols (FRPs). In order to address practical engineering requirements, the multirate sampling is taken into account which allows for different sampling periods of the system state and the measurement. On the other hand, the FRP is deployed in the communication network from sensors to estimators in order to alleviate the communication burden. The underlying nonlinearity studied in this article is of a general nature, and an approach based on neural networks is employed to handle the nonlinearity. By utilizing the convex optimization technique, sufficient conditions are established in order to restrain the estimation errors within certain ellipsoidal constraints. Then, the estimator gains and the tuning scalars of the neural network are derived by solving several optimization problems. Finally, a practical simulation is conducted to verify the validity of the developed set-membership estimation scheme.

This work investigates the probabilistic-constrained tracking control problem for T-S fuzzy systems under fading channels described by the memoryless multiplicative noise model. The main control objective is to confine the tracking error into its pre-given bounds no lower than a certain probability. To this end, a tracking controller is firstly synthesised by introducing the fading parameter as an amplification factor towards 'retrieving' the original information to some extent. An auxiliary function and the multi-dimensional Chebyshev inequality make it possible to derive sufficient conditions such that the tracking error falls into its specified domain with a frequency larger than a pre-given value. By employing an inequality involving unmatched membership functions of the controller and fuzzy system, the favourite property of matched membership functions can be employed. A numerical example illustrates the effectiveness of the proposed method.

A practical yet challenging scenario in transfer learning is unsupervised domain adaptation (UDA), where knowledge is transferred from a labeled source domain to unlabeled target domains. The crucially important role of domain-variant characteristics is often neglected by most existing UDA methods, which can deteriorate adaptation performance and result in negative transfer. In this article, an optimal unsupervised domain adaptation (OUDA) algorithm is proposed in order to address this issue, which balances the invariance of domain-sharing features and the variance of domain-specific features. In the proposed approach, a gradient adversarial adaptation (GAA) method is introduced to align the gradient directions of source and target features within the same category, thereby facilitating knowledge transfer. In addition, a local manifold embedding (LME) technique is proposed to preserve the intrinsic geometric structure of the original feature space while implementing distribution alignment, providing distinguishable features for UDA. To stabilize the process of knowledge transfer, an evolutionary control strategy is developed to adaptively control the tradeoff between the GAA and LME by employing the particle swarm optimization algorithm. Extensive experiments are conducted on cross-domain natural gas pipeline fault diagnosis, and the results on nine cross-domain classification tasks indicate that our OUDA algorithm outperforms the existing state-of-the-art UDA methods. Moreover, the performance analysis in terms of accuracy, loss, and domain divergence demonstrates the superior stability of the proposed OUDA algorithm in dealing with unsupervised knowledge transfer.

This paper investigates the problem of outlier-resistant distributed fusion filtering (DFF) for a class of multi-sensor nonlinear singular systems (MSNSSs) under a dynamic event-triggered scheme (DETS). To relieve the effect of measurement outliers in data transmission, a self-adaptive saturation function is used. Moreover, to further reduce the energy consumption of each sensor node and improve the efficiency of resource utilization, a DETS is adopted to regulate the frequency of data transmission. For the addressed MSNSSs, our purpose is to construct the local outlier-resistant filter under the effects of the measurement outliers and the DETS; the local upper bound (UB) on the filtering error covariance (FEC) is derived by solving the difference equations and minimized by designing proper filter gains. Furthermore, according to the local filters and their UBs, a DFF algorithm is presented in terms of the inverse covariance intersection fusion rule. As such, the proposed DFF algorithm has the advantages of reducing the frequency of data transmission and the impact of measurement outliers, thereby improving the estimation performance. Moreover, the uniform boundedness of the filtering error is discussed and a corresponding sufficient condition is presented. Finally, the validity of the developed algorithm is checked using a simulation example.

In this paper, the recursive quadratic filtering problem is investigated for a class of linear non-Gaussian systems with dynamical bias and amplify-and-forward relays. The stochastic bias, characterized by a dynamical process with certain non-Gaussian noises, is incorporated into the system state equation. An amplify-and-forward relay is utilized in the sensor-to-filter network channel to enhance signal transmission performance. The transmission powers of the sensor and relay are governed by two sets of random variables. Particular attention is given to the design of a quadratic filter in the presence of the dynamical bias, the amplify-and-forward relay, and non-Gaussian noises. For this purpose, an augmented system is constructed by aggregating the augmented state (comprising the original state and the associated bias) and its second-order Kronecker power. Consequently, the addressed quadratic issue for the underlying non-Gaussian system is reformulated as a linear filtering problem for the augmented system. Using difference equations, the filtering error covariance is derived and subsequently minimized through the design of an appropriate gain matrix. Moreover, sufficient conditions are established to ascertain the existence of the lower and upper bounds on the filtering error covariance. Finally, the effectiveness of the designed quadratic filtering algorithm is demonstrated through a numerical example.