To address the challenges of multiscale feature coupling, high-frequency noise interference, and complex nonlinear relationship modeling in inverter fault diagnosis, this study proposes an intelligent diagnostic method based on a synchronous cascaded wavelet transform and an improved pyramid vision transformer-Kolmogorov–Arnold Network. First, the synchronous cascaded wavelet transform is utilized to convert the inverter output signal into a 2-D time–frequency image, effectively capturing the multiband harmonic features induced by capacitor parameter degradation. Next, a cross-scale attention mechanism is developed to dynamically weight and fuse high-resolution details with low-resolution contextual features from adjacent stages, thereby enhancing the joint perception of both high-frequency transient faults and low-frequency gradual faults. In addition, an adaptive spatial reduction mechanism is introduced to lower computational costs while preserving fault-relevant frequency-band information. Finally, the Kolmogorov–Arnold network module employs interpretable basis functions constructed from spline-parameterized univariate functions to model the nonlinear mapping between capacitor parameters and harmonic characteristics. Its edge-level learnable activation structure further enhances the capability to decouple concurrent faults. Experimental results demonstrate that the proposed method achieves high diagnostic accuracy, significantly reduces model parameters, and improves computational efficiency.

This paper is concerned with the problem of dynamic event-triggered bipartite consensus control for nonlinear multi-agent systems based on signed networks. A new dynamic event-triggered control mechanism is proposed, whereby an adjustment variable is introduced to dynamically schedule the triggering frequency, enabling the minimization of the triggering times while ensuring system performance. Based on the designed event-triggered control algorithm, sufficient conditions are derived to guarantee that bipartite consensus can be reached by the considered nonlinear multi-agent system. Furthermore, it is proved that Zeno behavior will not occur. Numerical examples are provided in this paper to verify the effectiveness of the proposed control algorithm. The simulation results reveal that, compared with the static event-triggered mechanism and the traditional dynamic event-triggered mechanism, the proposed event-triggered algorithm can further reduce the triggering times and enhance the flexibility of the event-triggered mechanism.

This technical paper investigates the cluster synchronization of finite-field networks (FFNs) based on the algebraic state space representation. By resorting to the semi-tensor product of matrices, the cluster synchronization of an FFN can be completely converted into the set stability of state trajectory. Then, a necessary and sufficient condition for the cluster synchronization of FFNs is obtained based on the invariant subset and one-step transition matrix. In particular, the obtained results are applicable to check the leader-follower synchronization and group consensus of FFNs. Finally, a numerical example is given to illustrate the feasibility of the obtained results.

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 article is concerned with the distributed set-membership estimation problem for a class of discrete time-varying systems over binary sensor networks. For the binary sensors, the cases of fixed and time-varying thresholds are considered. In both the cases, the information useful for state estimation purposes is extracted by utilizing the crossings of binary measurements at two adjacent time instants, and then distributed estimators are constructed for each sensor node with the aid of the available measurements, where a set of vector saturation functions is introduced to resist the adverse effect of outliers during signal transmission. A novel distributed set-membership performance index is provided by averaging over the ellipsoidal constraints of all the sensor nodes, and the local performance analysis method is employed to establish sufficient criteria that guarantee the existence of desired estimators whose parameters are then derived for every node by recursively optimizing certain ellipsoids in the sense of matrix trace. The applicability and feasibility of the distributed set-membership schemes developed in this article are verified by two illustrative examples.

In this article, the distributed model predictive control (MPC)-based noncooperative game problem is dealt with for the discrete-time multiplayer systems (MPSs) with an undirected graph. To reflect the reality, the state and input constraints are considered along with the matched disturbances and unmatched disturbances. The disturbance-observer-based composite MPC strategy is put forward which optimizes a given cost function over the receding horizon while eliminating the matched disturbances. An iterative algorithm is developed such that the model predictive dynamic game (MPDG) converges to the so-called$\varepsilon $-Nash equilibrium in a distributed manner. Sufficient conditions are established to guarantee the convergence of the proposed algorithm. In addition, easy-to-check conditions are also provided to ensure the uniform boundedness of the studied MPSs. Finally, a numerical example of a group of spacecrafts is provided to verify the effectiveness of the proposed methodology.

In this article, we study the finite-time stabilization and the asymptotic stabilization with probability one of Markovian jump Boolean control networks (MJBCNs) by sampled-data state feedback controls (SDSFCs). Based on the semi-tensor product (STP), we introduce an augmented variable multiplied by the vector form of the switching signal and the state of MJBCN. We find that under SDSFC, the sequence of the states of the augmented variable at sampling instants satisfies the Markov property. Based on the convergences of the switching signal and the augmented variable, we obtain the sufficient and necessary criteria for the finite-time stabilization and the asymptotic stabilization of MJBCNs by SDSFCs, respectively. Moreover, for the two kinds of stabilization, the feedback matrices of SDSFCs are constructed, respectively. Finally, the obtained results are applied to an apoptosis network and a model of the lactose operon in the Escherichia Coli.

A new type of asymptotic stability for nonlinear hybrid neutral stochastic systems with constant delays was investigated recently, where the criteria depended on the delays’ sizes. Unfortunately, developed theory so far is not sufficient to deal with challenging problems of the decay rate, time-varying delays, and nonautonomous issues. These problems have not been tackled in the existing literature. Consequently, under the weak constraints, this article focuses on the general decay, including the exponential stability and the polynomial stability, for nonlinear nonautonomous hybrid neutral stochastic systems with time-varying delays by the approach of the multiple degenerate functionals. Moreover, this article derives the interesting assertions related to the general$H_{\infty }$stability and the polynomial growth at most.

This article investigates the problems of structural balance and distributed control for a high-order signed network with generic linear dynamics. A novel approach is proposed to analyze the structural balance of a general network based on the strongly connected components of the topology graph and the Frobenius normal form of the adjacency matrix. To address the distributed control of high-order signed networks, both state-feedback and observer-type algorithms are developed on the basis of low-gain strategies, where a unified framework is presented to construct full- or reduced-order observers. The dynamical behaviors of the signed network under the proposed algorithms are systematically explored, where the nontrivial final network state is computed by employing an eigenvector-based approach. The theoretical results are illustrated by numerical examples.

This paper investigates a distributed static and dynamic self-triggered impulsive control for nonlinear multiagent systems (MASs) where the impulsive gains follow a normal distribution, respectively. By integrating the distributed self-triggered control scheme with the impulsive control approach, a novel distributed impulsive controller is developed. The goal of the consensus of MASs can be realized using the proposed methods and several consensus criteria are obtained. Our schemes have some distinct superiorities, including the impulsive gains obeying a normal distribution, avoiding the continuous communication, and reducing the sampling frequency. Hence, compared with the existing literature, the conservativeness coming from the limitation of impulse gain and the sampling frequency is degraded, and it effectively extends the generality of the method in the practical application. Finally, the effectiveness of the theoretical results is demonstrated by two simulations.

This article studies the stability in probability of probabilistic Boolean networks and stabilization in the probability of probabilistic Boolean control networks. To simulate more realistic cellular systems, the probability of stability/stabilization is not required to be a strict one. In this situation, the target state is indefinite to have a probability of transferring to itself. Thus, it is a challenging extension of the traditional probability-one problem, in which the self-transfer probability of the target state must be one. Some necessary and sufficient conditions are proposed via the semitensor product of matrices. Illustrative examples are also given to show the effectiveness of the derived results.

This article investigates the reliable output feedback control problem for networked nonlinear semi-Markov jump systems, in which a control strategy with redundant channels is established to reduce the adverse effect caused by packet dropouts. The actuator faults are fully considered in the setup. On the basis of stochastic analysis theory and fuzzy-model-based method, some criteria are established to guarantee the σ -error mean-square stability for the considered systems. As a consequence, the reliable output feedback controller design method is proposed, which can be utilized to deal with the actuator failures problem effectively. Finally, two illustrative examples are employed to explain the availability of the presented design approach, where the single-link robot arm system model is contained.

In this paper, a continuous-time multiagent system is proposed for solving optimal resource allocation problems with local allocation feasible constraints. In the system, all the primal agents are divided into different groups. We use dual variables which describe the dual agents to represent the groups of the original agents. The groups of dual agents are used to communicate with others on behalf of the primal agents to reduce communication costs. That is to say, primal agents aim to seek their own optimal solutions by using local information. And dual agents represent primal agents to communicate with other agents in different groups by using the whole group information. The two kinds of agents cooperate to find the optimal solution of the problem. In this way, we only need to know the connections of dual agents to design the multiagent network, and do not need to consider the connections of the primal agents. So the communication cost and the amount of variables will be largely reduced especially for large-scale problem. Furthermore, it is proved that the multiagent system can reach consensus with respect to the dual variables. At the same time, the primal variables are convergent to the optimal solutions of the optimization problem under some certain assumptions on the communication network. For large-scale problem if we take the groups as areas, then the system is suitable for multiarea problem. Simulation results are presented to demonstrate the performance of the proposed multiagent system.

Event-triggering strategy is a control implementation technique which intends to minimizing resource usage while achieving the acceptable performance of the closed-loop system. In this paper, an event-triggered sliding mode control (SMC) is designed for attitude stabilization of a rigid spacecraft subject to external disturbances and model uncertainties. A semi-global event-triggering strategy is proposed for SMC to keep the system trajectory in the vicinity of a proposed sliding surface. Since in this sliding surface, the sliding mode is asymptotic stable, the trajectories of the attitude control system remain ultimately bounded under the circumstance of disturbances and uncertainties. Moreover, the given event-triggered control implementation is only dependent on the angular velocity at sampling instant and triggering strategy with Zeno phenomenon can be avoided. Some numerical simulations are shown to illustrate the availability of the obtained results.

In this paper, the dissipative synchronization control problem for Markovian jump memristive neural networks (MNNs) is addressed with fully considering the time-varying delays and the fragility problem in the process of implementing the gain-scheduled controller. A Markov jump model is introduced to describe the stochastic changing among the connection of MNNs and it makes the networks under consideration suitable for some actual circumstances. By utilizing some improved integral inequalities and constructing a proper Lyapunov-Krasovskii functional, several delay-dependent synchronization criteria with less conservatism are established to ensure the dynamic error system is strictly stochastically dissipative. Based on these criteria, the procedure of designing the desired nonfragile gain-scheduled controller is established, which can well handle the fragility problem in the process of implementing the controller. Finally, an illustrated example is employed to explain that the developed method is efficient and available.

The control system of six degrees of freedom visual industrial robot based on the control mode of multi-axis motion control cards and PC was researched. For the variable, non-linear characteristics of industrial robot`s servo system, adaptive fuzzy PID controller was adopted. It achieved better control effort. In the vision system, a CCD camera was used to acquire signals and send them to video processing card. After processing, PC controls the six joints` motion by motion control cards. By experiment, manipulator can operate with machine tool and vision system to realize the function of grasp, process and verify. It has influence on the manufacturing of the industrial robot.

This paper presents the design of a anti-windup compensator for networked time-delay systems with actuator saturation. Taking into account network-induced delay and packet dropout, the closed-loop system is modeled as a system with multiple delays and sector nonlinearities. By applying a discretized Lyapunov functional method and sector conditions, delay-dependent sufficient conditions are developed to achieve the asymptotic stability of the closed-loop system. The design of anti-windup compensators is proposed in a linear matrix inequality framework.

The system studied in this paper has four main features: 1) It is a networked controlled system (NCS), and therefore, the signal transfer is subject to random delay and/or loss; 2) it is a nonlinear system approximated by a Takegi--Sugeno (T-S) fuzzy model; 3) its multisensors and multiactuators are subject to various possible faults/failures; and 4) there are uncertainties in the plant model parameters. A comprehensive model is first developed in this paper to cover these features for a class of NCS nonlinear systems. This model has removed some limitations of similar models in the published literature. Then, the Lyapunov functional and the linear matrix inequality (LMI) are applied to develop two new stability conditions (Theorems 1 and 2). These conditions and an algorithm are used to design a controller to achieve robust mean square stability of the system. Finally, two examples are used to demonstrate the application of the modeling and the controller design method developed.

This paper deals with the problem of stabilization for networked stochastic systems with transmitted data dropout. The plant in the networked control system (NCS) under consideration is a discrete stochastic time-delay nonlinear system represented by a Takagi-Sugeno fuzzy model. Exponential stability criteria of the NCS are developed by using a common quadratic Lyapunov function and a fuzzy Lyapunov function, respectively. A stabilization controller with convergence rate constraint can be designed by solving a set of linear matrix inequalities that is numerically feasible with commercially available software. Three numerical examples are presented to demonstrate the effectiveness of the proposed methods.

In this paper, robust Hinfinobserver design for a class of Lipschitz nonlinear discrete-time systems with time-delay and disturbance input are addressed, where the Lipschitz condition is expressed in a component-wise rather than aggregated manner. It has been shown that both full-order and reduced-order robust Hinfinobservers can be obtained by means of the same convex optimization procedure with minimization of the disturbance attenuation upper bound gamma > 0. It is also shown that for a prescribed Hinfin-norm upper-bound gamma > 0, the tolerable Lipschitz bounds can be obtained by another convex optimization procedure

Global asymptotic stabilization for a class of delayed bilinear descriptor systems is first studied in this paper. New approaches are developed by means of the LaSalle invariant principle for delayed nonlinear systems. A new set of sufficient condition is first derived via the continuous static state feedback, the feedback not only guarantees the existence and uniqueness of solution but also the global asymptotical stabilization for the closed loop system

In this paper, robust H/sub /spl infin// control problem is investigated for a class of uncertain linear discrete-time systems with norm-bounded nonlinear uncertainties. The class of systems can be treated as linear nominal parts with nonlinear perturbations on both states and control inputs. By means of linear matrix inequality technique, an approach has been developed to find the tolerable uncertainty bounds for the robust H/sub /spl infin// performance, and the corresponding static (or dynamic) output feedback controllers at the same time.

This paper addresses the problem of the robust stabilization and the state estimation (RSSE) for uncertain stochastic discrete-delay large-scale systems. The purpose of this problem is to design a decentralized output feedback controller based on local observers such that, for all admissible parameter uncertainties, the resulting closed-loop system is robustly stochastically stable. A sufficient condition for solving the above problem is obtained by means of linear matrix inequality (LMI) techniques. A numerical example is provided to demonstrate the effectiveness of the proposed design approach.