This study addresses the safe consensus tracking issue for a specific category of multiagent systems (MASs) featuring a static directed communication graph. Each follower agent is subject to external disturbances and governed by unknown pure-feedback nonaffine dynamics. To facilitate the back-stepping approach in nonaffine systems, the mean value theorem (MVT) is employed. Additionally, dynamic surface control (DSC) is implemented to mitigate the intricacies typically encountered in back-stepping frameworks. For the approximation of the unknown nonlinearities, radial basis function neural networks (NNs) are utilized. Integrating these methodologies with principles from graph theory and barrier Lyapunov functions (BLFs), we propose a tailored neuro-adaptive distributed control scheme. The objective of this scheme is to ensure that followers can accurately track the leader’s path while maintaining the globally uniformly bounded (GUB) property of all system signals within the closed loop. Comparative simulation results demonstrate the effectiveness and superiority of the proposed control method.

This paper investigates the distributed secure fusion estimation problem under stochastic nonlinear hybrid attacks. Specifically, this work analyzes a hybrid attack scenario where the attacker employs a randomized approach to launch false data injection (FDI) attacks and Denial-of-Service (DoS) attacks on the measurement information communication channel. Then, an innovative distributed secure fusion estimation model is proposed, addressing three situations: DoS attacks, FDI attacks, and the absence of attacks. Following this, an existence condition is derived for the secure fusion estimator, utilizing probability-guaranteed set-membership filtering technology, to ensure that the fusion estimation error will consistently be bounded within an expected ellipsoid with the specified probability. Subsequently, a convex optimization problem involving constrained recursive matrix inequalities is formulated to compute the secure fusion estimation weight matrices. Finally, the effectiveness of the proposed probability-guaranteed set-membership secure fusion estimation (SSFE) algorithm is demonstrated through a simulation example. Note to Practitioners—The research in this paper is dedicated to addressing the problem of fusion state estimation in practical engineering tasks such as intelligent transportation, industrial manufacturing and military defense. With the increase in application requirements and process accuracy, the majority of projects demand that the true state must be bounded within a certain range, e.g., unmanned vehicle obstacle avoidance and missile precision strikes. To overcome this challenge, probability-guaranteed set-membership filtering is introduced to ensure that the fusion estimation error is bounded with a certain probability. However, due to the expanding scope of engineering applications, the system may be deployed to perform tasks in a non-secure environment, which increases the risk of malicious attacks that may lead to functional failures as well as performance degradation. Therefore, this paper simultaneously considers the scenario where the communication channels are subjected to the stochastic hybrid attacks, which can be effectively handled by utilizing the proposed probability-guaranteed SSFE algorithm. Preliminary simulations demonstrate the feasibility of the algorithm. Since the actual system may also encounter problems such as sensor energy constraints, bandwidth resource limitations, and data processing asynchrony. Therefore, in our future work, we will focus on addressing the various limitations present in the fusion estimation system and improving their resolution.

In this paper, the issues of tracking and synchronization for connected nonlinear servo system is considered. An adaptive neural network (NN) observer-based synthesize strategy is proposed. Firstly, a mathematical model of connected nonlinear isomorphism multi-motor servo system with disturbance is established, where the motors are connected through wired or wireless networks. Then, an adaptive disturbance observer based on sliding-mode is proposed, and the finite time convergence of observation errors has been proven. Thirdly, an adaptive NN tracking strategy is designed, and we have proved the tracking error is bounded and the full-state asymmetric constraints are satisfied. Furthermore, a synchronous control technique on sliding-mode is presented, and both the boundedness of synchronous error and reachability of sliding surface are verified. Finally, a numerical simulation and a semi-physical simulation are carried out to illustrate the validity of proposed methods.

This paper addresses the problem of designing probability-guaranteed set-membership secure estimation algorithms for energy-constrained multi-sensor systems with multi-rate asynchronous samplings. An event-triggered strategy (ETS) is employed to minimize data transmission overhead while maintaining estimation accuracy by transmitting only essential data. A novel measurement model is proposed to accurately characterize the operation of the multi-sensor system under ETS, taking into account both high- and low-energy transmission (HLET) modes and random denial-of-service (DoS) attacks, which impact communication energy consumption and data security. To cope with the challenges posed by uncertain sampling periods, a new fusion estimation model is established, including a redefined fusion estimation weight matrix and the formulation of a probability-guaranteed set-membership secure fusion estimation algorithm. Furthermore, a recursive optimization algorithm based on linear matrix inequalities is utilized to determine the minimum ellipsoid of the design parameters. The effectiveness of the proposed algorithm is validated through simulation studies.

This article presents a comprehensive theoretical framework for addressing the problem of secure fusion estimation in energy-constrained multisensor systems, specifically targeting hybrid attacks in multiple transmission levels. The lifespan of sensor nodes is constrained by the availability of energy supply, and all the sensors have the flexibility to choose between high-energy or low-energy levels for transmitting their measurements. Sensor data becomes vulnerable to malicious tampering when operating in a low-energy level, whereas the high-energy transmission level enables accurate data transmission. By introducing a set of Bernoulli random variables and ternary random variables, a novel measurement model is proposed to characterize scenarios involving both dual-energy transmission modes and hybrid attacks, including three statuses: safe, deception attacks, and Denial-of-Service attacks. Based on the innovation analysis approach, local secure estimators are designed to ensure that the estimation errors are minimized locally. Then, an optimal secure fusion algorithm is provided to generate the final estimated value by fusing all the local estimates. Additionally, the proposed secure fusion estimation algorithm's stability and steady-state properties are investigated. Finally, two simulation cases are conducted to provide the empirical evidence of the superior performance of the proposed approach.

This article investigates the secure control of a class of servo DC motors in the presence of input–output disturbances and DoS attacks. A multichannel observer-based switchingH∞ control strategy is proposed and a dynamic event triggering mechanism (DETM) is designed to save network resources. First, a mathematical model of servo DC motor containing input–output disturbances is developed and discretized to make it more suitable for computer control. Then, a state observer and a multichannel transmission strategy based on Markov theory are designed in order to obtain the accurate knowledge of disturbed system and transmit it to the remote controller under DoS attack. Third, observer-based state feedback switchingH∞ control strategy is proposed and the stability is demonstrated. Furthermore, the DETM is presented to reduce the occupation of network resources by introducing dynamic trigger variable. Finally, the performance of the characterized control strategy is verified by a numerical simulation and a semi-physical simulation.

In this article, an event-triggered sliding mode predictive control method for remote motor systems under Denial of service (DoS) attacks is investigated. First, an adaptive reaching law with integral sliding mode strategy is designed. The stability of the designed sliding mode strategy is demonstrated. Second, a security control generator based on sliding mode predictive control is proposed, which releases predictive control sequence to reduce the impact of sensor signal interruption caused by DoS attacks on system stability, and its stability is proved. Further, an event-triggered mechanism with better-dynamic performance is proposed by introducing dynamic loss factor. Finally, the effectiveness of the designed method is demonstrated by a simulation experiment and a semi-physical simulation together.

The consensus tracking problem means that a group of followers tracks the desired trajectory with local communication. In this article, partial components of cluster consensus have been considered. In this scenario, the$p$components of the followers in different clusters track the leader at different lag times, while$p$components of each agent in the same cluster reach a consensus, which is called$p$components of cluster-lag (PCCL) consensus. By using a seminorm$\|x_{i}\|_{2,p}$and a Lyapunov–Krasovskii functional, PCCL consensus for second-order multiagent systems with homogeneous nonlinear systems on cooperative–competitive networks has been considered. For the case that the communication network graph is undirected, a decentralized adaptive controller, which is based on the exchanged neighbors’ information from the same cluster, is designed such that all the agents reach PCCL consensus. For the directed graph case, an adaptive protocol based on the intracoupling strength is constructed for each cluster to achieve PCCL consensus. Finally, two simulation examples are illustrated to show the effectiveness of the proposed control protocols.

The capacity of combined gauge-satellite precipitation estimates largely depends on the characteristics of the input data such as the number, location and reliability of rain gauges, and satellite-derived precipitation quality. The objective of this study is to examine the influence of rain gauge network configuration including density and spatial distribution on the performance of the gauge-satellite merging estimation at monthly and ten-day temporal scales. Dense rain gauge observations and satellite-derived precipitation data (i.e., TMPA 3B42 Version 7 and Version 06 IMERG Final Run) in two provinces of China are used. A two-stage downscaling-integration approach is applied in the gauge-satellite precipitation estimation. Various scenarios of rain gauge density and combination are designed and their corresponding merged precipitation estimates are evaluated using statistical indices. The merged results using the TMPA and IMERG precipitation product, respectively, are compared. The results show that: 1) the influence of rain gauge network configuration on the gauge-satellite merged precipitation estimates gradually decreases with the increase in rain gauge density, and the gauge-satellite merged precipitation estimates are more sensitive to the rain gauge network density in wet season and ten-day temporal scale than in dry season and monthly scale, respectively and 2) the merged precipitation estimation using the IMERG precipitation data generally outperforms the estimation using TMPA precipitation data in the low gauge density scenarios, and the gap decreases with the increase in the rain gauge network density. In the areas with sparse rain gauges, improving the quality of satellite precipitation data would significantly improve the performance of the gauge-satellite merging estimation.

The ambiguity of name entities is a common problem in information retrieval, which leads to the decline of retrieval quality. This makes name disambiguation particularly important. In academic field, the rapidly increasing large-scale of publications has imposed more challenges to the name disambiguation problem. Existing works mainly focus on leveraging content information to distinguish different name entities. In this paper, we consider jointly utilizing both content information and relational information to disambiguate the same name. Firstly, we construct a Heterogeneous Academic Network based on meta information of publications such as collaborators, institutions and venues. Then, we transform the network into separate homogeneous graphs. After that, we propose Graph Attention Networks to jointly learn content and relational information by optimizing an embedding vector. Finally, a clustering algorithm is presented to gather author names most likely representing the same person. The experiments show that our method is effective and outperforms the state-of-the-art methods in both precision and recall metrics.

This article addresses the visual regulation problem of a differential-drive mobile robot with an arbitrarily installed monocular camera in the indoor environment. A three-stage controller is designed by using a novel nonadaptive switching approach, where the unknown image depth and the uncalibrated camera-to-robot translation parameters do not need to be estimated. Convergence of the error systems with the designed controller in each stage is analyzed. Moreover, the existence of the switching time instants from each stage is proved. The simulation results are presented to show the effectiveness of the proposed approach.

This paper is concerned with the distributed estimation problem in sensor networks subjected to unknown attacks. Network attacks are considered to exist in two classes of channels: 1) communication channels from the plant to sensors and 2) communication channels among sensors. The status of an attack is viewed as a stochastic phenomenon, and the transmitted information will be affected when the attacker successfully carries out an attack on the related data packet. Based on the sensors' own measurements and their neighbors' local information, a novel distributed estimation model against two-channel stochastic attacks is presented. A sufficient condition on the existence of the desired distributed H∞estimators is derived and the distributed estimator gains are designed by solving a linear matrix inequality. Two illustrative examples are provided to demonstrate the effectiveness of the new design techniques.

This paper is concerned with the set-membership estimation problem for complex networks subject to unknown but bounded attacks. Adversaries are assumed to exist in the nonsecure communication channels from the nodes to the estimators. The transmitted measurements may be modified by an attack function with added noise that is determined by the adversary but unknown to the estimators. A novel set-membership estimation model against unknown but bounded attacks is presented. Two sufficient conditions are derived to guarantee the existence of the set-membership estimators for the cases that the attack functions are linear and nonlinear, respectively. Two strategies for the design of the set-membership estimator gains are presented. The effectiveness of the proposed estimator design method is verified by two simulation examples.

In this paper, we consider a periodic estimation problem in sensor networks with a shared communication channel. The transmission constraint is inevitable in a single-channel-based sensor network if the sensors are heterogeneous or deployed far away from each other. A novel stochastic competitive transmission strategy is presented to deal with the transmission constraint, such that the sensors communicate with the fusion center (FC) in a strict asynchronous manner. A periodic mixed storage strategy combing the zero-input and the hold-input mechanisms is presented to describe periodic updating of the stored information in the sensors' buffers. A recursive Kalman filtering algorithm is derived for the FC to periodically generate estimates of state variables describing an object by using a linear continuous-time stochastic model. Two simulation examples are presented to show the effectiveness of the proposed results.

This paper is concerned with the state estimation problem for a class of nonlinear cyber-physical systems (CPSs) where the nonlinear dynamical physical process is approximated by a Takagi-Sugeno fuzzy model. The physical plant is measured by a set of wireless sensors and the sensors communicate with the remote estimator via a communication channel. In the considered CPS, the randomly occurring sensor saturation, signal quantization, packet dropouts as well as the medium access constraint are studied in a unified framework. We develop a sufficient condition such that the filtering error system is asymptotically stable in the mean-square sense and also with a prescribed H∞performance level. The filter gain parameters are determined by solving a convex optimization problem. Finally, the simulation study on the networked truck-trailer system is presented to show the effectiveness of the proposed estimator design.

This study investigates the signal estimation problem in noisy sensor networks with quantised communications. The sensors are subject to random sensor failures, and synchronously take noisy measurements to produce local estimates by using a Kalman filtering scheme at each sampling instant. A quantiser is considered to be embedded in each sensor, and the probabilistic quantisation strategy is adopted to reduce the energy consumption. In between two sampling instants, each sensor collects quantised local estimates from its neighbours and runs a consensus‐based fusion algorithm to generate a fused estimate. The process noises and measurement noises are considered to be spatially uncorrelated, a recursive equation is presented to calculate the estimation error covariance matrix and an upper bound is derived for the estimation performance index. Moreover, a sufficient condition for the convergence of the upper bound of the estimation performance index is also presented. Two types of optimisation problems are constructed for cases of infinite and finite recursions, respectively, where the former one focuses on minimising the derived upper bound of the estimation performance index, and the latter one aims to minimise the energy consumption subject to a constraint on the estimation performance. Illustrative examples are provided to demonstrate the effectiveness of the proposed theoretical results.

This letter investigates the hierarchical fusion estimation for clustered sensor networks. The sensors within the same cluster are connected to a local estimator, and all the local estimators are linked with a fusion center. The fusion center and the local estimators are not required to be synchronous. During each estimation interval, the sensors are allowed to communicate with the local estimator several times. A minimum variance estimation algorithm is presented for each cluster to aperiodically generate local estimates. A covariance intersection fusion strategy is presented for the fusion center to generate fused estimates by using asynchronous local estimates and previous fused estimates, without knowing the cross-covariances among the local estimation errors.

This paper is concerned with the design of networked multi-sensor fusion estimation system (NMFES). The Kalman filtering problem is considered for the NMFES with random observation delays, packet dropouts and missing measurements caused by sensor failures. For each observation subsystem, the sensor failure phenomenon is described by a Bernoulli distributed white sequence with a known conditional probability, and the packet dropout phenomenon and randomly delayed measurements are described by multiple binary random variables. Without resorting to the augmentation technique, an optimal recursive fusion filter for NMFES is obtained in the linear minimum variance sense by using the innovation analysis method. The dimension of the designed filter is the same to the original system, which can help reduce computation costs as compared with the augmentation method. Moreover, the performance of the designed Kalman filter is dependent on the missing rates of the measurements, the upper bounds of random delays and the occurrence probabilities of delays. Finally, the effectiveness of the proposed results is demonstrated by an illustrative example.