This article focuses on the scaled consensus problem of time-scale-type multiagent systems (MASs) under communication networks subject to denial-of-service (DoS) attacks. A dual-channel dynamic event-triggered mechanism (DETM) is proposed to reduce both actuator update frequency and communication overhead. By constructing a nonnegative function integrated with time-scale calculus theory, sufficient conditions are derived to guarantee the scaled consensus while excluding Zeno behavior. The conclusions derived from this method can be applied to MAS models in both continuous and discrete time domains. Finally, a numerical simulation and a case study on autonomous marine vehicles (AMVs) are presented to validate the effectiveness and feasibility of the proposed approach.

This work addresses the problem of predefined-time cluster lag synchronization for inertial neural networks. A dynamic event-triggered control scheme that incorporates a time-dependent exponential scaling function is proposed. Sufficient conditions are established to ensure the achievement of cluster lag synchronization within a prespecified time. In contrast to traditional Lyapunov-Krasovskii functional approaches that usually result in high-dimensional linear matrix inequalities, the criteria obtained in this paper are formulated as low-dimensional linear matrix inequalities that align with the dimension of the systems, which facilitates easy verification. Furthermore, an additional constraint is derived to preclude the occurrence of Zeno behavior in the closed-loop system. Finally, numerical simulation results are presented to validate the effectiveness of the proposed control strategy and the correctness of the theoretical derivations.

This paper develops a novel dynamic sampled-data-based event-triggered (DSET) secure fuzzy tracking control scheme for a category of unknown nonlinear heterogeneous multiagent systems (MASs). Asynchronous DSET mechanisms are developed, in which the triggering thresholds evolve dynamically with the system states. The proposed mechanisms only require discrete state sampling and triggering evaluation, thereby avoiding continuous monitoring and reducing triggering frequency. Moreover, the triggering interval is shown to admit a positive lower bound that is not solely determined by the sampling period. Under denial-of-service (DoS) attacks, distributed DSET observers are constructed to estimate the leader's system matrices and states, where only a subset of followers requires access to the leader information. A detection scheme is further designed to identify the termination of DoS attacks. Furthermore, a fuzzy fault-tolerant control scheme is proposed based solely on output measurements. The convergence of observations and tracking errors is rigorously established. Simulation results demonstrate the effectiveness and superiority of the proposed approach.

In this paper, the predefined-time synchronization of inertial neural networks with stochastic disturbances is investigated. First, an adaptive control strategy incorporating exponential scaling functions is designed to guarantee synchronization within a predefined time, independent of initial conditions and system parameters. Second, by combining Lyapunov stability theory with stochastic analysis techniques, sufficient criteria are derived to achieve predefined-time synchronization in the presence of stochastic perturbations. Unlike prior works, this paper explores the predefined-time synchronization while addressing the impact of stochastic disturbances. Moreover, the proposed results generalize previous findings on drive-response inertial neural networks to coupled inertial neural networks, offering a unified analytical framework. Finally, numerical simulations are performed to validate the theoretical results.

The interconnected RLC circuits are essential nonlinear models for analyzing dynamic coupling and energy transfer in complex electrical networks, serving as a standard testbed for nonsmooth control, time-delay analysis, and synchronization theory research. However, the existing theoretical studies exhibit prominent limitations: most works ignore the dimensional heterogeneity between drive Cresponse circuit subsystems and fail to integrate neutral time delays and Filippov nonsmooth characteristics in system modeling. Moreover, the coordination mechanism between dynamic event-triggered control and indefinite-function-based stability analysis remains underexplored, and incomplete Lyapunov derivative definiteness verification restricts the accuracy and generality of the existing synchronization criteria. Addressing these theoretical gaps, this article investigates the fixed-time (FxT) synchronization control of networked neutral Filippov systems with heterogeneous dimensions. A comprehensive system model is constructed to accommodate dimensional mismatch, neutral time delay, nonsmooth Filippov dynamics, and event-triggered control coupling. Specifically, a novel FxT stability lemma for indefinite functions is proposed to compensate for the defects of conventional Lyapunov analysis methods. Combined with nonsmooth analysis and state-space reconstruction, a rigorous synchronization error model is established for dimension-mismatched systems. Meanwhile, the static and dynamic event-triggered control strategies are designed to reduce network resource consumption, with the strict positivity of dynamic triggering functions theoretically proven. Finally, numerical simulations on mismatched RLC circuit systems validate the effectiveness and superiority of the proposed unified FxT synchronization framework.

This article proposes a novel distributed neuro-adaptive 3-D formation tracking control framework of multiple autonomous underwater vehicles (multi-AUVs) subject to marine environmental disturbances. On the one hand, we assume that all AUVs can obtain the real-time states. By introducing a series of variable transformations, the multi-AUV system is transformed into an underactuated nonlinear system with virtual control input. Radial basis function neural networks (RBFNNs), whose weights are updated online, are utilized to approximate nonlinear functions. Considering environmental disturbances, a virtual controller is designed such that all AUVs track the leader while maintaining the desired formation geometry. Then, the actual controller is given as an adaptive form according to the virtual control signals. On the other hand, we assume that all AUVs can only obtain the sampling states of themselves and their neighbors under the predefined event-triggered conditions. Multi-AUV system is transformed into a second-order system with complex nonlinear dynamics, then their states are reconstructed via a neuro-adaptive state observer using sampling states, and a virtual controller is proposed such that all AUVs track the leader while maintaining the desired formation geometry under local communication with no Zeno behavior. Finally, numerical simulations are carried out to demonstrate the effectiveness of the proposed control design.

In this paper, the finite-time stabilization (FTS) issues of singular stochastic time-varying systems (SSTVSs) with time-varying delay are explored based on fuzzy control and the interval matrix method. Initially, the T-S fuzzy (TSF) model is employed to carry out a time-dependent fuzzy dynamic partitioning of the systems. Simultaneously, the time-varying parameters are reformulated using interval coefficients. Correspondingly, the initial systems are transformed into singular stochastic fuzzy systems with time-varying delay and interval parameter coefficients. Subsequently, a piecewise fuzzy controller is developed by means of the interval matrix method, and integral inequalities are utilized to handle Lyapunov-Krasovskii functions (LKFs). Then, the sufficient conditions for guaranteeing FTS of SSTVSs are derived. Finally, a numerical example along with simulations is provided to illustrate the effectiveness of the proposed control strategy and its capacity to achieve the desired control objectives.

This article investigates the practical finite-time spatial deployment of a class of large-scale heterogeneous nonlinear multi-agent systems (MASs), for which a novel hybrid analysis methodology based on ordinary differential equations (ODEs) coupled with partial differential equations (PDEs) is proposed. The assumption is made that a portion of the agents is sparsely distributed in space, while the other portion is densely distributed. By designing appropriate network communication protocols (NCPs), the dynamics of MASs are represented by a hybrid model consisting of several ODEs and a PDE. Particularly, the network topological weights are specifically designed as semi-Markov switched to better align with real communication situations of MASs, while complying with inconsistent switching rules. Moreover, for delay-free and time-delayed cases, this article proposes two novel projection-based adaptive neural control schemes and obtains two design criteria of controller gains, such that the practical finite-time stability of the tracking error systems could be guaranteed. Finally, numerical examples are provided to illustrate the effectiveness of the developed approaches.

This paper addresses the stability issue of discrete-time switched positive nonlinear systems (SPNSs) characterized by partial unstable subsystems and constant delays. Firstly, we use a parameter to constrain the ratio of active durations of the stable and unstable modes, and derive the exponential stability conditions for SPNSs based on a key function. Then we analyze the relationship between convergence speed and the constant delay. Additionally, we obtain a stability criterion of SPNSs under a particular sequence of switching signals. Finally, we validate the results through a numerical example.

In this article, the event-triggered finite-time stabilization of time-scale delayed Takagi-Sugeno (T-S) fuzzy systems is studied. By comparing strategies, inequality techniques, and time scale theory, finite-time stabilization criteria for the systems are derived that do not require differentiability of the time delay, and the controller is designed in a simple form that does not rely on power functions or delayed state feedback controllers. Corresponding results cover both continuous-time and discrete-time cases, and construct a unified theoretical framework for the finite-time analysis of the time-scale delayed systems. Meanwhile, the proposed event-triggered mechanism can avoid Zeno behavior and reduce the consumption of communication resources. The validity of the theoretical results is verified by two simulation experiments.

In this article, leader-following consensus of time-scale-type heterogeneous nonlinear multiagent systems (HNMASs) is investigated with dynamic periodic event-triggered mechanism (DPETM). The event detection period in DPETM is determined by a function-dependent threshold, whose initial value and the value at each periodic event detection instant are used for the update of an auxiliary function in the DPETM. Furthermore, the auxiliary function with periodic jumps serves as a detection threshold. To guarantee the nonincreasing behavior of the designed non-negative analysis function, a weighted function is devised that shares the same derivative form as the function that determines the detection period during each detection period. Then, by integrating the theory of time scales and graph theory, leader-following consensus is achieved in a periodic communication fashion with fewer sampling updates. Two examples are presented to illustrate the validity of the results.

In this paper, the tracking consensus of timescale-type nonlinear multiagent systems is investigated. First, the finite-time reachability of the designed integral sliding manifold is achieved through a discontinuous controller. Second, combining the proposed dynamic event-triggered strategy, sufficient condition for practical tracking consensus is obtained by constructing non-negative functions and applying inequality techniques. In addition, in combination with time scale calculus theory, Zeno behavior is excluded. The framework for sliding mode control (SMC) of timescale-type multiagent systems is constructed to generalize previous continuous-time SMC and discrete-time SMC results. Ultimately, the feasibility of the obtained theoretical results is validated through two numerical simulations.

This article addresses the problem of fixed-time bipartite synchronization (FxTBS) of signed networks (SNs) affected by impulses. First, this article constructs a model of SNs that captures the multilayer properties of the network and takes into account the influence of nonlinear coupling strengths between nodes. To overcome the challenges brought by the introduction of nonlinear coupling strengths, this article adopts a Takagi-Sugeno fuzzy model to characterize the nonlinear variation of coupling strengths reasonably. Then, in the framework of average impulsive interval applicable to a wider range of impulsive signals, this article proposes a novel method for analyzing the fixed-time stability of impulsive systems, which not only loosens the restriction of the derivative of the Lyapunov function in the existing studies, but also gives a more accurate estimation of the settling time, and more importantly, provides a theoretical basis for designing appropriate impulsive signals to modulate the dynamic behavior of SNs toward achieving the desired goal. Based on the newly suggested method, this article derives a unified synchronization criterion suitable for evaluating the implementation of FxTBS of SNs under both desynchronizing and synchronizing impulses. Finally, this article visualizes the correctness of the aforementioned theoretical results utilizing a widely used numerical example.

This article establishes a series of theoretical findings on the sampled-data-based event-triggered (SET) output-feedback consensus for a class of heterogeneous high-order multiagent systems (MASs) with mismatched parametric uncertainties. First, a distributed SET leader observer is devised for each agent to estimate the information of leader, and its convergence is rigorously proven based on matrix theory and analysis approaches. Second, a continuous state observer is developed using solely SET output signal influenced by sensor faults is proposed to avoid the nondifferentiability of the virtual controller, and the SET output-feedback control protocol is designed via the backstepping technique. It is proven that the semi-global output consensus problem can be addressed through the proposed controller. Different from the existing work, the consensus protocol presented in this article further saves communicational and computational resources, because the SET mechanisms are deployed to each channel of MASs which merely need to discretely monitor the event-triggered (ET) conditions at sampling instants and transmit the information at triggered time. Besides, the Zeno behavior can be trivially prevented. Finally, a simulation example is depicted to demonstrate the validity of the proposed theoretical results.

This paper investigates the issue of finite-time dissipative synchronization for delayed neural networks through the design of a sampled-data controller. The system is characterized by discontinuous parameters and exhibits state-dependent switching behaviors. To handle the challenges posed by these switching characteristics, the interval matrix method is utilized to transform the switching system into one with interval parameters. Then several sufficient conditions for finite-time boundedness of the error system are derived by constructing an appropriate Lyapunov-Krasovskii functional. Building upon these conditions, criteria for finite-time (21, 22, 23)-psi dissipative synchronization are further established using advanced integral inequality techniques. Moreover, a sampled-data controller incorporating state-dependent switching parameters is designed by solving the linear matrix inequality. A circuit model of the switched delayed neural networks is presented to illustrate the engineering significance and feasibility of the theoretical results. Finally, numerical simulations are provided to demonstrate the effectiveness and advantages of the proposed control strategy.

In this article, the bipartite consensus problem of multiagent systems on time scales under switching topologies is investigated. A dynamic event-triggered control strategy is designed to reduce the number of triggers. Sufficient condition to guarantee the consensus is obtained by constructing a non-negative function and combining with the theory of time scale calculus. In addition, it is proved through a categorical discussion that the entire triggering sequence determined by the switching topology and the triggering function does not display Zeno behavior. Lastly, to confirm that the theoretical results are feasible, a numerical simulation and an application to spacecraft formation flight are provided.

This paper discusses finite-time synchronization of complex-valued neural networks (CVNNs) with infinite delays. By utilizing non-separation approach, analytical strategy, and inequality techniques, a criterion is constructed to ensure that finite-time synchronization of CVNNs can be achieved. The designed controller only consists of state feedback term and sign function term. Finally, to confirm the effectiveness of the theoretical outcomes, two numerical examples are given.

In this paper, the fixed-time intra/inter-layer output synchronization problem of output-coupled multiplex networks is investigated utilizing a dynamic event-triggered control method. Firstly, to solve the issue of unavailability of node states resulting from uncontrollable factors, a multiplex networks model with observable intra/inter-layer output coupling information is constructed. Subsequently, two dynamic event-triggered control strategies based on output information are proposed, on the basis of which the fixed-time output synchronization criteria are established and Zeno behavior is excluded. The controllers proposed in this paper replace the common linear terms and multiple power-law terms in the existing fixed-time controllers with an exponential term based on the output errors, and also no longer include the intra/inter-layer coupling information of the nodes, making the form of the controllers more streamlined and easier to implement the control strategies. Finally, the effectiveness of the designed control protocols is verified by some numerical simulations based on Chua’s circuit as well as spacecraft formation control.

This article develops a methodology employing partial differential equations (PDEs) to facilitate the exponential deployment of large-scale heterogeneous nonlinear multiagent systems (MASs). The considered MASs comprise a multitude of nonlinear first-order agents (FOAs) and second-order agents (SOAs). Two heterogeneous nonlinear PDEs are established to model the considered MASs by designing appropriate network communication protocols. Unlike previous PDE-based approaches for multiagent deployment, the topological weights between neighboring agents are defined as series-dependent. An informed agent, which is able to measure the location information of other agents and transmit its location information to neighboring agents through the communication network, is placed between the final FOA and the initial SOA. This novel network-based control scheme is referred to as single-point control, which could ensure the well-posedness and exponential stability of the error system. Accordingly, pointwise and distributed measurements are employed for delay-free and time-delayed cases, respectively. Numerical examples are provided in 3-D space to substantiate the obtained theoretical results.

In this article, global exponential stabilization of Takagi-Sugeno (T-S) fuzzy systems with discrete time-varying delays on time scales under denial-of-service (DoS) attacks is investigated. When a DoS attack occurs, the control channel is blocked and the controller is disabled. Combining analytical method, inequality techniques, and time scale theory, stabilization criterion for the underlying systems is obtained via a fuzzy controller. Furthermore, the corresponding outcomes on continuous and discrete time domains are provided, respectively. Finally, two numerical simulations and an application of the Chua's circuit are exhibited to validate the effectiveness of the theories.