The reliability of rotating machinery is of vital importance to industrial production efficiency and safety. However, current prognostic and health management (PHM) methods usually rely on specific task models and face significant challenges when dealing with multimodal data of different signal forms, fault modes, and operating conditions. We proposed a rotating machinery health management framework based on the input spatial over-parameterized convolutional model (ISOPConvM). ISOPConvM introduces unified processing of heterogeneous vibration signals based on prior knowledge enhancement. Subsequently, a new one-dimensional convolutional neural network named input spatial over-parameterized convolutional (ISOPConv) is proposed, which introduces over-parametrization through the input space dimension to improve the training convergence speed and stability of the model. Finally, on the large-scale multimodal bearing health management (MBHM) datasets, ISOPConvM achieves industry-leading performance on various rotating machinery and outperform specific methods designed for a single dataset. Therefore, the proposed intelligent unified framework has high generality and scalability.

This paper presents a novel nonsingular fixed-time distributed optimal formation control strategy for interlinked marine surface vehicles (IMSVs) subject to actuator faults and failures. A cyber-physical framework is constructed to handle such problems. Firstly, a fixed-time distributed optimization estimator in the cyber layer is designed to derives the optimal solution based on the local objective functions through consensus-based algorithms. Secondly, a fixed-time robust tracking controller is constructed in the physical layer with the estimated optimal solution being the reference signal. The overall controller combines fixed-time stability theory and distributed optimization techniques, enabling each vehicle to achieve optimal formation positioning based on local interactions and global formation requirements. Furthermore, a fault-tolerant mechanism is embedded to maintain performance under partial actuator degradation. Lyapunov analysis rigorously proves the fixed-time stability of the closed-loop system, and simulation results validate the effectiveness and robustness of the proposed method.

This research addresses the detection of weighted false data injection (FDI) attacks in nonlinear cyber-physical systems (CPSs), aiming to enhance both early warning capability and detection accuracy. First, considering the parameter uncertainties commonly present in real-world systems, two attack detection (AD) estimators are developed with varying degrees of conservatism, aiming to enhance detection accuracy and allow flexible selection according to different practical conditions. Second, a weighted method for detecting unknown FDI attack signals is proposed, significantly enhancing detection speed. Third, to address communication bandwidth limitations, a quantizer is employed to process transmission signals. This article aims to address the challenge of slow AD in CPSs, considering factors such as parameter uncertainties, packet loss, noise, and constraints on communication bandwidth. Finally, the effectiveness of the proposed method is validated through the longitudinal kinematics model of the aircraft.

The problem of detecting false data injection (FDI) attacks in a class of cyber–physical systems with discrete-time information under uncertain parameters is investigated in this article. In practical scenarios, it is noteworthy that accurately modeling the system matrix is often challenging, and traditional detection methods frequently result in false positives and missed detections. Therefore, this article introduces a novel approach for accurately detecting FDI attacks in the presence of parameter uncertainties. Assuming that system parameters reside within a convex polytope, three distinct FDI attack detection estimators with varying performance levels are proposed. Subsequently, a convex optimization problem is established to obtain the necessary gains for the detectors. The results demonstrate that the proposed detection method achieves higher accuracy under uncertain parameters. Furthermore, the method allows for flexible selection based on application-specific requirements, whether prioritizing accuracy or efficiency. Finally, the effectiveness and applicability of the proposed techniques are demonstrated through simulation examples using the F-404 aerospace engine system model.

The performance limitations of multiple-input multiple-output (MIMO) information time delay system (ITDS) with packet loss, codec and white Gaussian noise (WGN) are investigated in this article. By using the spectrum decomposition technique, inner-outer factorization, and partial factorization techniques, the expression of performance limitations is obtained under the two-degree-of-freedom (2DOF) compensator. The theoretical analysis results demonstrate that the system performance is related to the time delay, non-minimum phase (NMP) zeros, unstable zeros and their directions in a given device. In addition, WGN, packet loss and codec also impact the performance. Finally, the theoretical results are verified by simulation examples. Simulation results show that packet loss rate and encoding and decoding have a greater impact on system performance.

In this article, we consider distributed adaptive finite-time containment control for a class of nonlinear multiagent system with external perturbation which is unknown upper bound under directed graph. Multiagent systems is nonlinear dynamics system of which inherent nonlinearities are designed as higher- and lower-order. First of all, by using information of neighbor, it is possible to construct a second-order finite-time estimator to estimate weighted average of positions and velocities of leaders. Then, a distributed adaptive finite-time containment controller is proposed to make states of followers can converge to convex hull spanned by leaders in finite time. Finally, effectiveness of proposed control algorithm is demonstrated by numerical simulation.

A multiple-input–multiple-output (MIMO) infor-mation time-delay system (ITDS) has certain performance limitations due to message queue, additive Gaussian white noise (AGWN), bandwidth, and packet loss. This study analyzes the difference of system performance when the servers process message queue independently and cooperatively in detail. In addition, by partial factorization, inner–outer factorization, and the spectrum decomposition method, the performance expression under 2-degree-of-freedom (2DOF) controller has been obtained. The results show that the performance of ITDS is influenced not only by the inherent properties of system, including unstable zeros, time delay, nonminimum phase (NMP) zeros, and their directions in the given device but also by network factors, such as bandwidth, packet loss, and AGWN. Finally, the simulation verifies that even for the same parameters, under different queuing rules, the system has different performances.

This article investigates the dissipative stability and stabilization problems of Takagi–Sugeno fuzzy systems by employing sampled-data control. First, in order to enhance the adaptability of the controller, the internal and external influence factors, such as communication time delay and aperiodic sampling pattern, are taken into consideration in the design process. Then, some new terms are proposed to construct the looped-functional-like Lyapunov functional, which can improve the dissipative performance, enlarge the admissible upper bound of the aperiodic sampling periods, and reduce the occupation of control signals in the communication channel. Next, based on the free-matrix-based integral inequalities, some novel dissipative criteria are presented in terms of linear matrix inequalities. Finally, the superiority of the provided criteria is demonstrated through a truck–trailer system.

The performance limitations of multi-input–multi-output (MIMO) information time-delay system (ITDS) due to bandwidth, additive white Gaussian noise (AWGN), and cyber-attacks are investigated in this work. The game theory methods are used to analyze the impact of intrusion detection systems on systems performance in order to deal with network attacks under incomplete information. The expression of performance limitations is obtained by using the spectrum decomposition technique, inner–outer factorization, and partial factorization techniques under the two-degree-of-freedom (2DOF) compensator. The results demonstrate that the performance limitations of the system are not only influenced by the inherent properties, including time delay, nonminimum phase (NMP) zeros, and unstable zeros and their directions of the given object but are also influenced by the communication parameters, such as AWGN and bandwidth. In addition, the results show that under the cyber-attacks with incomplete information, the performance is only influenced by the payoff matrix of the defense, and not by the payoff matrix of the offense. Finally, the theoretical results are verified by performing simulations.

This article investigates the exponential synchronization problem for a class of memristor-based neural networks with mixed time-varying delays and parameter perturbations, and inertial items are considered (MINNs). A periodically intermittent control protocol is designed to guarantee the exponential synchronization between two MINNs. Then, by adopting nonsmooth analysis, Halanay inequality, and Lyapunov theory, the exponential synchronization criteria for MINNs under the proposed controller are obtained. Furthermore, instead of the reduced-order method, the synchronization of MINNs is considered under the framework of the second-order system directly, which is different from existing literature. Some numerical simulations are presented to show the validity of the proposed criteria in the end.

In this article, the time-varying formation (TVF)-containment tracking problem is investigated for linear multiagent systems (MASs), in which the input of tracking leader is unknown and bounded. A novel kind of adaptive discontinuous TVF-containment tracking protocol for linear MAS is proposed. Based on the Lyapunov stability theory, it is shown that under the appropriate assumptions the leaders can achieve an expected TVF and track the trajectory of the tracking leader, while the follower agents can move into the convex hull formed by leader agents. Furthermore, the continuous protocols are also presented to avoid undesirable chattering from the tracking leader’s nonzero input, under which the errors are uniformly ultimately bounded and the upper bound of errors can be made arbitrarily small. In the end, simulation example verifies the feasibility of the desired theoretical results.

This article is concerned with the problem of compensation-based output feedback control for Takagi-Sugeno fuzzy Markov jump systems subject to packet losses. The phenomenon of packet losses is assumed to randomly occur in the feedback channel, which is modeled by a Bernoulli process. Employing the single exponential smoothing method as a compensation scheme, the missing measurements are predicted to help offset the impact of packet losses on system performance. Then, an asynchronous output feedback controller is designed by the hidden Markov model. Based on the mode-dependent Lyapunov function, some novel sufficient conditions on the controller existence are derived such that the closed-loop system is stochastically stable with strict dissipativity. Besides, an algorithm for determining the optimal smoothing parameter is proposed. Finally, the validity and advantages of the design approach are manifested by some simulation results.

This article investigates the predefined-time stabilization problems of Takagi–Sugeno (T–S) fuzzy systems. For addressing the considered problems, a class of novel integral sliding mode surface is first designed based on the time-regulator function, on which the system states are forced to converge to the origin in a predefined time after the sliding mode surface is reached. Further, the proposed sliding surface is employed to construct predefined-time integral sliding mode controller for the time delayed and disturbed T–S fuzzy system. The settling time appears as the sum of two predefined-time parameters in the controller design, which, respectively, adjusts the convergence time for reaching the sliding mode surface and the convergence time for arriving the origin from the sliding mode surface. The sufficient conditions for maintaining the predefined-time stability of the T–S fuzzy systems are obtained through systematic Lyapunov stability analysis. Finally, a numerical simulation example on delayed and disturbed Chua circuit is presented to verify the effectiveness of the proposed predefined-time integral sliding mode controller.

This article is concerned with the optimal tracking performance on linear time-invariant (LTI) discrete-time multi-input–multi-output (MIMO) systems based on quantization, channel noise, and encoding–decoding, as well as bandwidth constraints. Meanwhile, power constraint, feedback and feedforward loops constraints of the systems are also taken into account. The tracking performance expression is described by inner–outer factorization, which is obtained by all stabilizing two-degree-of-freedom compensators. The results will illustrate that tracking performance limitation is decided by nonminimum phase zeros and unstable poles of the given plant. Moreover, time delay, encoding–decoding, channel noise, quantization, and bandwidth constraints also impact the performance. The accuracy of the method is verified by simulation examples.

This paper is concerned with the sliding mode control problem for a class of continuous-time switched neural networks with mode-dependent average dwell time (MDADT). The considered continuous-time switched neural networks are motivated by biological neural networks which contain a nonlinear term and a changeable switched signal. The concept of MDADT is introduced, in which every subsystem has its own dwell time before switching to another subsystem. Moreover, a novel sliding mode controller is designed by an event-triggered mechanism which is based on the observer error and the system mode, where its triggered condition can be more conservative and practical than the existing triggered conditions. Sufficient conditions are derived to ensure that the closed-loop system is stochastically exponentially stable in terms of linear matrix inequalities. The designed sliding mode controller can promote the sliding mode motion of the system state. Finally, an illustrative example is provided to demonstrate the effectiveness and merits of the proposed method.

In this paper, the event-triggered distributed Kalman filtering problem is considered for a class of networked multisensor fusion systems (NMFSs) under sensor energy and network bandwidth constraint. A general event-triggered scheme is employed for the NMFSs to reduce the energy consumption and communication burden between the sensor nodes and fusion center (FC) under the communication bandwidth constraints. Local estimation information is allowed to transmit partial components to FC over the network with limited bandwidth. A group of binary variables are introduced to describe the component transmitting process when the triggering condition is violated. Furthermore, the untransmitted local estimation signals are compensated by the previous transmitted one, and a recursively event-triggered distributed fusion Kalman filter in the linear minimum mean square error sense is designed from the restructured local unbiased estimators. At each time instant, a set of binary variables are determined by an optimal judgement criterion. Finally, a simulation example is provided to illustrate the effectiveness and advantages of the proposed methods.

In this paper, the event-triggered H∞state estimation problem is investigated for a two-degree-of-freedom quarter-car suspension system operated over a switching-channel network environment. First, the channel-switching is governed by a nonhomogeneous Markov chain whose probability transition matrix is time-varying. Then, a Markov jump linear system model is adopted to represent the overall networked system in view of the event-triggered communication scheme, signal quantization and random packet losses on account of the limited network bandwidth. By virtue of the Lyapunov functional and linear matrix inequality method, the event-triggered H∞state estimation problem is transformed into an optimization problem that switching-channel-dependent estimators are designed such that the estimation error system is exponentially stable in the mean square sense and achieves a desired performance level. Finally, a simulation example is used to demonstrate the validity of proposed design method.

This paper is concerned with the event-based security control problems for a set of discrete-time stochastic systems suffered from randomly occurred attacks, especially the denial-of-service attacks and the deception attacks. An attack model with compensation is established to describe combinations of attacks to the networked control systems. An event-triggered mechanism is employed to debase the communication load by transmitting measurement signals when a definite triggering condition is satisfied. A definition of security probability is proposed to describe the transient state of controller systems. The aim of this paper is to design a dynamic output feedback controller, thereupon the closed-loop system achieves the described security in probability. Some novel sufficient conditions are proposed to guarantee the input-to-state stability of the system in probability, and the controller gains are designed by solving a set of matrix inequalities. The upper bound of the quadratic cost function is also derived. Finally, simulation examples are applied to illustrate the effectiveness of the proposed design scheme.

This brief is concerned with stability analysis for delayed neural networks (DNNs). By establishing polynomials and introducing slack variables reasonably, some improved delay-product type of auxiliary polynomial-based functions (APFs) is developed to exploit additional degrees of freedom and more information on extra states. Then, by constructing Lyapunov-Krasovskii functional using APFs and integrals of quadratic forms with high order scalar functions, a novel stability criterion is derived for DNNs, in which the benefits of the improved inequalities are fully integrated and the information on delay and its derivative is well reflected. By virtue of the advantages of APFs, more desirable performance is achieved through the proposed approach, which is demonstrated by the numerical examples.

This paper is concerned with the guaranteed cost control problem for a class of Markov jump discrete-time neural networks (NNs) with event-triggered mechanism, asynchronous jumping, and fading channels. The Markov jump NNs are introduced to be close to reality, where the modes of the NNs and guaranteed cost controller are determined by two mutually independent Markov chains. The asynchronous phenomenon is considered, which increases the difficulty of designing required mode-dependent controller. The event-triggered mechanism is designed by comparing the relative measurement error with the last triggered state at the process of data transmission, which is used to eliminate dispensable transmission and reduce the networked energy consumption. In addition, the signal fading is considered for the effect of signal reflection and shadow in wireless networks, which is modeled by the novel Rice fading models. Some novel sufficient conditions are obtained to guarantee that the closed-loop system reaches a specified cost value under the designed jumping state feedback control law in terms of linear matrix inequalities. Finally, some simulation results are provided to illustrate the effectiveness of the proposed method.

In this paper, the distributed H∞state estimation problem is investigated for a class of filtering networks with time-varying switching topologies and packet losses. In the filter design, the time-varying switching topologies, partial information exchange between filters, the packet losses in transmission from the neighbor filters and the channel noises are simultaneously considered. The considered topology evolves not only over time, but also by event switches which are assumed to be subjects to a nonhomogeneous Markov chain, and its probability transition matrix is time-varying. Some novel sufficient conditions are obtained for ensuring the exponential stability in mean square and the switching topology-dependent filters are derived such that an optimal H∞disturbance rejection attenuation level can be guaranteed for the estimation disagreement of the filtering network. Finally, simulation examples are provided to demonstrate the effectiveness of the theoretical results.