This paper investigates the distributed state estimation problem for nonlinear time-varying systems over sensor networks governed by gossip-based token bucket protocols. To address this issue, an easy-to-implement framework is developed for the push-based gossip protocol, where each node randomly selects a subset of neighbors for information exchange. Furthermore, a token bucket protocol is employed, where a sensor can transmit information to its selected neighbors only if the tokens stored in the bucket are sufficient to meet the transmission requirement. Specifically, the token consumption is determined by the number of neighbors randomly selected under the gossip protocol. The primary objective is to construct state estimators capable of minimizing the upper bounds on the estimation error covariance matrices by determining appropriate gains at each time step. Finally, the effectiveness of the proposed algorithm is illustrated through two simulation examples.

In this article, the distributed filtering issue is explored for time-varying state-saturated systems affected by Byzantine attacks over sensor networks. To regulate data transmission, a token bucket protocol (TBP) is utilized, in which the stochastic nature of token consumption arises from variations in packet sizes. Particularly, the measurements are transmitted to the filter only when the available tokens suffice to meet the required consumption. A Byzantine attack model is formulated in which Byzantine nodes arbitrarily alter the measurement signals transmitted to neighboring nodes. The primary objective is to construct an upper bound of the filtering error covariance (FEC) and to compute suitable filter gains by minimizing this bound. Furthermore, the boundedness of the proposed filtering error dynamics is rigorously analyzed via matrix-based theoretical analysis. Finally, numerical simulations are conducted to verify the effectiveness of the proposed algorithm.

In this article, the secure consensus control problem is investigated for discrete-time multiagent systems (DTMASs) subjected to denial-of-service (DoS) attacks, where a model-free controller is proposed based on the Q-learning (QL) method. In an attack-free case, a dynamic decaying encryption key is designed to enable secure state transmission over communication channels through quantization-based encryption to prevent unauthorized access. Under DoS attacks, the system switches to a safe mode that partially halts communication, where the key transforms into a local scaling parameter to prevent quantizer saturation through dynamic expansion. The QL-driven algorithm is introduced to autonomously synthesize control gain matrices, without requiring system dynamics models. Moreover, sufficient conditions on the frequency and duration of DoS attacks are derived to ensure that the model-free controller guarantees consensus in DTMASs. Finally, simulation studies involving highly maneuverable aircraft technology vehicles (HiMATVs) are conducted, demonstrating that DTMASs equipped with the proposed approach exhibit significantly enhanced attack resistance compared to conventional methods.

This article examines the recursive estimation issue for a class of complex networks that incorporates decode-and-forward (DaF) relays and energy harvesting (EH) techniques. The random intercoupling topologies are captured by Gaussian noise. Owing to the insufficient transmission capacity of sensors, DaF relays are implemented to connect sensors with remote estimators, augmenting the transmission range and improving communication quality. The energy required for signal transmission can be supplied through EH techniques deployed at sensors and relays. This study focuses on designing a recursive state estimator aimed at guaranteeing accurate estimation performance. An upper bound for the estimation error covariance matrix is formulated via two recursive equations, and subsequently minimized by properly designing the estimation gain. Moreover, the developed estimator is evaluated through detailed theoretical analysis, with emphasis on its uniform boundedness and monotonic behavior. Simulated examples confirm the efficacy of the underlying distributed estimator.

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.

This paper deals with the tracking control problem for a type of nonlinear networked systems by utilizing the data-driven control algorithm. A scalar-uniform-quantization-based two description coding mechanism is employed, with the hope to alleviate communication bandwidth limitation and packet dropouts phenomenon during data transmission. Such a mechanism first encodes the source signal into two descriptions, and then send the coded information to the decoder via independent network channels. The random packet dropouts phenomenon is considered that is described via Bernoulli sequences. A data-driven control scheme is designed, ensuring that the system output can track the desired reference. By resorting to the dynamic linearization method in combination with certain convex optimization technique, the desired control protocol is established. Finally, the usefulness of our designed control approach is demonstrated via two examples.Note to Practitioners—Networked systems are composed of sensors, actuators and controllers connected through a shared digital communication network, which have advantages in long-distance control operations. It should be noted that, however, during network communication, data packets would probably suffer dropout due to various reasons such as limited bandwidth, environment abrupt change, device aging and so on, which will degrade system performance or even damage the system. However, so far, in the context of data-driven control, where data play an essential role, such a data-lost phenomenon has not been fully examined when designing the control strategies. Therefore, the main motivation of this paper is to study the tracking control problem of networked systems with data packet dropouts. In this work, we provide an efficient control algorithm that enables the system to track the desired target in spite of packet loss. The core of this method is to transmit data through two independent network channels so as to reduce the probability of the transmission information being completely destroyed at the same time. We show the feasibility and efficiency of our data-driven control scheme on basis of two description coding mechanism by simulation. In future research, we will extend the method to target tracking in multi-sensor networked system subject to data loss.

Data augmentation (DA) has the potential to address the issue of imbalanced and insufficient datasets (I&ID) in pipeline fault diagnosis. However, the majority of existing DA methods for time series are inspired by computer vision techniques, ignoring the temporal dynamic properties and fine-grained fault features, which leads to limited performance of the augmentation. To tackle this problem, we introduce a novel DA approach called the subdomain-alignment adversarial self-attention network (SA-ASN), which takes into account both temporal association and semantic correlation. Our approach features a novel temporal association learning (TAL) mechanism, which transfers temporal information from the discriminator to the generator via a customized knowledge-sharing structure, improving the reliability of synthetic long-range associations. Additionally, we introduce a prototype-assisted subdomain alignment (PASA) strategy that forms a hierarchical structure in the synthetic dataset by incorporating local semantic correlation into the model training. With the support of TAL and PASA, our SA-ASN algorithm enhances the authenticity of temporal structure at the instance level and improves the discriminability of fault features at the category level. Our experimental results show that the SA-ASN algorithm provides a more diverse and accurate augmentation of pipeline data. The effectiveness of our SA-ASN algorithm encourages the use of data-driven diagnostic models in complex real-world oilfield pipeline networks.

A new distributed filtering problem is fully studied in this article for time-varying systems over sensor networks under measurement errors and measurement censoring, where the measurement error is modeled as stochastic sensor bias driven by a dynamical equation and the measurement censoring is described by the Tobit measurement model. To reduce data congestion and transmission burden, a weighted try-once-discard protocol (WTODP) is applied to transmission channels to efficiently orchestrate data communication. The transmission priority is determined in a dynamical way depending on the importance of missions. The aim of this article is to construct an optimal distributed Tobit Kalman filter (TKF) such that filter parameters are rigorously determined in the minimum mean squared error sense under the consideration of the bias, censoring and WTODP effects. Specifically, sparsity of the network topology is comprehensively considered by using the novel matrix simplification technique. Furthermore, the resultant filtering error is ensured to be exponentially bounded in the mean squared sense. Finally, an illustrative example is used to show the applicability of the proposed filter.

This article investigates the ultimately bounded proportional–integral–derivative (PID) control problem for a class of discrete-time Takagi–Sugeno fuzzy systems subject to unknown-but-bounded noises and protocol constraints. The signal transmissions from sensors to the remote controller are realized via a communication network, where the FlexRay protocol is employed to flexibly schedule the information exchange. The FlexRay protocol is characterized by both the time- and event-triggered mechanisms, which are conducted in a cyclic manner. By using a piecewise approach, the measurement outputs affected by the FlexRay protocol are established based on a switching model. Then, a fuzzy PID controller is proposed with a concise and realizable structure. To evaluate the performance of the controlled system, a special time sequence is introduced that accounts for the behavior of the FlexRay protocol. Subsequently, a general framework is obtained to verify the boundedness of the closed-loop system, and then, the controller gains are designed by minimizing the bound of the concerned variables. Finally, a simulation study is conducted to validate the effectiveness of the developed control scheme.

Abstract In this paper, a new Multi‐Focus Image Fusion (MFIF) method based on multi‐source joint layering and Convolutional Sparse Representation (CSR) is proposed. Based on the characteristics of multi‐focus source images, a multi‐source joint layering regularization model was designed to divide the sources into a common base‐layer and respective focus detail‐layers. This strategy can overcome the defects caused by source layering separately effectively. In detail‐layer fusion, CSR was employed to extract and global features. It can avoid detail blur and high computational cost caused by image blocking in the conventional sparse representation model. The proposed detail‐layer fusion rule combined the CSR coefficient maps pairwise with the window based select‐max rule. In the experiments, the optimal layering parameter was selected by experiments at first, and then five recently proposed specific MFIF or general image fusion algorithms were contrasted with the proposed method by plenty of subjective and objective experimental comparisons. The experimental results demonstrated the superiority of the authors’ method.

In this article, an adaptive learning framework is established for a deep weighted sparse autoencoder (AE) by resorting to the multiobjective evolutionary algorithm (MOEA). The weighted sparsity is introduced to facilitate the design of the varying degrees of the sparsity constraints imposed on the hidden units of the AE. The MOEA is exploited to adaptively seek appropriate hyperparameters, where the divide-and-conquer strategy is implemented to enhance the MOEA's performance in the context of deep neural networks. Moreover, a sharing scheme is proposed to further reduce the time complexity of the learning process at the slight expense of the learning precision. It is shown via extensive experiments that the established adaptive learning framework is effective, where different sparse models are utilized to demonstrate the generality of the proposed results. Then, the generality of the proposed framework is examined on the convolutional AE and VGG-16 network. Finally, the developed framework is applied to the blind image quantity assessment that illustrates the applicability of the established algorithms.

This article focuses on the consensus control problem for a class of time-varying stochastic nonlinear time-delay multiagent systems (MASs) attacked by deception attacks. The stochastic deception attack is considered in the procedure of propagating measurement information among agents. To solve the consensus control problem for addressed MASs under stochastic deception attacks, a definition of quasi-consensus is put forward. The objective of our investigation is to devise a consensus protocol to drive all agents to stay within an allowable range despite the existence of stochasticity and external malicious attacks. With the help of recursive linear matrix inequality and stochastic analysis methods, sufficient conditions are acquired to guarantee that all agents are constrained in the desirable range. Subsequently, an optimization algorithm is presented, which is to seek the locally optimal allowable distance among agents. Finally, a simulation example is presented to demonstrate the availability of our proposed algorithm.

In this paper, the input-to-state stabilization problem is investigated for a class of nonlinear delayed systems with exogenous disturbances. The model under consideration is general that covers for both mixed time-delays and Lipschitz-type nonlinearities. An observer-based controller is designed such that the closed-loop system is stable under an event-triggered mechanism. Two separate event-triggered strategies are proposed in sensor-to-observer (S/O) and controller-to-actuator (C/A) channels, respectively, in order to reduce the updating frequencies of the sensor and the controller with guaranteed performance requirements. The notion of input-to-state practical stability is introduced to characterize the performance of the controlled system that caters for the influence from both disturbances and event-triggered schemes. The estimates of the upper bounds of the delayed states and two measurement errors are employed to analyze and further exclude the Zeno behavior resulting from the proposed event-triggered schemes in S/O and C/A channels. The controller gain matrices and the event-trigger parameters are co-designed in terms of the feasibility of certain matrix inequalities. A numerical simulation example is provided to illustrate the effectiveness of theoretical results.

This paper is concerned with the synchronization analysis and control problems for a class of nonlinear discrete-time stochastic complex dynamical networks (CDNs) consisting of identical nodes. The discrete-time stochastic dynamical networks under consideration are quite general that account for asymmetric coupling configuration, nonlinear inner coupling structures as well as nonidentical exogenous disturbances. By resorting to both the error bound and the synchronization probability, a notion of quasi-synchronization in probability is first introduced to assess the synchronization performance of the addressed CDNs. An event-triggered pinning feedback control strategy is adopted to control a small fraction of the network nodes with hope to reduce the frequency of updating and communication in the control process while preserving the desired dynamical behaviors of the controlled networks. By using the Lyapunov function method and the stochastic analysis techniques, a general framework is established within which the problems of dynamics analysis and controller synthesis are solved for the closed-loop stochastic dynamical networks. Two numerical examples and their simulations are presented to illustrate the effectiveness and the usefulness of our theoretical results.

In this paper, the distributed set-membership filtering problem is investigated for a class of discrete time-varying system with an event-based communication mechanism over sensor networks. The system under consideration is subject to sector-bounded nonlinearity, unknown but bounded noises and sensor saturations. Each intelligent sensing node transmits the data to its neighbors only when certain triggering condition is violated. By means of a set of recursive matrix inequalities, sufficient conditions are derived for the existence of the desired distributed event-based filter which is capable of confining the system state in certain ellipsoidal regions centered at the estimates. Within the established theoretical framework, two additional optimization problems are formulated: one is to seek the minimal ellipsoids (in the sense of matrix trace) for the best filtering performance, and the other is to maximize the triggering threshold so as to reduce the triggering frequency with satisfactory filtering performance. A numerically attractive chaos algorithm is employed to solve the optimization problems. Finally, an illustrative example is presented to demonstrate the effectiveness and applicability of the proposed algorithm.

This paper deals with the consensus control problem for a class of nonlinear discrete time-varying stochastic multiagent systems (MASs) over a finite horizon via static output feedback. The measurement output available for the controller is not only from the individual agent itself but also from its neighboring ones according to the given topology. The nonlinearities described by statistical means can encompass several classes of well-studied nonlinearities in the literature. A new index of mean-square consensus performance, which quantifies the deviation level from the state of individual agent to the average value of all agents' states, is proposed to reflect the transient consensus behavior of the MAS. The purpose of the addressed problem is to design a time-varying output feedback controller such that: 1) the H∞consensus performance defined over a given finite horizon is guaranteed with respect to the additive noises and 2) at each time step, the mean-square consensus performance satisfies the prespecified upper bound constraint. By using a set of recursive matrix inequalities, sufficient conditions are derived for the existence of the desired control scheme for achieving both H∞and mean-square consensus performance requirements. Finally, a simulation example is utilized to illustrate the usefulness of the proposed control protocol.