This paper studies the finite-time multistability of impulsive Hopfield neural networks with a general class of activation functions. First, the existence of Πni=1(2Mi+1) equilibrium points and Πni=1(Mi+1) invariant sets in such n-neuron neural networks can be guaranteed by applying the Brouwer’s fixed-point theorem as well as upper and lower functions method. Furthermore, it is demonstrated that these equilibrium points and invariant sets remain valid for the same neural networks when subjected to an appropriate controller. Then, on the basis of Lyapunov function method and impulsive control theory, two finite-time multistability theorems are established for Hopfield neural networks under distinct impulse scenarios: stabilizing impulses and destabilizing impulses. The settling time estimations for determining the local finite-time stability of Πni=1(Mi+1) equilibrium points are developed by designing general impulsive sequences, which reveal that the settling time is dependent on initial state, impulsive effects and control parameters. From the perspective of impulsive effects, the introduced stabilizing impulses in neural networks not only accelerate the convergence rate but also yield tighter upper bound of settling time estimation relative to impulse-free systems. In stark contrast, destabilizing impulses significantly degrade the convergence performance while resulting in more conservative upper bound of settling time estimation. Finally, theoretical results are shown to be effective by two illustrative examples and two associative memory applications of grayscale image.

This article investigates the prespecified performance consensus problem for a class of nonlinear multiagent systems (MASs) with unknown actuator faults. By employing a sensor-triggered mechanism and neural estimation algorithm, a novel leader-follower consensus protocol is devised for the nonlinear MASs. The developed sensor event-triggered mechanism comprises two parts, the first one is sensor event-triggered sampling, and the second one is event-triggered information transmission. Due to the presence of the sensor-triggered mechanism, the system states cannot be available in real time. In order to solve this challenge, a signal decomposition and compensation strategy is constructed to balance the intermittent sensor-sampled signals and the real system inputs. Furthermore, the considered actuator faults in each follower are not limited to be finite, the time, frequency and mode of the faults are also unknown. To address the unknown actuator faults in the nonlinear MASs, a resilient fault management mechanism is developed for each follower. Based on the managed actuator faults dynamics, some bounded estimation signals are constructed and the issue of "explosion of complexity" in the backstepping design procedure is eliminated through the application of nonlinear filters with compensation terms. Finally, simulation results are given to illustrate the effectiveness of developed control protocol.

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 proposed a model-free adaptive security control (MFASC) method designed for nonlinear vehicle systems, simultaneously tackling controller saturation, external disturbances, and aperiodic Denial-of-Service (DoS) attacks. To achieve a desired platoon control objective with only input-output data, the dynamics are reconstructed into a differential form with pseudo partial derivative parameters. Under saturation constraints, a de-saturation factor is introduced to ensure the controller’s state remains within permissible limits. Additionally, a disturbance observer is included to estimate external disturbances and mitigate their negative effects. For aperiodic DoS attacks, an attack compensation mechanism is developed. Comprehensive stability analysis demonstrates the ultimate boundedness of the formation error of vehicle systems under aperiodic DoS attacks. The validity of the theoretical results is supported by numerical simulations.

In this article, we contribute to dynamic event-triggered-based leader-follower quantized consensus design for fractional-order (FO) nonlinear multi-agent systems (MASs) under asymmetric time-varying state constraints. Firstly, we propose a novel dynamic event-triggered mechanism (ETM) to fully utilize the channel resources between the controller and actuator. Subsequently, by means of the characteristics of quantization nonlinearities and the architecture of FO nonlinear MASs, the influence of quantized inputs is eliminated. In the consensus protocol design procedure, the radial basis function neural networks (RBF-NNs) technology is employed to dynamically estimate the uncertain functions existing in the system. Then, by employing a bivariate FO derivative lemma and some convex time-varying barrier Lyapunov functions (BLFs) with two variables, a dynamic event-triggered-based leader-follower quantized consensus protocol is constructed. Exhaustive theoretical analysis manifests that the tracking errors of the FO nonlinear MASs converge to a small region, all signals in the FO nonlinear MASs are bounded, and the asymmetric time-varying state constraints can be achieved regardless of the presence of the quantized inputs and dynamic event-triggered communication. Finally, a chaotic Duffing FO nonlinear MASs is employed to demonstrate the efficacy of the developed consensus protocol.

This study develops the space-time sampled-data control problem for memristor-based reaction-diffusion neural networks (MRDNNs) using a memory event-triggering scheme. Unlike traditional Markov switching models with fixed transition probabilities, the nonhomogeneous sojourn probabilities are capable of describing the switching behavior more accurately. By effectively leveraging historical transmitted packets, an innovative mode-dependent memory event-triggering scheme that incorporates nonhomogeneous sojourn probability information is introduced, optimizing communication resource utilization. A space-time sampled-data control law is designed by sampling both spatial and temporal domains, significantly reducing network communication resource consumption while achieving the desired system performance. The validity and superiority of the proposed space-time sampled-data control strategy are demonstrated through a simulation example.

This article focuses on the crucial role of reliable dc–dc converter operation for the stability of modern power electronic devices. Addressed is a common issue in the fault diagnosis of dc–dc converters: the tendency to rely on local feature fitting while the temporal continuity of electrical signals is neglected. An innovative diagnostic method that utilizes an adaptive wavelet transform from a data processing perspective is proposed. This technique can dynamically adjust the scale and translation parameters to adapt to the continuous changes in electrical signals caused by varying circuit conditions. From the standpoint of model improvement, the extended convolutional capsule network model is designed. Through multiscale feature extraction, integration of global-local attention mechanisms, and global vector analysis, this model effectively diagnoses fault features. It is demonstrated that our method is effective in extracting the time-continuity features of electrical signals, and exhibits significant advantages in diagnostic accuracy, performance metrics, and application generalization capability. Consequently, this study presents a holistic and effective approach for fault diagnosis in dc–dc converters.

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.