This study mainly focuses on the observer-based control problem of cyber-physical systems equipped with dual channel energy-harvesting relays. In order to expand communication coverage and maintain long-term operation, a decode-and-forward relay driven by energy harvesting technology is introduced. During the transmission of codewords, consecutive packet losses may occur due to stochastic network conditions, which is modeled as bounded stochastic communication delay. To address this issue, an observer-based remote control strategy incorporating stochastic delay modeling is developed to guarantee that the closed-loop dynamics ultimately maintain exponential mean square boundedness. By utilizing stochastic stability theory and inequality based analysis techniques, a set of conditions for the expected existence of the controller is derived. Obtain the corresponding estimated gain and control gain by solving the linear matrix inequality (LMI). Numerical experiments are carried out to validate the stability and reliability of the developed control approach.

With the rising global energy consumption, the energy use of heating, ventilation, and air conditioning (HVAC) systems has become a critical concern. Existing deep reinforcement learning control methods for HVAC systems often exhibit slow convergence and poor adaptability to dynamic environments, resulting in significant indoor temperature fluctuations, inefficient temperature control strategies, and consequently, energy waste and failure to meet thermal comfort requirements. To address these challenges, this study proposes a novel HVAC control method based on the Soft Actor-Critic with Intrinsic Reward and Correlation-Aware CNN (SAC-IRCNet) model. The model incorporates cooling load prediction as a constraint to enable on-demand cooling supply. It integrates an Elliptical Dynamics Exploration intrinsic reward mechanism, which accelerates SAC convergence through elliptical exploration rewards and inverse dynamics models, thereby reducing energy waste. Additionally, the Correlation-Aware CNN enhances SAC’s feature extraction capability by leveraging state correlations to better understand contextual information, enabling more accurate responses to environmental changes and improved thermal comfort. Experimental results show that SAC-IRCNet achieves a 3.94% faster reward convergence and a 5.78% higher maximum cumulative reward within five episodes compared to SAC. It reduces energy consumption by up to 17.25% (21,837.58 kW) and lowers thermal discomfort violations by up to 6.44%, demonstrating excellent generalization ability across two datasets.

As one of the most basic specifications for many types of sensors, sensor resolution has been largely overlooked in a multitude of state estimation studies. Under the binary-encoding mechanism, this article deals with the outlier-resilient state estimation problem of Markov jump neural networks (MJNNs) with sensor resolution. An improved binary-encoding procedure, capable of assigning distinct bit lengths to different MJNN modes, is proposed to accommodate diverse physical constraints and limited network resources. Building on this procedure, a mode-dependent state estimation scheme embedded with a saturation function is put forward to alleviate the by-effects of external disturbances, decoding errors, and measurement outliers. Sufficient conditions are derived to guarantee the exponential ultimately boundedness of estimation errors. Lastly, simulation experiments are carried out to demonstrate the applicability of the proposed method.

This work presents a novel adaptive sampled-data backstepping control method for the fractional-order air handling units (AHUs) in the building heating, ventilating, and air conditioning (HVAC) systems to achieve indoor temperature regulation. The control for fractional incommensurate AHUs, which relieve computational costs for implementing control and thus enhance building energy efficiency due to the concise and precise form of the system model in comparison to the integer-order case, are studied for the first time. By strictly considering the infinite-memory and hereditary characteristics of fractional-order systems, the temperature control scheme, which novelly combines the sampled-data scheme with the backstepping technique for reducing control and transmission resources, is proposed. It is proven on the basis of Lyapunov stability analysis to be effective with the indoor temperature being able to track the target temperature accurately while all the closed-loop signals remain globally bounded even with practically time-varying AHUs system uncertainties and external disturbances. Simulation studies verify the efficacy of the proposed strategy and validate the established results.

This article investigates the predictor-based stabilization by noise for nonlinear systems. A novel concept, the noise-compensated auxiliary ordinary differential equation (ODE), is introduced to simulate system behavior, predict system state, and compensate for delay effects in the corresponding stochastic differential equation (SDE). Utilizing the auxiliary ODE, a predictor-based stabilizing noise is designed. Unlike conventional predictor-based schemes for stochastic systems, the proposed approach fully accounts for stochastic influences while generating state values that can be directly utilized for control. The proposed scheme is further applied to networked control systems (NCSs) under dual-channel packet loss, where the number of consecutive packet losses is allowed to be unbounded. In this way, the conventional assumption of a finite upper bound on packet loss is removed, and the system stability is guaranteed even under arbitrarily high-packet loss rates. To showcase the superiority of the proposed methodology, numerical simulations are conducted.

In this paper, the stability analysis and H∞controller design problem are investigated for a class of randomly sampled-data systems with uniformly bounded sampling errors and random denial-of-service (DoS) attacks, with an application to a fourth-order interleaved flyback module-integrated converter (IFMIC). First, a discrete-time stochastic model is formulated for the considered system affected by random sampling errors and DoS attacks. A stability condition that incorporates the expectation of a stochastic, nonlinear, and attack-dependent coupling matrix is then derived through a (reduced-order) Vandermonde matrix method. Subsequently, the existence of this expectation is established, followed by the application of the Kronecker product operation to effectively decompose the resulting matrix expectation. On this basis, an H∞synthesis algorithm is designed to guarantee the exponential mean-square stability and H∞performance of the discrete-time system. Furthermore, a special case considering only random sampling errors is analyzed, along with its corresponding H∞controller. Compared with existing studies, the proposed method yields a fixed-dimensional linear matrix inequality (LMI) condition that remains unaffected by the upper bound of consecutive DoS attacks and does not require reformulation when network conditions vary, thus enhancing both computational efficiency and implementation flexibility. Finally, the effectiveness of the proposed algorithm is demonstrated through simulation studies on a fourth-order IFMIC under different operations.

This paper proposes a data-driven self-triggered model predictive control (MPC) scheme for constrained linear time-invariant (LTI) systems that are unknown and affected by bounded process and measurement noise. The proposed scheme relies solely on initial input-output data and accounts for noise in both offline and online measurements. A self-triggered control policy is developed based on optimal input-output trajectories obtained by solving the data-driven MPC optimization problem. Recursive feasibility of the control scheme is guaranteed and practical exponential stability of the closed-loop system is established under sufficiently small noise level and certain technical conditions. Two simulation examples are provided to validate the effectiveness of the theoretical results.

The$H_{\infty }$static output feedback tracking control issue for discrete-time periodic Markov jump systems with quantization and packet loss is explored. The packet loss follows Bernoulli random distribution, which assumes that the packet is discarded in a probabilistic manner before being transmitted to the controller. On this basis, considering the restricted network bandwidth, a novel quantization-based packet loss compensation scheme using single exponential smoothing approach is firstly given to help offset the influence of network congestion and missing packets. Then, an output feedback tracking controller is firstly designed to minimize the tracking error between the system output and the given reference model output. Aiming at the loss of mode information, the tracking controller designed is partially mode-dependent. Furthermore, by giving a mode-dependent Lyapunov function with periodicity, the sufficient condition for the existence of this controller is derived to ensure the stability of the tracking error system with$H_{\infty }$performance. Ultimately, the effectiveness and practicality of the developed technique are demonstrated through an example of a single-link robotic arm model. Note to Practitioners—In real life, periodic systems generated by random mutations can be seen everywhere, such as economic systems. This type of system undergoes structural or parameter changes within a single operating period due to sudden changes in the external environment. Periodic Markov jump systems (PMJSs) can effectively describe complex systems with both periodic and stochastic characteristics. To address the adverse effects of quantization and packet loss, a new packet loss compensation strategy based on single exponential smoothing method and quantization is proposed. On the other hand, research on output feedback tracking control is crucial for fields such as missiles and spacecraft. To ensure that the system can operate according to the specified trajectory, a new design method for a periodic output feedback tracking controller has been proposed. And this type of controller effectively solves the tracking problem of PMJSs with quantization and packet loss.

This paper investigates how to stabilize unstable Markov jump systems with delay (MJSs-D) using non-Gaussian white noise based on sampled observations. First, the study addresses the noise stabilization problem of the corresponding delay-free Markov jump systems (MJSs). Using the Lyapunov function method and the stationary distribution of Markov chains, an aperiodic intermittent stochastic control based on Lévy noise (AISC-LN) is developed. The analysis of the low-order moment exponential stability of stochastic controlled systems is comprehensively based on the ergodicity of the Markov chain, making the derived criterion less restrictive than the traditional one underM-matrix-based conditions. Subsequently, leveraging the auxiliary system method and comparison principle, the AISC-LN based on discrete-time state and mode observations is proposed for MJSs-D. This strategy encompasses specific cases such as discrete-time feedback control and periodic intermittent control based on Brownian motion, furthermore highlights the stabilizing effects of Markov chains and Poisson white noise, thereby providing a definitive answer to the proposed problem. The new control scheme applies to MJSs-D with various types of relatively small delays. Finally, the feasibility and accuracy of the conclusions are validated through an example of a DC-DC buck converter circuit.

Toward exploring the positive impact of media debunking and random blocking on the spread of rumors, we discuss a stochastic hybrid control strategy that combines an individual and media debunking method, a continuous stochastic blocking method, and an impulse interruption method. Using stochastic analysis, the almost sure exponential stability of the controlled system is analyzed, along with the expression of control intensities. To balance rumor suppression, minimize control costs, and enhance the generality of control, a data-driven machine learning (ML) approach is developed to provide suboptimal control solutions. Numerical simulations based on two real-case datasets are carried out to validate the theoretical results and evaluate the potential impact of the model-based, data-driven stochastic hybrid control strategy.

This article presents the implementation of an asynchronous ${\mathcal {H}}_{\infty }$ filter for 2-D discrete-time periodic Markov jump systems with multiplicative noise. The study addresses the issue of missing measurements, which is treated as a stochastic variable following the Bernoulli random distribution. To account for the nonsynchronous phenomenon between the system and filter due to the loss of mode information, the periodic hidden Markov model (HMM) is introduced. Moreover, the transition rate matrix of the system and the conditional probability matrix of the filter are general, allowing for transition probabilities in fully known, partly known or fully unknown cases. The objective is to implement an asynchronous ${\mathcal {H}}_{\infty }$ filter based on periodic HMM that guarantees the filtering error system is mean-square asymptotically stable while maintaining a specified ${\mathcal {H}}_{\infty }$ disturbance attenuation performance. In the light of linear matrix inequalities, the article offers sufficient conditions for filter to exist and provides a solution for the parameters of the filter. Ultimately, a demonstration of the validity of the presented design technique is provided through the Darboux equation.

This study introduces a new asynchronous sampled-data distributed consensus control protocol for nonlinear fractional-order multiagent systems (MASs) containing system uncertainties along with time-varying disturbances. With strict consideration of the hereditary and infinite-memory characteristics of fractional-order systems, a novel adaptive backstepping-based distributed sampled-data control scheme is developed for individual agents with asynchronous sampling mechanisms. Through Lyapunov stability analysis, it is demonstrated that the proposed strategy guarantees the stability of the entire closed-loop system, meaning that all signals will remain within bounds and each agent can achieve output consensus with the specified time-varying reference trajectory. The efficacy of the proposed approach is illustrated through simulation studies, which also serve to validate the results obtained.

In compound fault diagnosis, the scarcity of samples leads to a low fault diagnosis rate. Existing zero-shot compound fault diagnosis methods lack the ability to simultaneously recognize both seen and unseen fault classes, especially when aligning attributes of unseen faults, where a dimensionality explosion occurs. To counter the deficiencies of traditional zero-shot compound fault diagnosis methods, this article introduces an innovative generalized zero-shot compound fault diagnosis approach. This approach pioneers the use of an attribute transformation strategy, establishing a knowledge bridge between single and compound faults through a semantic label definition module, thereby creating a fault attribute set. The fault generation module is then utilized to ingeniously convert the attribute set into training samples that are rich in fault characteristics. These samples are employed to train a multilabel classification module, enabling the model to identify compound faults, including those of unseen classes. Experiments conducted on two real-world bearing datasets have validated the effectiveness and strong generalization capabilities of this method, offering a novel solution and technical support for current zero-shot rotating machinery fault diagnosis.

The study focuses on finite-time stability (FTS) and stabilization problems in a class of stochastic distributed parameter systems with impulsive effects. To tackle the impulsive effects within the system, we adopt two key methods: 1) a looped form quasi-periodic Lyapunov function and 2) an interpolation quasi-periodic Lyapunov function method. This combined approach allows us to obtain a detailed mean-square FTS criterion, closely related to the specific dwell time of the impulse sequence in the system. Moreover, a finite-time input controller featuring a constant gain is designed to simultaneously stabilize both the continuous and discrete components of the controlled system. Finally, we present two numerical examples to demonstrate the validity of our outcomes.

In this article, the stability analysis and synthesis issues of networked control systems with asynchronous sensors and controllers are studied, where the stochastic variable obeying a certain probability distribution is introduced to characterize the clock offset between the sensor and the controller. First, a continuous-time framework covering random clock offsets and consecutively lost packets is established. An appropriate discrete-time stochastic augmented model is then constructed to investigate the analysis and synthesis problems of resulting continuous-time framework. Therefore, we prove that the stochastic stability of the discrete-time model implies the stochastic stability of resulting continuous-time system. Based on the discrete-time stochastic augmented model, the random and highly nonlinear terms are decoupled with the help of the law of total expectation, Kronecker product operation, and reduced-order confluent Vandermonde matrix. Subsequently, we present the stability condition in the form of linear matrix inequality and design the desired gain matrix such that the original continuous-time system is stochastically stable. Finally, two numerical examples and a practical example are utilized to clarify the practicability of the designed strategy.

This article focuses on the mean-square exponential stability of networked stochastic systems with time delays (NSSTDs) under nonlinear coupling and deception attacks, employing a sampled-data control strategy. A generalized Halanay inequality for NSSTDs is proposed to investigate the stability of the closed-loop system, where multiple time delays with different bounds are considered, with an incorporation of the graph theory. By the comparison of the continuous control system and the sample-data control system, the equivalence condition on the stabilities of the two systems is studied. Meanwhile, estimates for the maximum tolerable attack probability and the corresponding largest sampling period are obtained. Moreover, a qualitative analysis of various indicators for the deception attacks and the sampling period is revealed. Following this, the theorized results are applied to linear systems with multiple time delays under nonlinear coupling, and matrix inequalities for identifying the appropriate value of the control gain are provided by the generalized Halanay inequality. To show the correctness of the results, computational simulations are conducted.

This article addresses the stabilization of neutral stochastic delay systems (NSDSs) employing aperiodically intermittent controllers (APIC) based on delay feedback and asynchronous switching. To tackle issues arising from the neutral term, we introduce a special auxiliary system (AS) that is not a neutral system, and is distinct from existing literature 41. Utilizing the Lyapunov–Krasovskii functional approach and the iterative method, the stability criterion for the AS is given, which consists of the bound of three delay functions and the duty-cycle. If the stability criterion is satisfied, the AS will achieve mean-square exponentially stability, offering a viable APIC design scheme for non-NSDSs. Additionally, employing the equivalence technique (ET), this article obtains an additional bound for the system delay function, denoted by τ*. When the system delay function$\tau(t)<\tau^{*}$, we demonstrate that the NSDS with intermittent feedback is mean-square exponentially stable if the non-neutral AS is stable. This method is called as AS method based on non-neutral type (ASMbNT). With one comparison, this article reveals that the ASMbNT proposed in this article not only addresses the problem considered in 41, but also yields improved results. Lastly, to demonstrate the effectiveness and validity of the proposed approach, a numerical example is presented.

This article presents a novel theoretical framework for the finite-time synchronization (FTS) of complex dynamic networks (CDNs) under stochastic disturbances. Few studies have explored the combination of pinning impulsive control and pinning finite-time feedback control, with most finite-time feedback controls being designed globally rather than locally. Our approach integrates both pinning impulsive and pinning finite-time feedback strategies to achieve FTS of CDNs. We introduce a new impulse-type stochastic finite-time stability theory to demonstrate FTS in the presence of disturbances. Additionally, we propose criteria to ensure FTS and provide an explicit expression for the settling time, which is shown to be shorter than those in previous works. A numerical simulation is presented to validate the proposed methodology.

Recently, Large Language Models (LLMs) have achieved amazing zero-shot learning performance over a variety of Natural Language Processing (NLP) tasks, especially for text generative tasks. Yet, the large size of LLMs often leads to the high computational cost of model training and online deployment. In our work, we present ALTER, a system that effectively builds the multi-tAsk Learners with mixTure-of-task-adaptERs upon small language models (with <1B parameters) to address multiple NLP tasks simultaneously, capturing the commonalities and differences between tasks, in order to support domain-specific applications. Specifically, in ALTER, we propose the Mixture-of-Task-Adapters (MTA) module as an extension to the transformer architecture for the underlying model to capture the intra-task and inter-task knowledge. A two-stage training method is further proposed to optimize the collaboration between adapters at a small computational cost. Experimental results over a mixture of NLP tasks show that our proposed MTA architecture and the two-stage training method achieve good performance. Based on ALTER, we have also produced MTA-equipped language models for various domains.

The networked control systems (NCSs) under cyberattacks have received much attention in both industrial and academic fields, with rare attention on the delayed networked control systems (DNCSs). In order to well address the control problem of DNCSs, in this study, we consider the resilient event‐triggered safety control problem of the NCSs with time‐varying delays based on the switched observer subject to aperiodic denial‐of‐service (DoS) attacks. The observer‐based switched event‐triggered control (ETC) strategy is devised to cope with the DNCSs under aperiodic cyberattacks for the first time so as to decrease the transmission of control input under limited network channel resources. A new piecewise Lyapunov functional is proposed to analyze and synthesize the DNCSs with exponential stability. The quantitative relationship among the attack activated/sleeping period, exponential decay rate, event‐triggered parameters, sampling period, and maximum time‐delay are explored. Finally, we use both a numerical example and a practical example of offshore platform to show the effectiveness of our results.

In this paper, stabilization by aperiodically intermittent sampling stochastic noise is introduced as a new way of solving the consensus problem of a class of homogeneous multi-agent systems. A multiplicative noise is designed as a control input to stabilize the error system of the multi-agents. The average noise control rate (ANCR) is employed to estimate the working time of aperiodically intermittent noise, which reduces the conservativeness caused by the irregular noise working and rest time distribution. A novel piecewise analysis technique (PAT) is adopted to estimate the mean square of the error state, which allows for the larger noise sampling period. Meanwhile, the sufficient criteria to ensure almost sure stability of the error system are obtained, as well as the transcendental equation concerning the noise sampling period$\tau $and the ANCR$\Gamma $. Finally, the numerical simulation demonstrates the efficiency and accuracy of the proposed method.

This study examines the intermittent control of a rumor propagation system with anti-rumor mechanism. The interaction with the anti-rumor mechanism is investigated, including the existence and stability of two boundary equilibriums, the condition of bistability behavior. Threshold parameters are identified which determine the global exponential stability of the rumor-free equilibrium. To combat rumor spreading, we design deterministic and stochastic control strategies with aperiodically intermittent control time. The expressions of the minimum control intensities are obtained, which are related to the control ratio and system parameters. Numerical examples are carried out to verify the validity of the theoretical results and evaluate the potential roles of the intermittent control strategies.

In this article, the stochastic analysis and$H_{\infty }$controller design problems of networked systems with packet dropouts and false data injection attacks are investigated. Different from the existing literature, we focus on the linear networked systems with external disturbances and both sensor–controller channel and controller–actuator channel are studied. First, we present a discrete-time modeling framework that leads to a stochastic closed-loop system with randomly varying parameters. To facilitate the analysis and$H_{\infty }$control of resulting discrete-time stochastic closed-loop system, an equivalent yet analyzable stochastic augmented model is further constructed by matrix exponential computation. Based on this model, a stability condition is derived in the form of linear matrix inequality (LMI) with the aid of a reduced-order confluent Vandermonde matrix, Kronecker product operation, and law of total expectation. Specifically, the dimension of the LMI obtained in this article does not increase as the upper bound of consecutive packet dropouts does, which is also different from the existing literature. Subsequently, a desired$H_{\infty }$controller is obtained such that the original discrete-time stochastic closed-loop system is exponentially mean-square stable with a prescribed$H_{\infty }$performance. Finally, a numerical example and a direct current motor system are exploited to substantiate the effectiveness and practicability of the designed strategy.

In this article, the consensus problem of sampled-data second-order integrator multiagent systems with switching topology and time-varying delay is studied. And, a zero rendezvous speed is not required in the problem. Two new consensus protocols that employ no absolute states are proposed, depending on the presence of delay. Sufficient synchronization conditions are obtained for both protocols. It is shown that consensus can be reached, provided there is a sufficiently small gain and periodically joint connectivity in the sense of scrambling graph or spanning tree. Finally, both numerical and practical examples are supplied for illustrative purpose, and both show the effectiveness of the theoretical results.

In this article, the issue of event-triggered multiasynchronous$H_{\infty }$control for Markov jump systems with transmission delay is concerned. In order to reduce sampling frequency, multiple event-triggered schemes (ETSs) are introduced. Then hidden Markov model (HMM) is employed to describe multiasynchronous jumps among subsystems, ETSs, and controller. Based on the HMM, the time-delay closed-loop model is constructed. In particular, when triggered data are transmitted over networks, a large transmission delay may cause disorder of transmission data such that the time-delay closed-loop model cannot be developed directly. To overcome this difficulty, a packet loss schedule is presented and the unified time-delay closed-loop system is obtained. By the use of the Lyapunov–Krasovskii functional method, sufficient conditions with the controller design are formulated for guaranteeing the$H_{\infty }$performance of the time-delay closed-loop system. Finally, the effectiveness of the proposed control strategy is demonstrated by two numerical examples.

Time-varying quadratic programming (TV-QP) is widely used in artificial intelligence, robotics, and many other fields. To solve this important problem, a novel discrete error redefinition neural network (D-ERNN) is proposed. By redefining the error monitoring function and discretization, the proposed neural network is superior to some traditional neural networks in terms of convergence speed, robustness, and overshoot. Compared with the continuous ERNN, the proposed discrete neural network is more suitable for computer implementation. Unlike continuous neural networks, this article also analyzes and proves how to select the parameters and step size of the proposed neural networks to ensure the reliability of the network. Moreover, how to achieve the discretization of the ERNN is presented and discussed. The convergence of the proposed neural network without disturbance is proven, and bounded time-varying disturbances can be resisted in theory. Furthermore, the comparison results with other related neural networks show that the proposed D-ERNN has a faster convergence speed, better antidisturbance ability, and lower overshoot.

This study is concerned with the$H_{\infty }$control issue of networked systems with consecutively lost packets using sampled-data. First, we establish a discrete stochastic system for the networked system with external disturbances and consecutively lost packets. To enable$H_{\infty }$performance analysis, an equivalent but analyzable stochastic framework is then derived by using matrix exponential computation. Subsequently, by law of total expectation, Kronecker product operation, and eigenvalue decomposition approach, we compute the expectation of a coupling term with significant nonlinearity and randomness. Based on this, a stabilization controller is constructed that ensures the resulting discrete stochastic system’s exponential mean-square stability with a prescribed$H_{\infty }$performance. Unlike the existing literature, the linear matrix inequalitys (LMIs) dimension derived in this article does not change along with the maximum number of consecutively lost packets, which prevents an LMI with high-computing complexity. Finally, the validity and applicability of the algorithm are demonstrated by a numerical example and an example using a satellite system.

The study considers the strategies for multiple high-order integrator agents to reach an agreement using local measurements and local communications over networks with time-varying connectivity and delays. Two new consensus protocols are presented using only relative states and parameterized weights in polynomial form, one for networks where the connectivity is time varying, the other for networks where connectivity and delays are both time varying. Analysis shows the ensuring of consensus as well as the weights’ existence for both protocols, provided that the network connectivity is jointly and periodically kept as scrambling or with directed spanning tree. A new analysis method, termed as block seminorm is employed in the consensus analysis, and shows less conservative in parameter validation. Finally, both numeric simulations and experiments show the proposed consensus schemes’ effectiveness.

The input-to-state stability in p-th moment (p-ISS) and the integral input-to-state stability in p-th moment (p-iISS) of the nonlinear switched systems (NSS) with modedependent event-triggered mechanism (ETM) are shown in this paper. According to the designed ETM and utilizing the mode-dependent average dwell time (MDADT), we can rule out the Zeno phenomenon. Then one applies the theoretical consequences to nonlinear systems and derive a class of ETM by linear matrix inequality (LMI). In the end, the examples and some simulations are demonstrated.

The neural-network (NN)-based state estimation issue of Markov jump systems (MJSs) subject to communication protocols and deception attacks is addressed in this article. For relieving communication burden and preventing possible data collisions, two types of scheduling protocols, namely: 1) the Round-Robin (RR) protocol and 2) weighted try-once-discard (WTOD) protocol, are applied, respectively, to coordinate the transmission sequence. In addition, considering that the communication channel may suffer from mode-dependent probabilistic deception attacks, a hidden Markov-like model is proposed to characterize the relationship between the malicious signal and system mode. Then, a novel adaptive neural state estimator is presented to reconstruct the system states. By taking the influence of deception attacks into performance analysis, sufficient conditions under two different scheduling protocols are derived, respectively, so as to ensure the ultimately boundedness of the estimate error. In the end, simulation results testify the correctness of the adaptive neural estimator design method proposed in this article.

This article is concerned with the stability and stabilization of nonlinear hybrid stochastic systems. First, the concepts of synchronous and asynchronous switching parameters to the system states are proposed for a generalization of the asynchronous control. As preliminaries, a hybrid nonlinear differential inequality is established for coping with the synchronous models, and the [Formula: see text]-continuity of the system parameters driven by Markov chains is established for coping with the asynchronous models by the attribute of Markov chains for less conservativeness. Second, stability criteria with moment exponential estimates are established for two kinds of system models under the nonlinear growth condition. Third, the stabilization problem is illustrated based on the stability criteria and Riccati like matrix equations. A corollary is given to improve some stability theorems obtained in the related literature. New initial condition is formally proposed and explained for the nonlinear stochastic systems. Finally, a numerical example with simulation is proposed to illustrate the method, verify the conclusions of this article, and show the superiority of this work.

In this article,${H}_{\infty }$control for stochastic singular time-varying delay systems under arbitrarily variable samplings is addressed via designing a sampled-data controller. The first and foremost, a novel time-dependent discontinuous Lyapunov–Krasovskii (L–K) functional is built, which takes good advantage of the factual sampling pattern’s available properties. Then, based on the refined input delay method by utilizing the constructed time-dependent L–K functional, the free-weighting matrix method, and the auxiliary vector function approach are adopted to develop conditions ensuring the stochastic admissibility for the studied stochastic singular systems with time-varying delays. On the basis of the derived conditions, the sampled-data${H}_{\infty }$control issue is tackled, and an unambiguous expression for the sampled-data controller design method is obtained. Finally, simulation examples manifest that our proposed results are correct and effective.

This article investigates event-triggered and self-triggered$\mathcal {L}_{\infty }$control problems for the Markov jump stochastic nonlinear systems subject to denial-of-service (DoS) attacks. When attacks prevent system devices from obtaining valid information over networks, a new switched model with unstable subsystems is constructed to characterize the effect of DoS attacks. On the basis of the switched model, a multiple Lyapunov function method is utilized and a set of sufficient conditions incorporating the event-triggering scheme (ETS) and restriction of DoS attacks are provided to preserve$\mathcal {L}_{\infty }$performance. In particular, considering that ETS based on mathematical expectation is difficult to be implemented on a practical platform, a self-triggering scheme (STS) without mathematical expectation is presented. Meanwhile, to avoid the Zeno behavior resulted from general exogenous disturbance, a positive lower bound is fixed in STS in advance. In addition, the exponent parameters are designed in STS to reduce triggering frequency. Based on the STS, the mean-square asymptotical stability and almost sure exponential stability are both discussed when the system is in the absence of exogenous disturbance. Finally, two examples are given to substantiate the effectiveness of the proposed method.

The security control issue of nonlinear networked systems is considered in this article on the basis of interval type-2 fuzzy modeling strategy. For avoiding communication congestion, a stochastic scheduling strategy called Markovian communication protocol is introduced to coordinate the sensor transmission order. An asynchronous observer is designed for estimating the unmeasured states via the hidden Markov model. In addition, a more comprehensive scenario on deception attack is considered, in which attacks occur both in the sensor–observer and controller–actuator communication channels with different types of deception signals. In view of slack matrix approach and stochastic analysis technique, some sufficient conditions for ensuring the ultimately boundedness of the resulting closed-loop system are obtained. In the end, simulations show the validity of the proposed protocol-based fuzzy control method.

This article is concerned with the neural network (NN)-based event-triggered control problem for discrete-time networked Markov jump systems with hybrid cyberattacks and unmeasured states. The event-triggered mechanism (ETM) is used to reduce the communication load, and a Luenberger observer is introduced to estimate the unmeasured states. Two kinds of cyberattacks, denial-of-service (DoS) attacks and deception attacks, are investigated due to the vulnerability of cyberlayer. For the sake of mitigating the impact of these two types of cyberattacks on system performance, the ETM under DoS jamming attacks is discussed first, and a new estimation of such mechanism is given. Then, the NN technique is applied to approximate the injected false information. Some sufficient conditions are derived to guarantee the boundedness of the closed-loop system, and the observer and controller gains are presented by solving a set of matrix inequalities. The effectiveness of the presented control method is demonstrated by a numerical example.

The$H_{\infty }$filtering problem for discrete-time periodic Markov jump systems with quantized measurements and packet loss compensation is addressed in this article. The stochastic packet loss phenomenon, which arises from the plant to the filter, obeys Bernoulli distribution. Then, aiming at the phenomenon that the system performance is degraded or even unstable due to packet loss, a new packet loss compensation strategy is proposed, which adopts the single exponential smoothing method. Considering the limited communication channel, a static logarithmic quantizer with mode-dependent property is used to quantify the measured output. Besides, since the system modes are not always fully available, a quantized periodic filter, which is partially mode-dependent, is constructed to guarantee that the filtering error system is stochastically stable. Furthermore, by constructing a periodic Lyapunov function with mode-dependent property, the existence conditions of periodic filter are presented. Eventually, to illustrate the usefulness of the proposed approach, a practical example of a boost converter is presented.

In article, we study the problem of aperiodically intermittent control (APIC) for neutral stochastic delay systems (NSDSs) based on discrete observations. To overcome the difficulty caused by intermittent control, an auxiliary system is introduced. By using the Lyapunov function method, an upper bound of observation period$\delta ^{*}$is obtained. If observation period$\delta < \delta ^{*}$, then the auxiliary system is$p$th$(p\geq 2)$-moment exponentially stable. In addition to the fixed observation period$\delta < \delta ^{*}$, this article gives a method to design an aperiodically intermittent controller and obtains a lower bound of duty cycle for all fixed$0 < \underline {T}\leq \overline {T}$with$\underline {T}$and$\overline {T}$being lower bound and upper bound of control frames. That is, we proved the NSDSs with the intermittent discrete observation controller is$p$th$(p\geq 2)$-moment exponentially stable if the auxiliary system is$p$th$(p\geq 2)$-moment exponentially stable. We call this method the auxiliary system method (ASM). In fact, different from mainstream techniques, the ASM used in this article can handle the case of$0 < \underline {T}\leq \overline {T} < \delta $even if$\delta $is small enough. Besides, this article reveals one interesting phenomenon: classic methods may lead to error accumulation, which cannot be avoided in APIC or periodically intermittent control (PIC) for NSDSs. Finally, one numerical example, one application, and one comparison are given to show the usefulness and correctness of the proposed results.

The majority of modern speaker verification systems take spectral analysis-based features as input, which contains multiple frequency bins. Naturally, there would be a question of whether all different frequency bins contribute equally to the speaker verification system performance? In this paper, we propose the frequency reweighting layer (FRL) to automatically learn and balance the importance of different frequency bins. This new layer can be freely inserted into the original speaker embedding learner once or multiple times at different layers, with an ignorable number of new parameters. Based on the proposed novel architecture, a set of experiments are designed and carried out on the VoxCeleb1 dataset, which not only achieves superior performance but also exhibits an interesting weight distribution – the lower frequencies matter more.

This article deals with the problem of$H_{\infty }$and$l_{2}-l_{\infty }$filtering for discrete-time Takagi–Sugeno fuzzy nonhomogeneous Markov jump systems with quantization effects, respectively. The time-varying transition probabilities are in a polytope set. To reduce conservativeness, a mode-dependent logarithmic quantizer is considered in this article. Based on the fuzzy-rule-dependent Lyapunov function, sufficient conditions are given such that the filtering error system is stochastically stable and has a prescribed$H_{\infty }$or$l_{2}-l_{\infty }$performance index, respectively. Finally, a practical example is provided to illustrate the effectiveness of the proposed fuzzy filter design methods.

This article investigates the dynamic consensus problem for the discrete-time second-order integrator networked multiagent system with time-varying delay and switching topology, in which the speed of each agent is not required to be synchronized to zero value. Novel consensus protocols using only relative state information are proposed, and sufficient conditions for dynamic consensus are derived. The results show that consensus can be reached for both the case with delay and the case without delay, if the gain is sufficiently small and the union of interaction graphs is scrambling or contains a spanning tree frequently enough as the system evolves. Numerical examples demonstrate the effectiveness of the theoretical results.

In this article, the event-triggered resilient$\mathcal {L}_{\infty }$control problem is concerned for the Markov jump systems in the presence of denial-of-service (DoS) jamming attacks. First, a fixed lower bound-based event-triggering scheme (ETS) is presented in order to avoid the Zeno problem caused by exogenous disturbance. Second, when DoS jamming attacks are involved, the transmitted data are blocked and the old control input is kept by using the zero-order holder (ZOH). On the basis of this process, the effect of DoS attacks on ETS is further discussed. Next, by utilizing the state-feedback controller and multiple Lyapunov functions method, some criteria incorporating the restriction of DoS jamming attacks are proposed to guarantee the$\mathcal {L}_{\infty }$control performance of the event-triggered Markov closed-loop jump system. In particular, the bounded transition rates rather than the exact ones are taken into account. That is appropriate for the practical environment in which transition rates of the Markov process are difficult to measure accurately. Correspondingly, some criteria are proposed to obtain state-feedback gains and event-triggering parameters simultaneously. Finally, we provide two examples to show the effectiveness of the proposed method.

The sampled-data stabilization problem of nonlinear stochastic singular systems on the basis of the Takagi–Sugeno fuzzy models under variable samplings is discussed in this article. A new piecewise Lyapunov–Krasovskii functional is constructed, which can capture the actual sampling mode’s available features more fully, and an enhanced input-delay method is presented. By using the proper augmented scheme based on the auxiliary vector function, the new mean square admissibility criteria are derived by making good use of the convex combination techniques and the free weighting matrix approach. It is shown that the obtained results in this article contain less conservatism when compared with the existing ones. The superiority and correctness of our results are verified by an application example of a truck–trailer model.

This article is concerned with$H_\infty$control problem for discrete-time Takagi–Sugeno fuzzy Markov jump systems with event-triggering scheme (ETS) and denial-of-service (DoS) attacks. Aperiodic DoS attacks characterized by duration and frequency are introduced. In the light of DoS attacks and event-triggered fuzzy controller, the switched fuzzy Markov jump closed-loop system is established. Particularly, in order to address the issue of mismatched behavior between membership functions of fuzzy system and fuzzy controller, a novel ETS consisting of membership functions is developed. Then, with the help of multiple Lyapunov function method and iterative technique, some sufficient conditions are provided to ensure the$H_\infty$performance of the resulting closed-loop system. Subsequently, the explicit parameter design of controller and ETS is provided. Finally, an example is employed to verify the validity of the proposed theoretical method.

The stability issue of stochastic hybrid systems against energy-constrained denial-of-service (DoS) attacks is investigated in this article. A sampled-data-based Round-Robin protocol, which sends measurement data cyclically according to the predetermined transmission sequence, is introduced to avoid communication network congestion. Additionally, a new switched time-delay stochastic closed-loop system with an unstable subsystem is established by discussing the effect of DoS attacks. Then, the stability of the closed-loop system is discussed in light of the piecewise Lyapunov–Krasovskii functional method and stochastic analysis technique. Finally, two examples are included to illustrate the correctness and applicability of the proposed method.

In this article, we apply the active disturbance rejection control (ADRC) to the output tracking of a class of lower triangular nonlinear systems subject to mismatched bounded stochastic disturbances of unknown statistic characteristics and nonvanishing at the origin. A major obstacle is that the paths of the stochastic disturbances are nowhere differentiable almost surely which causes that the stochastic disturbances cannot be refined into the control input channel by the usual way of state transformation. To overcome this obstacle, a set of second-order extended state observers is first designed to estimate, in real time, the disturbance in each channel, and then a backstepping ADRC based on feedforward compensation and a constructive backstepping procedure is developed, guaranteeing that the closed-loop output tracks a time-varying reference signal in practically mean square and the closed-loop states are practically bounded in probability first defined in this article. Finally, some numerical simulations are presented to validate the effectiveness of the proposed backstepping ADRC approach.

To realize the robust control of multirotor unmanned aerial vehicle (UAV) systems, adaptive multilayer neural dynamics (AMND) controllers are proposed and analyzed. The proposed AMND controllers with the strong anti-perturbation property can drive multirotor UAVs to track time-varying tasks and deal with parameter uncertainty problems. First, the design method of the general multilayer neural dynamics (MLND) controllers is introduced and analyzed. Second, based on the design method, the attitude angles, height, and position controllers of a UAV system are designed. Third, according to the adaptive control theory, a novel AMND controller is designed, which can self-tune the parameters of the UAV. Finally, the proposed AMND method applies to a real-world hexrotor UAV system to illustrate its reliability. Mathematical analysis, computer simulations, and experiments verify the reliability, stability, and effectiveness of the proposed controllers which are used to track time-varying tasks.

This paper studies the modeling and synchronization problems for stochastic complex dynamical networks subject to consecutive packet dropouts. Different from some existing research results, both probability characteristic and upper bound of consecutive packet dropouts are involved in the proposed approach of controller design. First, an error dynamical network with stochastic and bounded delay is established by step-delay method, where the randomness of the bounded delay can be verified later by the probability theory method. A new modeling method is introduced to reflect the probability characteristic of consecutive packet dropouts. Based on the proposed model, some sufficient conditions are proposed under which the error dynamical network is globally exponentially synchronized in the mean square sense. Subsequently, a probability-distribution-dependent controller design procedure is then proposed. Finally, two numerical examples with simulations are provided to validate the analytical results and demonstrate the less conservatism of the proposed model method.

In this paper, we discuss the modeling and stabilization problems for a class of discrete-time stochastic networked systems (DSNSs) subject to successive packet losses. The system under consideration is transformed into a stochastic one with bounded stochastic delay. Considering that the stochastic delay is characterized by nonuniform distribution, a new equivalent model is then constructed that enables the DSNS's controller design to benefit from knowing the probability characteristic of packet losses. Specially, to verify this feature, the probabilities of the delay can be explicitly obtained by utilizing the formula of total probability, which is critical to model transformation and analyze practical problems that exist in DSNSs. Based on the proposed model, sufficient conditions are established under which the globally mean-square asymptotic stability of resulting stochastic delay closed-loop system is guaranteed, and a delay-distribution-dependent design procedure is then proposed. A numerical example with simulation is provided to validate the analytical results and demonstrate the effectiveness of the design procedure.

Event-triggered schemes are characterized in less communication traffic while maintaining the resulting controlled plant's desired stability and performance criteria. In the presence of packet dropouts, this paper is concerned with the modeling and control problems for a class of discrete-time stochastic systems with event-triggered schemes. Two different mathematical analysis methods are proposed to model the packet loss when an event-triggered scheme is subject to packet loss. First, a stochastic distributed sequence satisfying the Bernoulli process is utilized to model the triggered packets transmitted in the communication networks. Considering the difference between time-triggered and event-triggered schemes, an equivalent model with a random sequence not satisfying a Bernoulli distribution process is also analyzed, which is not the same as some existing results in the literature. Then, the mean-square exponential stability of resulting augmented system is guaranteed and the prescribed H∞performance level is achieved by solving resulting discrete P-problem. Two examples with simulations are provided to validate the analytical results and demonstrate the effectiveness of the proposed co-design techniques.

This paper investigates the H∞filtering problem for nonhomogeneous Markovian jump repeated scalar nonlinear systems with multiplicative noises and partially mode-dependent (PM) characterization. A new PM H∞filter is proposed, which guarantees the stochastic stability of the filtering error systems. The transition probabilities (TPs) of the nonhomogeneous Markovian process are assumed to be polytopic and the probability for successful transmission of mode information is characterized by a Bernoulli distributed sequence. By constructing the Lyapunov functional method, the existence conditions of filter are presented. Finally, the efficiency of the obtained results for PM H∞filter are demonstrated by an economic system example.

Correlation filter has demonstrated the power in object tracking, benefiting from its superior speed and competitive performance. However, existing correlation filter based trackers (CFTs) are fragile for some inherent defects caused by the boundary effect. To address this issue, we propose a novel correlation filter based tracking framework by integrating three highly collaborative components, including a fast target proposal module, a distractor-aware filter, and a correlation filter based refiner. Specifically, the target proposal aims at determining some target-like regions in contexts efficiently, which provides target-like patches to learn a distractor-aware filter and detect. Multi-region strategy enlarges space fields for learning and prediction. The filter learned from both target and distractors enhances its ability to identify background. Therefore, our method is capable of evaluating multiple candidates in wider context with less risk of drifting to distractors, namely multi-anchor detection. Besides, the proposed Proposal-Detect-Refine hierarchical searching process progressively achieves data alignment between testing and training samples, which benefits for reliable model prediction. A refiner is used to fine-tune positions after multi-anchor detection for lessening error accumulation and preventing model from drifting. Comprehensive experiments on five challenging datasets, i.e. OTB2013, OTB2015, VOT2017, VOT19, and TC128, demonstrate that the proposed method achieves superior performance against the state-of-the-art methods.

This paper investigates the mean square consensus problem for continuous-time multiagent systems with randomly switching topologies and noises. The switching is governed by a time-homogeneous Markov process, and each topology corresponds to a state of the process. Meanwhile, the communication noises are also considered for practical applications. We introduce a time-varying gain which can reduce the effect of communication noises. It is shown that the effect of Markovian switching topologies mainly depends on the union of topologies associated with the positive recurrent states of the Markov process. Then, necessary and sufficient conditions can be obtained under a control protocol with time-varying gain. Moreover, we extend our result to the cases where the topological structure is semi-Markovian switching and the elements of transition rate matrix are partly unknown. Finally, we give an example to illustrate the validity of our results.

Finger vein modality has unique advantages, allowing it to play an important role in biometrics. However, the approach to vein imaging and information acquisition typically adopted in current vein verification systems employs a monocular camera to acquire a single-view 2D vein image from only one side of the finger, which causes two problems: it acquires limited vein pattern information for verification, and it causes clear differences among samples of the same subject captured from different finger positions in contact-free mode. Both of these problems have adverse effects on system performance. In general, existing systems are more sensitive to positional variations of the finger, particularly those caused by pitch and roll movements. This concern remains a challenge despite considerable efforts to address it in recent years. To provide a fundamental solution to the above issues, we propose an entirely new system, which includes a software and hardware platform that collects a full-view of the vein pattern information from whole fingers with three cameras, a novel 3D reconstruction method to build the full-view 3D finger vein image, and a corresponding 3D finger vein feature extraction and matching strategy based on a lightweight convolutional neural network (CNN) with depthwise separable convolution. Experimental results demonstrate the potential of our proposed system and show that compared to the traditional single-view 2D mode of finger vein recognition, the new system both efficiently improves the recognition performance and simultaneously takes full advantage of additional valid information provided by the finger vein biometrics.

This paper considers the sample-data-based consensus problem of multiagent systems with time-varying delay and packet losses. To distinguish the time delays caused by network-induced time-delay and packet losses, the switched system is utilized. A lower gain controller is designed based on the solution of a parametric algebraic Riccati equation. The Lyapunov stability theory is utilized to obtain the limitations on the frequency and the duration of packet losses which guarantees the consensus of multiagent systems. On the other hand, we theoretically prove that refined time-delay function can reduce the conservatism. A simulation example is given to illustrate the effectiveness of the proposed method.

Finger vein pattern has been proven to be an effective biometric for personal identification in recent years. Nevertheless, there remain challenges that need to be solved, such as finger-vein features that lack robustness and expressiveness. In this paper, we propose a deep convolutional neural network (CNN) model, named the Finger-vein Network (FV-Net), to learn the features representative of a finger vein that is more discriminative and robust than handcrafted features. Next, to address the issue of translation and rotation in vein imaging, we propose a template-like matching strategy while designing the top architecture of the FV-net to extract features with spatial information. Finally, the extensive experimental results show that our proposed method can achieve excellent performance on several public datasets.

Although promising results have been achieved for human action recognition under well-controlled conditions, it is very challenging to recognize human actions in realistic scenarios due to increased difficulties such as dynamic backgrounds. In this paper, we propose to take multimodal (i.e., audiovisual) characteristics of realistic human action videos into account in human action recognition for the first time, since, in realistic scenarios, audio signals accompanying an action generally provide a cue to the nature of the action, such as phone ringing to answering the phone . In order to cope with diverse audio cues of an action in realistic scenarios, we propose to identify effective features from a large number of audio features with the generalized multiple kernel learning algorithm. The widely used space-time interest point descriptors are utilized as visual features, and a support vector machine is employed for both audio- and video-based classifications. At the final stage, fuzzy integral is utilized to fuse recognition results of both audio and visual modalities. Experimental results on the challenging Hollywood-2 Human Action data set demonstrate that the proposed approach is able to achieve better recognition performance improvement than that of integrating scene context. It is also discovered how audio context influences realistic action recognition from our comprehensive experiments.

The variable structure control problem of a class of stochastic distributed parameter system with uncertainty is discussed. Variable structure dynamic equation is established by employing nonlinear transformation. The stability character has been analyzed. Based on previous conclusion, the variable structure regulator is designed for the system.

The premise to evaluate the performances of a queuing system is based on knowing the distributions of customer arrival or service time in advance. It is very important to identify probability distributions or estimate density functions fast and efficiently. Support vector machine (SVM) based on statistical learning theory has been used generally in machine learning because of its good generalization ability. By using SVM we can classify and identity some probability distributions appeared in queuing system and solve the density function regression problem through using support vector regression (SVR). Some other problems need to be solved are formulated in the end.

Sliding mode control problem for a class of stochastic partial differential system with time-delay are studied. Variable structure controllers are designed for the system. The existence of sliding mode motion is shown. And the stability character is analyzed.

In this paper, sufficient condition for stability of linear stochastic system with distributed parameter is discussed. The main idea of this paper is to discuss stability of the kind of system by analyzing solution of the partial differential equations in one dimension and stability by integrating about spatial variables in high dimensions. Simulation of application illustrates at the end of the paper.

Standard genetic algorithm conducts probabilistic parallel searches for the best chromosome by repeating generate-and-test processes, which completely ignore experiences gained during individuals' lifetime. Such inborn defect, however, is fully intact under conventional improvements. In this paper, a novel self-adaptive generalized GA based on Baldwin effect is proposed. A fourth operator Baldwin learning, is introduced. All members must perform Baldwin learning before they enter into the gene pool for further crossover and mutation. Besides, mechanisms of "inbreeding is forbidden" and activation are built in to solve problems of crowding and slow convergence. Finally, the kernel of inconsistent self-adaptive GA is also fused into this new algorithm. Application to a benchmark problem shows the new algorithm is feasible and highly effective.

The fuzzy SOFM-GIS space cluster model is a new cluster model based on theory of fuzzy, self-organizing feature map network and GIS. Fuzzy theory is used to solve uncertain problems. GIS is of strong space analysis ability. Self-organizing feature map network are of ability of strong self-learned, self-adaptive, error-permissive ability and model identification. The model is adapted to solve cluster problem of multiple objects under multi-index condition in fuzzy or uncertain systems and these systems are of space characteristic. It is used to classify the red soil resource in Zhejiang province. The results show that the model is stable and reliable.