This paper investigates the problem of a robust diffusion Kalman filter in a nonlinear system within multi-sensor networks. To address the challenge of model mismatch caused by unmodeled dynamics or external disturbances, a novel robust diffusion Kalman filter (RDKF) algorithm is proposed. Conventional DKF algorithms typically suffer from significant performance degradation when system models deviate from actual dynamics, due to their reliance on accurate model assumptions. To overcome this limitation, a model mismatch compensation mechanism is incorporated into the proposed RDKF, which actively corrects estimation failures induced by inaccurate models. Furthermore, to release the additional network communication burden introduced by the compensation mechanism, two selective neighbor communication strategies are developed, which are the Round-Robin-inspired (RR-inspired) periodic selection strategy and the optimal selection strategy preserving estimation quality. In addition, the theoretical analysis about the mean and mean-square stability is established, and the equivalence between local and global estimation stability is demonstrated, which guarantees the effectiveness of the designed global compensation mechanism for both global and local estimates. Finally, two simulation examples on target tracking are presented to demonstrate the performance comparison between the proposed RDKFs and the conventional DKF approaches in both linear and nonlinear systems.

This paper investigates the robust state estimation problem for non-cooperative target tracking to overcome the limitations of conventional tracking algorithms under model uncertainty and noise uncertainty. Although some existing algorithms employ the maximum correntropy criterion (MCC) to handle non-Gaussian noise, most of them generally assume precise state-space models and neglect the effect of uncertain noise parameters as well. To deal with this limitation, a robust fuzzy adaptive MCC-based unscented Kalman filter (UKF) algorithm is proposed, which enhances robustness through the simultaneous state and unknown input estimation (SSUIE), and adjusts the noise covariance via a fuzzy inference system (FIS) adaptively. First, an unknown input (UI) estimator is integrated into the MCC-based UKF to tackle the challenge of model uncertainty, thereby eliminating the reliance on a precise state-space model. Then, an FIS is employed to adjust noise covariance, which explicitly accounts for uncertain noise statistics, improving adaptability in challenging conditions. Notably, the FIS-based adjustment is a more efficient and flexible method compared to the interacting multiple model (IMM) and variational Bayesian (VB) methods, as it avoids the need for precise prior knowledge and high computational complexity. Finally, the simulation results demonstrate that our proposed algorithm can enable effective tracking of non-cooperative targets, even during orbital maneuvers and under uncertain noise conditions. This work provides an adaptive solution for enhancing estimation robustness against uncertain target behaviors and dynamic environments, with potential applications in autonomous systems and security defense systems.

In this article, we investigate a Euclidean-distance-based distributed constrained optimal formation matching (EDCOFM) problem for open large-scale multiagent systems (OLSMASs), where the number of agents is large and variable. To address the open property of the multiagent system, we introduce the concept of a depository, which can provide additional agents or store redundant agents. When the number of agents is sufficient to achieve the formation configuration, a distributed formation matching algorithm for a large-scale multiagent system (DFMA-LSMAS) is proposed to search for the optimal location of the formation configuration within a designed constraint and the optimal matching relationship. Notably, the framework is applicable to open multiagent systems. When the number of agents is smaller than the requirement to achieve the formation configuration, and more than one agent needs to be provided from the depository, an unmatched phenomenon occurs, which results in the failure of the proposed algorithm. To address this, a disturbance-based approach is proposed to eliminate the phenomenon without impacting the optimal solution. When the number of agents is larger than the requirement to achieve the formation configuration, and some agents have to leave the system, a fair competition mechanism is proposed to selectthe remainder and their optimal matching relationship. This mechanism avoids multiple competitions in a centralized manner. Finally, several simulation results are provided to verify the proposed algorithms.

This paper focuses on the fully distributed dynamic event-triggered optimized backstepping (OB) containment control for a class of stochastic nonlinear high-order strict-feedback multiagent systems (MASs) based on reinforcement learning (RL) algorithm. Firstly, an OB control scheme is proposed using the critic-actor structure. In this scheme, neural networks are applied to approximate the value function and the controllers are derived from the gradients of simple positive functions. This approach simplifies the algorithm and effectively addresses two fundamental requirements commonly encountered in RL-based optimal control: known dynamics and persistent excitation. Furthermore, the static and dynamic event-triggered mechanisms are designed in order to reduce the control actions in the highest layer for nonlinear high-order stochastic MASs. Then, the distributed event-triggered control protocols based on RL are proposed for the strict-feedback nonlinear MASs with stochastic disturbances. Finally, simulation results are shown to demonstrate the effectiveness of the theoretical analysis.

Cooperative adaptive cruise control (CACC) systems face critical cybersecurity challenges due to vulnerabilities in vehicle-to-vehicle (V2V) networks and onboard sensors. Covert attacks have become a critical research focus in CACC systems due to their inherent risks and stealthiness. In this article, a novel attack model called covert coordinated attack (CCA) is proposed, and its detection and isolation schemes are proposed to enhance the resilience of CACC systems against such an attack. First, the specific CCA strategies are proposed by revealing dual-channel attack coordination mechanisms targeting both sensors and V2V networks. Second, the detection framework based on unknown input observers (UIOs) is designed by monitoring the residual-based health characteristic. Third, based on the established two auxiliary systems, an attack isolation strategy integrating UIOs and extended state observers (ESOs) is proposed for attack reconstruction and system performance recovery. Finally, numerical simulations are conducted to validate both the stealthiness of CCA and the effectiveness of the proposed attack detection and isolation methods.

This article investigates the adaptive resilient flexible output containment (FOC) control problem for semi-Markov jump fully heterogeneous multiagent systems (FHMASs) under random switching topologies and denial-of-service (DoS) attacks. In contrast to most existing containment control results, the proposed control strategy can address the challenges posed by the full heterogeneity of multiagent systems (MASs), particularly when multiple leaders exhibit different system dynamics. To better reflect real-world MASs and communication networks, multiple asynchronous semi-Markov chains are employed for the first time to capture system parameter variations and communication topology switching, incorporating generally uncertain transition rates (TRs). In order to deal with this problem, a novel adaptive observer-based FOC control framework is developed. First, by introducing an adaptive gain, the adaptive resilient observers can observe leaders' states without prior knowledge of global topology information and TRs, while resisting the impacts of random switching topologies and DoS attacks. Then, a dynamic output feedback controller is designed to ensure the achievement of FOC. Notably, the containment coefficients in the controller design are no longer tied to the Laplacian matrix and can be flexibly predefined to align with specific task requirements. Furthermore, the linear matrix inequalities (LMIs) to obtain estimator gain matrices and controller gain matrices are derived for the case of generally uncertain TRs, respectively. Finally, the effectiveness of the theoretical method is demonstrated through the simulation.

This article proposes a novel fuzzy secure containment control strategy in an adaptive framework for fully heterogeneous nonlinear multi-agent systems (FHNMASs), which can resist the combined impacts of randomly switching topologies and denial-of-service (DoS) attacks. Unlike existing containment control protocols that rely on multiple leaders sharing the same system matrices, our approach is applicable to more general systems, where multiple leaders and followers are allowed to be equipped with non-identical system matrices and even state dimensions. The switching signal of communication graphs is regulated by the random Markov process, and the connectivity assumption is relaxed by merely requiring the union graph of possible subgraphs to be connected. Firstly, new adaptive observers are constructed, that can observe leaders' states without access to global topology information, even under randomly switching topologies and DoS attacks. Secondly, based on fuzzy logic systems and output regulation methods, fuzzy system state estimators are introduced to address challenges associated with unknown nonlinear functions and unmeasurable states of followers. Further, a new fuzzy controller is developed to guarantee the achievement of flexible secure output containment, where containment coefficients no longer depend on the Laplacian matrix and can be flexibly preset to accommodate various tasks. Finally, the theoretical algorithm is validated through simulation results.

This work proposes a memory fusion controller design methodology for sampled-data control of fractional-order (FO) systems with sliding memory window. Composed of finite-dimensional previous inputs, the devised controller is capable of handling hereditary effect and meanwhile enabling pseudo state to satisfy general integer-order (IO) discrete plant at sampling instants. Additionally, the asymptotical stability of controller and sampling error are further guaranteed. The developed fusion controller provides an "out-of-the-box" method for users who are not familiar with FO calculus and significantly facilitates the corresponding analysis. The above mentioned approach is thereafter employed in a more sophisticated case, that is, the coordination control of FO multiagent systems (MASs) subjects to intermittent sampled-data transmission. It is proved that the achievement of consensus only relates to the connectivity of communication graph. Numerical results are presented finally to substantiate the proposed control strategy.

This paper addresses the adaptive fuzzy faulttolerant output containment control of fully-heterogeneous nonlinear multi-agent systems (FHNMASs) subject to stochastic disturbances, where not only followers but also leaders are equipped with non-identical dynamics. Firstly, fully distributed fixed-time observers are designed to observe active leaders' information including system matrices, positions, and external inputs, which are only accessible to a limited group of followers. Subsequently, adaptive algorithms are utilized, informed by the observations of leaders' system matrices, to solve the regulator equations. Then, the state observer-based controller is proposed, which employs the adaptive fuzzy-based approximation law to approximate the unknown nonlinear item and integrates the adaptive gains to cope with the actuator faults in the follower's dynamics. Additionally, a set of modest conditions and rigorous proofs is constructed to guarantee the accomplishment of predetermined output containment through the output regulation method and Lyapunov stability theory. Finally, the theoretical analysis is validated through performing a numerical simulation.

This article addresses an enhanced distributed outlier-resilient fusion estimation problem using an interval type-2 (IT-2) Takagi–Sugeno (T–S) fuzzy model, integrating outlier detection schemes and dimensionality reduction (DR) strategies. First, the IT-2 T–S fuzzy model is employed to handle system uncertainty and nonlinearity effectively. Then, the outlier-resilient local estimator is proposed using the zonotope-based set-membership filters (ZSMFs), where the outlier detection scheme only relies on the intersection between the predicted set and the measurement set. Furthermore, the compressed local estimate (LE) are designed when there are bandwidth constraints in sensor networks, and a novel DR strategy is proposed to design this compressed LE, where the compression matrix is determined by the Round–Robin protocol (RRP). After this, based on the compressed LEs, a distributed resilient zonotopic fusion estimator (DRZFE) is derived by the matrix-weighted fusion method. Note that the computational load of the DRZFE is reduced effectively due to the zonotope order reduction and the RRP-based DR independent of the online optimization. Moreover, the compensation of outliers and the compensating state estimate of RRP-based DR may improve the resilience of the algorithm and reduce information loss. Finally, two numerical examples are provided to validate the advantages and effectiveness of the proposed methods, and we use root-mean-square-error as the indicator to assess the estimation accuracy.

Accurate state of charge (SOC) is crucial to achieving safe, reliable, and efficient use of batteries. This article proposes an adaptive neural network (NN)-based event-triggered observer to estimate SOC. First, a stochastic battery equivalent circuit model (ECM) is established, where an adaptive NN is employed to approximate the unknown nonlinear part. The learning process of network weight is conducted online to observe the variations of model parameters and avoid time-consuming processes for parameter extraction. Besides, for the purpose of saving computational cost, an event-triggered mechanism (ETM) is employed in the weight updating law, which means the weights only update when it is necessary. Then, an adaptive radial basis function (RBF) NN-based SOC observer is designed, and its stability is proven by the Lyapunov theory. Moreover, the strictly positive lower bound of interevent time is derived, and undesirable Zeno behavior can be excluded. Finally, the accuracy and robustness of the proposed observer are evaluated by experiments and simulations. Results show that the proposed method can estimate SOC accurately in the presence of initial deviation and sensor noises.

This article studies the impact of constrained optimal stealthy attacks on the state estimator, where man-in-the-middle attacks with a linear form can compromise innovations transmitted through a wireless network. First, a novel resource-constrained attack model is proposed, in which there are only a finite number of attack instants within a fixed interval. Second, the evolution of the estimation error covariance under attacks is obtained, and the covariance at the ultimate instant of the attack interval is regarded as the attacker’s cost function. Moreover, a relaxed condition of the strict stealthiness, named Kullback–Leibler divergence, is employed to describe the attacker’s the stealthiness metric. Third, the one-time and holistic optimization problems of stealthy attacks are solved by exploiting the Lagrange multiplier method. Then the constrained optimal attack strategies are obtained to produce the largest ultimate estimation error covariance. Finally, two simulation cases are provided to confirm the correctness of the designed attack strategies.

This article deals with the fully distributed pull-based event-triggered bipartite fixed-time output consensus problem of heterogeneous linear multiagent systems (HLMASs) with an active leader, whose information can be merely accessed by a small fraction of followers. First, a class of fully distributed fixed-time observers is proposed for each follower to estimate the leader's system matrices, position, and control input under the signed communication topology, respectively. Then, based on the estimations of leader's system matrices, two adaptive algorithms are given to solve the regulator equations. Furthermore, the fully distributed fixed-time observer-based controllers associated with state feedback and output feedback are, respectively, proposed by employing the pull-based event-triggered mechanism (ETM) where each agent merely updates controller at its own triggering instants. Correspondingly, some sufficient criteria and the rigorous proofs are provided to ensure the implementation of bipartite output consensus in fixed time by using the Lyapunov stability theory and fixed-time stability theory. Moreover, the strictly positive lower bounds of intervals between two adjacent event-triggered times are derived, which means the Zeno behavior is ruled out. Finally, numerical simulations are performed to demonstrate the theoretical analysis.

Accurate state of charge (SOC) can effectively improve safety performance and prolong the cycle life of the batteries. The widely used model-based SOC estimation methods have underlying assumptions of complete measurements and accurate estimator gains, which are not always reasonable in practical applications. Thus, this article designs a dual Kalman filter-type resilient filter to estimate SOC and parameter jointly with the random missing measurement phenomenon which is modeled by a Bernoulli distributed sequence. Besides, the filter gain variations, in both online parameter identification and state estimation, are characterized by mutually independent multiplicative noise terms. Then, based on the minimum-variance principle, the filter gains are designed to minimize the effects of the missing measurement and gain variations on the estimation performance. Finally, extensive simulations and experiments are conducted to validate the effectiveness and resilience of the proposed method.

This article presents several new α -passivity and α -finite-time passivity ( α -FTP) concepts for the fractional-order systems with different input and output dimensions, which are distinct from the concepts for integer-order systems and extend the existing passivity and FTP definitions to some extent. On one hand, we not only develop some sufficient conditions for ensuring the α -passivity of the multi-weighted fractional-order complex dynamical networks (MWFOCDNs) with fixed and adaptive couplings, but also discuss the synchronization for the MWFOCDNs based on the α -output-strict passivity ( α -OSP). On the other hand, the α -FTP for the MWFOCDNs with fixed and adaptive couplings are also studied on the basis of the designed state feedback controller, and the relationship between finite-time synchronization (FTS) and α -FTP for the MWFOCDNs is also illustrated. Finally, two numerical examples with simulation results are used to demonstrate the validity of the obtained criteria.

In this article, we establish an experience-correction (E-C) framework to deal with sampled-data fractional-order systems in the manner of general integer-order discrete-time model at sampling instants by coping with the memory effect. The designed E-C controller can be partitioned into local input and history input, respectively. Generated by current sampling, the local input is in a sense analogous to the input of integer-order system. In particular, the history input can correct all previous experience via incorporating earlier local inputs. Emphasize that the weights of earlier local inputs, norm of E-C controller together with corresponding sampling error are proved to descend to origin no slower than the Pareto function, i.e.,$\mathcal {O}(t^{-\alpha })$, where$\alpha $represents the derivative’s order. Similar to the well-known forgetting curve, we witness a rapid decline in the proportion of earlier past in E-C controller, which is with respect to one of the memory failures: transience, the deterioration of memory with the passage of time. The closer the order$\alpha $is to 1, the faster the forgetting is. The above analysis accounts for a phenomenon not previously understood: well-performed simulation results could still be expected in existing research who ignored memory effect and only considered local input. This is owing to the fractional orders are usually hypothesized to be larger than 0.5 and thus the weight of earlier past decays sharply over time. Furthermore, both absolute and proportional decline of earlier past’s weight will slow down when time goes by, which is consistent with memory consolidation and Jost’s law in psychology. Overall, the E-C method keeps an elegant formulation which theoretically describes the evolution of hereditary effect. Meanwhile, it is also suitable for first-order systems. Thereafter, we address related applications in fractional-order multiagent systems (MASs) subject to intermittent sampled-data communication. Numerical simulations are provided finally to substantiate the effectiveness of theoretical results.

This article deals with the fully distributed dual-terminal dynamic event-triggered bipartite output containment control of heterogeneous linear multiagent systems (HLMASs) subject to actuator faults. First, a class of fully distributed dynamic event-triggered observers is proposed over the directed signed communication network for each follower to estimate the leaders’ system matrices and (symmetric) combination states, which are merely available to a small fraction of followers. Then, based on the estimations of leaders’ system matrices, adaptive algorithms are employed to solve the regulator equations. Furthermore, the fully distributed observer-based dynamic event-triggered controllers are proposed by integrating the adaptive gains to compensate for actuator faults. Dual-terminal dynamic event-triggered mechanisms (ETMs) are addressed to exclude not only continuous control updates but also continuous communication among agents. Correspondingly, some mild criteria and the rigorous proofs are established to ensure the implementation of bipartite output containment through the Lyapunov stability theory. Moreover, the strictly positive lower bounds of intervals between two adjacent event-triggered time instants are derived, which indicates that Zeno behavior is ruled out in the dual-terminal dynamic ETMs. Finally, numerical simulations are performed to demonstrate the theoretical analysis.

The fully distributed consensus tracking of stochastic nonlinear multiagent systems (MASs) is investigated with Markovian switching topologies and intermittent control strategy, where the dynamics of agents are depicted by Itô differential equations and the leader’s information is just known for a fraction of followers. The switching mechanism of interaction topologies is modeled as a Markov process. A novel class of fully distributed control protocols is proposed via intermittent control method, which is only associated with the relative state measurements of neighbors and does not involve any global information. Meanwhile, the control gains are designed to be intermittently adaptive, which can effectively reduce energy consumption and avoid the gains being larger than those needed in practice. Several sufficient conditions and corresponding proofs are provided by using the Lyapunov stability theory. Finally, numerical simulation is presented to state the feasibility of the theoretical results.

In this article, the containment control problem of multiagent systems (MASs) with active leaders under a directed communication graph is addressed. A novel pull-based event-triggered protocol combined with aperiodic sampled-data mechanism is first presented, where each agent merely updates controller at its own triggering instants. Under the proposed control protocols, the information is only exchanged and calculated at the aperiodic sampling instants, therefore, it can reduce communication congestion and save computing resources. Furthermore, the Zeno behavior is naturally excluded because the trigger instants are in the set of aperiodic sampled instants. By virtue of the algebraic graph theory and Lyapunov stability analysis, it is shown that the active leaders can reach their desired states predefined arbitrarily and all followers are driven to the convex hull formed by the leaders. Finally, an illustrative example is given to validate the theoretical results and emphasize the advantages of the proposed protocols.

The group consensus problem for fractional-order multiagent systems is investigated in this paper. With the help of double-tree-form transformations, the group consensus problem of fractional-order multiagent systems is proved to be equivalent to the asymptotical stability problem of reduced-order error systems. A class of distributed control protocols and some simple LMI sufficient conditions as well as necessary and sufficient conditions are proposed in this paper to solve the group consensus problem for fractional multiagent systems. Moreover, pinning control strategy has been taken into consideration. It is shown that the system converges more rapidly when the designed pinning protocols are adopted. In addition, the case of fractional system with switching topologies is also discussed and some corresponding sufficient conditions are obtained. Finally, some simulation results are presented to illustrate the theoretical results.

This paper investigates the event-triggered consensus of linear multiagent systems with periodic data sampling mechanisms, where random packet losses are taken into account. The random packet losses occur in communication links based on a certain probability, and it is subject to the Bernoulli distribution. A novel distributed control protocol is designed based on the combined measurement to achieve the mean square consensus. By using the Riccati inequalities and linear matrix inequalities, an event-triggered condition with fewer parameters is also designed to reduce the information updating number. The interaction among the control gain matrix, sampling interval, and packet losses probability is used to describe the consensus conditions. The maximum sampling interval is presented explicitly. It is shown that the advantages of the proposed event-triggered strategy with the data sampling mechanism can avoid the Zeno behavior of the systems and continuous monitoring of the states. The simulations are provided to verify the proposed control strategy.

The fuzzy logic controller is used to synchronize the master-slave unified chaotic systems with uncertainties. The simulation results show that the error dynamics of the unified chaotic synchronization systems are regulated to zero asymptotically in shorter time in spite of the overall system is undergoing uncertainty and disturbance.