This paper investigates the sensor fault detection and diagnosis problems for lithium-ion battery systems. A combined model-based and data-driven approach is proposed to detect and diagnose four types of sensor faults, i.e., bias fault, drift fault, accuracy degradation and complete fault. Firstly, an optimal model-based fault detection filter is designed to obtain the residual data, which is utilized to extract the features of the sensor faults. Secondly, a data-driven method is developed to realize the fault detection and diagnosis, which adopts the technique of short-time Fourier transform (STFT) to generate a multi-window STFT-based fault detection and diagnosis algorithm. By employing appropriate multiple windows, the trade-off between time-domain and frequency-domain accuracy is achieved, allowing for a more precise characterization of the faults. Compared with the traditional fault detection and diagnosis techniques, the developed approach is more general and applicable to a broader range of sensor faults. Finally, a physical experiment is conducted for validating the effectiveness and practicality of the proposed method.

This paper introduces a discrete-time resilient event-triggeredH∞estimator method for autonomous vehicles (AVs) while effectively addressing important-data-based (IDB) attack strategy. An IDB attack method is first developed from an adversary’s perspective to amplify its destructive potential in critical states of the AVs system. To mitigate these threats, a discrete-time resilientH∞estimator is designed to counteract the effects of the attack model. Using the application of the Lyapunov function approach, sufficient conditions are derived to guarantee the asymptotic stability of the augmented error dynamics of AVs system and ensureH∞disturbance suppression. Simulations results further validate the proposed IDB attack strategy has numerous destructiveness on the AVs system than some existing attack models. Meanwhile, the designed resilientH∞estimator demonstrate its capability to mitigate the adverse impacts of IDB attack strategy while preserving overall AVs system performance.

This paper proposes a data-driven detection framework for false data injection (FDI) attacks in cyber-physical DC microgrids. First, a novel data-driven characterization framework for cyber-physical DC microgrids is presented, eliminating the need for restrictive assumptions such as the invertibility of stacked data matrices and quasi-stationary linear approximations. Subsequently, a data-drivenL∞ estimator is designed for real-time monitoring of the states of distributed generation units (DGUs), capable of adapting to noise-corrupted measurements. Meanwhile, a two-stage model-free scalable (TSMFS) attack detection framework is proposed. In the offline stage, historical data are leveraged to construct a set of linear inequalities whose solution yields a residual generator. The online stage generates detection residuals exclusively from received measurements, without relying on coupling information from neighboring DGUs, thus endowing the scheme with inherent scalability. Finally, experiments validate the performance of the data-driven estimator and the effectiveness of the proposed TSMFS detection framework.

This article explores the problems of the attack-defense framework of power systems; first, an innovative voltage-sensitivity-based (VSB) attack scheduling method is proposed from the perspective of the attacker, with the aim of maximizing disruption to the target bus in the power system. To counteract the introduced VSB attack strategy, a digital second-order generalized integrator phase-locked loop (SOGI-PLL) compensatory mechanism is established to mitigate the adverse effects of the disruption. In the initial phase, the relationships between bus voltages and currents are analyzed, forming the basis for calculating the sensitivity of the target bus to each bus. Building upon this analysis, a novel VSB attack strategy is introduced, allocating higher attack energy to more sensitive buses with the purpose of maximizing the attack disruption of the target bus. Subsequently, a detection-compensation defense framework is established to find and make up for the malicious attack. Specifically, a SOGI-PLL compensation method is introduced to mitigate the negative effect of the attack. Finally, the effectiveness of the attack design method and defense strategy is validated through simulation experiments conducted on the IEEE 14-bus and 39-bus systems.

This paper investigates the distributed formation control problem of second-order multiagent systems (MASs) subject to unknown nonlinear dynamics. A predictor-based fuzzy learning framework is developed, in which fuzzy logic systems (FLSs) approximate the unknown nonlinear dynamics and a two-layer learning mechanism accelerates weight adaptation by utilizing the velocity prediction error. A distributed observer is designed for jointly connected switching directed topologies to handle limited leader accessibility, allowing agents to estimate the global reference trajectory using solely local information. Furthermore, an actor–critic reinforcement learning architecture is integrated to realize distributed optimal formation control, in which the value function is reformulated with a compensatory term that exploits available system structural information, thereby enhancing policy optimization efficiency and accelerating convergence. Simulation results demonstrate that the proposed approach exhibits improved robustness and accuracy in both dynamics approximation and position tracking when compared with conventional learning-based methods.

This article investigates the security challenges associated with integrated attack-defense strategies for multiarea power systems (MAPSs). Traditional attack models often employ indiscriminate approaches that overlook the heterogeneous significance of different area subsystems. In practice, however, these areas contribute unequally to the overall stability and security of MAPSs. To address this gap, an area-significance-driven attack model is introduced, in which the importance of each area is quantitatively evaluated based on its contribution to global system stability and operational security. This targeted approach enables adversaries to maximize system disruption by selectively exploiting high-impact components. In parallel, an adaptive defensive control strategy is proposed to dynamically respond to the characteristics of detected attacks, thereby mitigating damage and enhancing system resilience. By coupling targeted attack modeling with adaptive defensive control, a unified framework is established that characterizes the interactive dynamics between adversarial threats and system responses in MAPSs. Finally, simulations on a three-area power system confirm the disruptive potential of area-significance-driven attacks and demonstrate the effectiveness of the proposed defense strategy in maintaining system-wide stability under adversarial conditions.

This article investigates the decentralized adaptive event-driven (AED) reliability control problem for nonlinear interconnected systems (NISs) with privacy-preserving. The decentralized optimal control strategy for the whole system is formulated by the optimal control for nominal subsystems, where an AED homomorphic cryptosystem is implemented for each subsystem to alleviate network burden while achieving security by encrypting transmitted signals. The Paillier cryptosystem with additive homomorphic properties is introduced to conceal the original data. Therefore, the transformed Hamilton–Jacobi–Bellman equations (HJBE) are constructed to facilitate cooperative optimization across subsystems within the framework of reinforcement learning. Subsequently, we leverage single critic networks to derive solutions to the HJBE, utilizing the experience replay approach for weight updates. Furthermore, by virtue of the Lyapunov function, the derived decentralized control law can force the whole NIS to be uniformly ultimately bounded stable. Eventually, numerical examples of NISs are provided to illustrate the effectiveness of the proposed optimization algorithm.

This study proposes a novel methodology driven by adaptive dynamic programming (ADP) to achieve optimal fault-tolerant tracking control (FTTC) in affine nonlinear systems (ANSs) subject to actuator failures. To begin with, a key innovation lies in constructing an extended system model that integrates tracking error dynamics and desired trajectory, establishing a basis for control synthesis. For this extended system, a critic neural network (NN) with adaptive weights is designed to approximate the discounted value function, enabling online optimization of control policies. Additionally, to address practical network constraints, a bandwidth-efficient event-triggered scheme (BEETS) is introduced to optimize limited bandwidth resources in networked control setups. Meanwhile, an NN based fault observer realizes real-time identification and dynamic compensation of actuator faults, mitigating their adverse effects on system performance. The resulting FTTC framework, which merges ADP-based optimal control with a fault compensator, ensures robust fault tolerance under both constant and time varying fault scenarios. Through rigorous Lyapunov stability assessment, uniform ultimate boundedness (UUB) is proven to be achieved for the closed-loop nonlinear system, guaranteeing stable tracking performance. The validity of this algorithm is verified through rigorous theoretical analysis and captivating numerical simulations.

This article proposes a synergistic model-based actor–critic (AC) learning framework for secure vehicle tracking under a dynamic event-triggered scheme (DETS) and subject to deception attacks. To explicitly resolve the conflict between communication conservation and learning convergence, the AC algorithm is structurally embedded within a discrete linear quadratic tracker (LQT) and backstepping architecture. This co-design utilizes the nominal linear structure as a rigorous prior to analytically derive update laws, ensuring stable Hamilton–Jacobi–Bellman approximation even when data are sparse and stochastically corrupted. By bounding the policy search space, it ensures a minimal number of learnable parameters and a clear physical interpretation. Lyapunov analysis rigorously proves the asymptotic convergence of the tracking errors to zero. Numerical simulations demonstrate superior tracking accuracy, faster convergence, and significant bandwidth savings compared to standard model-free reinforcement learning baselines.

This article investigates the security of a class of nonlinear systems subject to denial-of-service (DoS) attacks from an adversarial, data-aware perspective. An important-data-based (IDB) DoS attack strategy, inspired by event-triggered logic from the perspective of attackers, is skillfully constructed to increase the destruction of the attack. Unlike most existing DoS attack models that indiscriminately disrupt packet transmissions without considering packet content, the proposed IDB DOS attack strategy can identify the importance degree of the packets and focus on attacking the most important ones. The other goal of this article is to construct an$H_{\infty }$state estimator against the IDB DoS attack, which can achieve a particular system performance in the presence of attacks. By using the Lyapunov functional method, a sufficient condition is successfully obtained to ensure the asymptotical stability of the augmented system and the$H_{\infty }$interference suppression performance. Simulation examples indicate that: 1) the constructed IDB DoS attack strategy will result in a worse system degradation and 2) the designed$H_{\infty }$state estimator can effectively mitigate the impact of the potential IDB DoS attack.

This letter focuses on the design of a novel critical-area-based (CAB) stochastic denial-of-service (DoS) attack strategy aimed at diminishing the quality of remote state estimation in multi-area power systems. This attack strategy features two main characteristics: firstly, unlike most existing DoS attack models which typically overlook system information, the proposed CAB stochastic DoS attack strategy can selectively target critical areas of the power system, and the more severe impacts on system performance are expected. Secondly, the proposed attack strategy is stochastic, offering enhanced concealment compared to continuous or uniformly distributed DoS attack models. Furthermore, when the attacker faces energy constraints, an analytical relationship between the upper bound of the attack parameter and the expected total number of attacks is derived. Simulation results conducted on IEEE 39-bus and 118-bus systems validate that, compared to existing models, the proposed CAB stochastic DoS attack strategy induces more substantial disruptions in power system estimation quality, thus confirming its effectiveness from the attacker's perspective.

In this paper, the multi-fault diagnosis problem is investigated for series-connected lithium-ion battery packs based on an improved correlation coefficient method. Different from existing correlation-based fault diagnosis methods having difficulties in distinguishing between sensor faults and connection faults, a novel sensor topology is proposed to separate sensor faults from connection faults. Furthermore, to solve the problem of inconsistent correlation coefficients in different working states, an adaptive threshold mechanism is proposed. By combining the improved sensor topology and adaptive threshold mechanism, multi-fault detection and diagnosis can be realized. Subsequently, through different characteristics of the correlation coefficient and voltages, the types of sensor faults, such as the bias fault, scaling fault, drift fault, sticking state and noise, can be determined. Experiments prove the effectiveness and superiority of the proposed scheme. Note to Practitioners—This paper focuses on the multi-fault diagnosis of lithium-ion battery packs, aiming to enhance system robustness and longevity. The string-level redundancy topology structure greatly simplifies the fault diagnosis process without increasing the hardware cost and system complexity, which greatly reduces the calculation amount. With the adaptive threshold mechanism, the consistency of correlation coefficients in different working states is improved, so the accuracy of fault diagnosis and the robustness to different working states are increased. This paper uses four batteries in series to form a battery pack. In theory, the proposed method is equally effective when the number of cells in the pack rises. Thus, our multi-fault diagnosis method has great potential to be applied to electric vehicles and energy storage systems and adeptly addresses complex and dynamic operational environments. Our research has been validated in practical battery packs, yielding significant successes. By effectively identifying and addressing various faults, we have improved the overall performance and reliability of battery packs.

This paper explores a T-S fuzzy-model-based approach for load frequency control in network-based power systems under random false data injection attacks, taking into account the integration of electric vehicles and photovoltaic power generation systems. Amidst uncertainties in parameters and nonlinear items of integrated power systems, a T-S fuzzy model is formulated, enabling convenient design and analysis of the rolling horizon optimal control (RHOC) strategy with variable control gains to ensure mean-square asymptotic stability in power systems. Considering the broad dispersion of new energy generation systems across the power grid, the use of network communication has become crucial. To overcome the limitation of network bandwidth while ensuring frequency stability, a novel event-triggered mechanism employing a varying probabilistic release strategy (VPRS) within grouped triggered data-packets is proposed for transmitting power frequency signals. Initially, a conventional event-triggered mechanism is established to generate primary triggering packets, stored in a buffer until designated packet capacity of the group is reached. Then, following the probabilistic updating algorithm in RHOC strategy, the transmission task is executed upon the arrival of the final triggered packet within each group. The efficacy of the proposed method is validated through a numerical example. Note to Practitioners—This paper introduces a T-S fuzzy-model-based RHOC strategy for power systems with renewable energy, addressing challenges posed by limited communication resources and random false data injection attacks. The innovative event-triggered mechanism with a VPRS optimizes network utilization for power systems, while the RHOC strategy with variable control gains guarantees mean-square asymptotic stability despite uncertainties and nonlinearities within the power network. While the efficacy of the approach is demonstrated through numerical examples, practitioners should take into account practical constraints, such as scalability issues and the complexities involved in real-world implementation. Future research will focus on enhancing scalability and addressing implementation challenges to facilitate broader adoption in power systems.

This paper investigates the secure state estimation problem for time-varying singularly perturbed discrete sequential systems (SPDSSs) under bandwidth-constrained and multichannel-enabled wireless communication environment. Firstly, to avoid the potential data collision as well as fully utilize the available wireless channels, a round-robin-like multichannel access policy (RRL-MAP) is presented for scheduling the transmission of measurement signals. The considered SPDSS is further assumed to be compromised by deception attack, and then the measurement outputs are updated based on the RRL-MAP and the attack driven by a Bernoulli process. In light of the constructed measurement model, distributed recursive state estimators are designed with the proposing of an algorithm that determines the estimators’ gains. Finally, the efficiency of the developed state estimation method for the envisioned SPDSS is verified by both theoretical analysis and simulation experiments.Note to Practitioners—State estimation for SPDSSs is critical given the widespread application of the systems, however it is challenged by limited communication resource and stochastic cyber attacks. Traditional RR protocol is viewed as a desirable method for mitigating the limitation of network bandwidth, but which becomes inefficient under practical multichannel communication scenario. In addition, deception attack is acknowledged as a kind of hardly detected cyber attacks, and will seriously threaten the data integrity. In view of this, this paper presents the RRL-MAP to guarantee efficient data transmission by appropriately scheduling the available communication channels, and uses Bernoulli process to properly describe deception attack. Then, by further considering the structural characteristics of SPDSSs, distributed recursive state estimators are designed to achieve desired system performance. The effectiveness of the estimators is also comprehensively evaluated, which validates the practical applicability of the proposed state estimation method.

This paper investigates the attack-defense framework for power systems, a novel voltage-sensitivity-based (VSB) attack schedule method is proposed to maximize the attack destructiveness of the remote estimator of the target bus in the power system, and a mitigation mechanism is also constructed to make up for the negative effect of the proposed attacks. Firstly, the relationship between voltage and input current at different buses within the power system is analyzed, and the voltage sensitivity of target buses to other buses is calculated. Based on this, a VSB attack strategy is proposed to maximize the attack disruption on the target bus by targeting those with higher voltage sensitivity. Secondly, considering the clear periodicity of voltage signals, an enhanced local outlier factor algorithm is proposed to mitigate the proposed attack strategy. This algorithm can identify abnormal situations by detecting outliers and compensate for the attack by establishing a voltage model. Finally, the effectiveness of both the attack design method and mitigation strategy is validated through extensive simulation experiments conducted on the IEEE 14-bus and 118-bus systems. Note to Practitioners—This paper is motivated by the escalating challenges of attack-defense problems in power systems. From the perspective of attackers, existing DoS attack strategies often lack discrimination. They operate without considering the specific characteristics of the target, proceeding with attacks based on predetermined sequences while disregarding the varying importance of information carried by different targets. In response, this paper proposes a novel VSB attack strategy that utilizes readily available information to evaluate the voltage sensitivity of each bus within the power system. By targeting buses with high sensitivity, this strategy aims to magnify its destructive impact. From the perspective of defenders, this paper proposes utilizing the periodicity of voltage signals to mitigate the aforementioned attack strategy. Through simulation, the effectiveness of the proposed attack-defense framework is demonstrated from both attacker and defender perspectives.

This article is centered on an optimal tracking control problem for nonlinear networked systems with limited network bandwidth and unavailable dynamics. Foremost, considering the absence of system dynamics, a state identifier grounded in generalized fuzzy hyperbolic model (GFHM) is estabilshed to eliminate the reliance on known system states. Meanwhile, in order to efficiently utilize the limited communication resources within the network, a dynamic event-triggered mechanism (DETM) with an adaptive threshold is adopted. Subsequently, the DETM-based Hamilton-Jacobi-Isaacs (HJI) equation is constructed following the Bellman optimality theory. Furthermore, we develop a particle swarm optimization (PSO)-driven reinforcement learning (RL) algorithm to achieve the desired optimal tracking control strategy. It is demonstrated that the tracking error system can be promised to be uniformly ultimately bounded (UUB) stability. Eventually, a numerical experiment is provided to illustrate the validity of the developed RL algorithm.Note to Practitioners—This study targets the optimal tracking control issue of unknown networked systems, which holds practical significance in various fields such as robotics, autonomous vehicles and industrial control. In real-world environments, the dynamics of networked systems are not always accessible, thus this paper employs GFHM to approximate unknown dynamic information. Additionally, due to the involvement of the network, the restriction of communication resources is an unavoidable concern. To address this, DETM is adopted to improve network transmission efficiency. Subsequently, to achieve the optimal tracking control, the HJI equation based on DETM is constructed. The solution to the HJI equation is obtained through the RL algorithm supported by critic neural network, with its weights trained using the PSO algorithm. Compared to the commonly applied gradient descent method, under the PSO algorithm, the initial weights can be randomly assigned, making implementation easier and increasing the success rate of system operation. This is of significant importance in practical applications. Besides, a simulation example illustrates the feasibility of the developed method.

From the perspective of attackers, this article proposes a creative essential-information-aware (EIA) attack policy targeting remote state estimation in cyber-physical systems (CPSs), in which adversaries aim at maximizing the trace of estimation error covariance by selectively leveraging crucial information during data transmission. Through manipulating the transmitted signals, the attacker can recognize critical packets exchanged among sensing nodes and allocate higher attack energy level to them, thereby enlarging the attack success probability (ASP) and amplifying the disruptive effect. Then, a scientific power allocation scenario has been designed with the aid of signal-to-interference-plus-noise ratio (SINR), while explicitly accounting for the confinement imposed by limited channel transmission capacity. Furthermore, the relationships among the attack coefficient, ASP, attack cost, and the channel capacity are rigorously analyzed utilizing stochastic methods. The upper bounds for attack coefficient are derived to balance attack impact with resource limitations. Finally, two examples are provided to verify the effectiveness and practicality of the attack strategy.

This paper presents an investigation of the sliding-mode secure control for nonlinear semi-Markov jump systems in the presence of non-periodic denial-of-service attacks and false data injection attacks. First of all, we employ Takagi-Sugeno fuzzy model to describe the nonlinear semi-Markov jump control systems. In order to optimize transmission efficiency and control performance, a novel dynamic-memory event-triggered mechanism is developed by incorporating auxiliary dynamic variable and historical transmitted data. Then, a memory-based fuzzy sliding surface is put forward to attenuate the influences of stochastic cyber attacks with the aid of event-triggered state information. Moreover, by utilizing Lyapunov stability theory, sufficient conditions are derived to guarantee the exponentially mean-square stability of the system with an$H_{\infty}$performance index, even in the cases of generally uncertain and unknown transition rates. Furthermore, a memory-based sliding mode secure controller is designed to ensure the reachability of the predefined switching surface and desirable sliding motion within finite time. Finally, the efficacy of the proposed control scheme is demonstrated through a tunnel diode circuit model.Note to Practitioners—This study focuses on the issue of secure control for nonlinear semi-Markov jump systems, which holds practical significance across various domains, including applications in robotic manipulators, circuit models, and DC motors. We broaden the scope by considering more general jump parameter matrices to align more closely with the real-world system environment. Moreover, networked environments pose two primary challenges: network bandwidth constraints and the external network attacks. To tackle these issues, this paper introduces an innovative dynamic memory event triggering mechanism to enhance network transmission efficiency and optimize communication resource utilization. Meanwhile, to address cyber attacks resulting from the inherent openness of networks, the current study adopts a defensive sliding mode control strategy to provide robust protection against network attacks and disturbances. The effectiveness of the suggested approach is confirmed through a circuit system model. It is worth mentioning that the proposed method has the potential for broader application within real-world engineering scenarios, particularly those involving network-based nonlinear systems with various practical constraints.

DC microgrids (DC-MGs) consist of multiple units interconnected in a network. This makes the system vulnerable to malicious cyber attacks. Malicious attacks here refer to hybrid attacks of false data injection (FDI) attacks and denial of service (DoS) attacks. The main focus of this paper is to study the state-FDI estimation and resilient control problems of nonlinear DC-MGs under malicious attacks. Since the fuzzy controller/observer based on the parallel distributed compensation method cannot obtain more relaxed satisfactory results. Methods presented in the literature will be reviewed and a method based on multi-mode fuzzy switching mechanism (MMFSM) will be developed to relax the stability analysis of these DC-MGs. Furthermore, a co-design method based on improved fuzzy switching technology is proposed. This method adopts a novel MMFSM, which makes the constraints more relaxed, the state estimation more accurate, and the conservatism of the co-design method is reduced. Finally, the effectiveness of the proposed improved co-design method is verified by comparison and analysis of simulation examples.Note to Practitioners—The motivation of this paper comes from the problem that DC-MG systems, which can realize large-scale application of new energy technology, is vulnerable to malicious cyber attacks in practical tasks. Malicious attacks (such as FDI attacks and DoS attacks) will bring security risks such as data tampering and function destruction to the system. To this end, this paper develops a MMFSM-based co-design method for DC-MGs under malicious attacks. The improved co-design method can deal with sector nonlinearity, state imperfectly measurable, FDI attacks and DoS attacks simultaneously. For this method, the main difficulty in this paper is how to reduce its conservatism. To solve this problem, we adopt a novel MMFSM. By introducing time-varying equilibrium matrix synchronously with the system dynamics of different modes, the improved co-design method can switch accordingly, thereby making the constraints more relaxed and the state estimation more accurate.

This paper focuses on the problem of outlier-resistant distributed filtering under dynamic event-triggered scheme (DETS) and cyber-attacks over sensor networks. A novel distributed DETS is proposed, which allows us to minimize the data-releasing rate while simultaneously preventing abrupt data from being sent over the network. Consider the situation of measurement outliers in presence, a saturation function is used in distributed filter design to confine the contaminated signals so as to improve filtering performance. The occurrence of stochastic DoS attacks which may corrupt the measurement in the sensor networks are also considered. Sufficient conditions for the expected filter are derived while guaranteeing the asymptotically stability of the filtering error system with a guaranteed$H_{\infty}$performance index$\gamma$. A numerical simulation is given to illustrate the effectiveness of the theoretical analysis and design method.Note to Practitioners—The issue of filtering over sensor networks is subject to network impections induced by the limited communication resources and cyber-attacks. Since these imperfections may lead to performance degration or instability of the system, it is critical to take some measures to reduce unnecessary signal communication among sensor nodes and eliminate the adverse effects of cyber-attacks. Besides, the measurement outliers are frequently encountered due to sensor aging or abnormal disturbances, which may impair the accuracy of the estimation resuts. In this paper, the outlier-resistant distributed filtering problem is investigated in sensor networks subject to dynamic event-triggered schemes and DoS attacks. A co-design method of the DETS and the distributed filter is propsed which can gurantee the desired system performance.

The dynamic event-triggered (DET) bipartite consensus and attack estimation problem is investigated for multi-agent systems (MASs) under false data injection (FDI) attacks generated by exogenous systems. For the purpose of compensating for the system bias, a novel set of estimators are introduced to estimate the FDI attacks. To optimize the limited resources of the network bandwidth, a DET protocol dependant on the auxiliary variable and the local estimations is employed to regulate data transmission. By employing the designed state observer and attack observer, an novel attack tolerant event-triggered control strategy is established. Subsequently, sufficient criteria are obtained to ensure the bipartite consensus performance with$l_{2}$-$l_{\infty }$constraint. Then, by solving algebraic matrix equations and recursive linear matrix inequalities (LMI), the gains of state estimator, attack estimator and desired controller are determined, respectively. Finally, simulation examples are provided to demonstrate the validity of the derived results. Note to Practitioners—This paper discusses the secure bipartite consensus control problem for MASs subject to FDI attacks. Since the system state deviation caused by FDI attacks will bring about degradation of system performance, it is important to protect the MASs from being destroyed by designing observers to estimate the FDI attack and the system state. In view of the bandwidth constraints among the agents in MASs, optimization utilization of the network bandwidth should be taken into account seriously. Therefore, in this paper, the proposed observer-based DET controller is developed to guarantee bipartite consensus of the considered MASs while ensuring efficiency utilization of bandwidth resources. The current research provides a helpful reference for the secure bipartite consensus control of MASs with bandwidth constraints.

This paper deals with the privacy-preserving-based distributed secure balancing control problem for battery energy storage systems (BESSs) in a microgrid. A novel distributed consensus algorithm is proposed based on the state decomposition strategy and the homomorphic cryptography technique, under which the state-of-charge (SOC) balancing issue is addressed amid potential privacy leak threats. By decomposing the state of the modified SOC for BESSs based on the number of neighboring BESSs, the privacy of each BESS’s state is safeguarded, provided that the BESS has at least one neutral neighboring BESS. Each homologous substate exchanges information directly, and non-homologous substates exchange information using a homomorphic cryptography algorithm. A weighted approach is used to ensure that the sum of the homologous substates aligns with the group decision value, thereby maintaining consistency among these homologous substates. The effectiveness of the proposed method is validated through a series of illustrative simulation experiments.

This paper delves into the constant voltage output regulation problem for delayed inductive power transfer (IPT) systems with linear matrix inequality region constraints. Different from existing works, the proposed approach not only provides a model-free controller design procedure, but also deals with time-varying delays and packet losses. The main contributions are twofold. First, we propose a novel data-based parameterization of delayed systems. Second, data-based sufficient conditions are derived under which the delayed stability and LMI region constraints are guaranteed and then controller is parameterized. Finally, an experimental example is proposed to demonstrate the accuracy of proposed methods and the robustness of the suggested controllers.

This paper concerns the problem of constant voltage regulation for an unknown inductive power transfer (IPT) system with phase shift modulation (PSM). Different from mainstream model-based approaches, the proposed data-driven fuzzy method not only formulates a controller solely based on input-output data, but also guarantees the stability of nonlinear inductive power transfer systems. First, a data-based closed-loop fuzzy representation is derived to parameterize the nonlinear system. Subsequently, a sufficient data-based condition is proposed to ensure the stability of the nonlinear system and the asymptotically tracking characteristic without identifying the system parameters. Finally, an experimental example is proposed to demonstrate the accuracy of the proposed method and the robustness of suggested controllers.

This article proposes a novel scalable control strategy for large-scale direct current microgrids (LSDCmGs) consisting of multiple distributed generation units (DGUs). The objective is to facilitate real-time plug-and-play (PnP) functionality for DGUs, despite the presence of power transmission line coupling. In brief, the insertion or removal of DGUs does not require reconfiguration of adjacent controllers to maintain the stability of the entire LSDCmGs. To achieve this goal, a generalized free-weighting matrix technology is introduced. The strict assumptions previously made on Lyapunov matrices are ingeniously shifted to free-weighting matrices by this technology. As a result, scalable control is achieved and conservatism is reduced simultaneously. Moreover, the topological structure of LSDCmGs frequently undergoes changes due to external factors, equipment failures, and line issue. Markov chains are employed to accurately model these variations, providing a more realistic analysis of the microgrid's operational states. Additionally, by selecting a mode-related structured Lyapunov-Krasovskii function, sufficient criteria are derived to realize tracking of a given reference voltage in the LSDCmGs under PnP operation. Finally, the effectiveness of the proposed scalable strategy is validated through a case study involving the LSDCmGs with five DGUs.

This paper is concerned with the security issues related to integrated attack-defense strategy for a category of multi-sensor networked control systems with state saturation constraints. In general, existing denial-of-service (DoS) attack models typically conduct indiscriminate attacks on data packets, disregarding the significance of the attacked data packets to the system. Note that the measurement data from different sensor nodes possesses varying levels of importance. In light of this, we first propose a novel form of attack from the perspective of attack design, known as a data-importance-aware attack. The importance of data refers to the quantitative impact of the measured values at each sensor node on the stable and safe operation of the entire system. As such, the proposed attack has the awareness to launch attacks against critical sensor nodes, rendering data unable to be transmitted. Then, an attack-node-dependent security controller is devised from the defender’s perspective against the constructed attack, which can effectively resist the impact of attacks and stabilize the system. By employing the Lyapunov functional method, sufficient conditions are derived to ensure the asymptotic stability of the closed-loop system. Finally, the reliability and effectiveness of the node importance-aware attack strategy and control countermeasure are validated by numerical simulation.

In this article, a reinforcement learning (RL)-based event-triggered guaranteed cost control (GCC) is developed for power systems with control input saturation (CIS). An event-triggered mechanism with a balance factor is developed to optimize both control performance of power systems and computation efficiency. To obtain an online solution to the corresponding modified Hamilton–Jacobi–Bellman equation for the power system, a critic neural network is employed. In contrast to RL-based methods employing dual-network setups, the single neural network not only conserves computational resources but also eliminates the need for an initial admissible control. For accelerating convergence toward optimal solutions, a new adaptive weight tuning law is constructed. Control performance of power systems with CIS and limited computation power is ensured by the proposed RL-based optimal GCC strategy. Simulation results validate the effectiveness of the proposed methodology.

This article investigates the output feedback stabilization problem for a parabolic partial differential equation (PDE) system with uncertain parameters, which is subject to limited communication resources. First, to alleviate the burden of channel communication and avert data collision, the multichannel measurement outputs are scheduled by the weighted try-once-discard protocol. Moreover, to bridge the gap between theory and reality, the Markov process is utilized to depict the occurrence of various actuator faults in a stochastic manner. An impulsive closed-loop model is established in light of the network-induced delay and various stochastically occurring actuator faults. Following the model, sufficient criteria for guaranteeing the input-to-state stability of the PDE system are put forward by constructing a new Lyapunov functional. Then, one can deduce the determination of the controller gains and weighted matrices by employing the technique of linearizing the matrix inequalities. Finally, simulation results unequivocally demonstrate the effectiveness of the proposed method.

This article investigates a semantic-aware multipacket parallel transmission (MPPT) scheme for networked control systems (NCSs) using an autoencoder-based encoding–decoding framework. First, semantic encoding and decoding schemes are proposed based on autoencoder technology, aiming to effectively enhance the information density of data packets and reduce bandwidth usage during MPPT in NCSs. Through abstract semantic representations, raw data are mapped into a lower dimensional space for semantic encoding. Subsequently, these semantic representations are transmitted and utilized for semantic decoding, achieving the reconstruction of raw data. Considering multiple factors, such as semantic decoding errors, delays, and disturbances, stability analysis and$H_\infty$controller design methods of NCSs under the semantic-aware MPPT scheme are explored based on Lyapunov stability theory. Using gradient descent optimization, semantic encoding–decoding parameters are pretrained offline to enhance the collaborative efficacy of semantic encoder and decoder, concurrently meeting real-time requirements of NCSs. Guided by operational feedback, subsequent parameter refinement is achieved through an incremental search algorithm, aiming to improve the performance of data reconstruction. Finally, the effectiveness of the proposed method is demonstrated through the example of a six-area power system.

From the perspective of industrial production reliability, a robust event-triggered (ET) control strategy is presented for uncertain continuous stirred tank reactor (CSTR) system with asymmetric input constraints. To begin with, we propose a nonquadratic performance function to transform the robust control issue by constructing the relevant auxiliary dynamics. For effectively mitigating the pressure of data transmission and controller execution, a dynamic ET scheme (DETS) with an adjustable threshold function is adopted. Subsequently, we formulate the DETS-based Hamilton–Jacobi–Bellman (DET-HJB) equation according to optimality theory. In addition, a DETS-assisted reinforcement learning algorithm with a unique critic neural network can efficiently tackle the derived DET-HJB equation. Meanwhile, the corresponding critic weight is regulated on the basis of gradient descent technique and experience replay approach. By presenting a rigorous analysis under two situations, the uniform ultimate boundedness of auxiliary dynamics and weight approximation error can be ensured. Eventually, the feasibility of the proposed algorithm is demonstrated by experimental results of CSTR system.

The article designs a secure co-estimation approach for nonlinear systems with false data injection (FDI) attack and network bandwidth limitations, which is described by interval type-2 (IT2) fuzzy system. For mitigating communication load and avoiding network data collision, an event-triggered weighted try-once-discard protocol is implemented to adjust the data transmission frequency. Given the unknown system state and FDI attacks, both of the state observer and the attack observer are put forward. Besides, a new attack compensation controller is proposed under co-design of the observer of the state and FDI attack. The asymptotical stability of the augmented system can be realized by the proposed sufficient conditions for the existence of the fuzzy observer and controller gains, while the prescribedH∞performance requirement can be met. Then the secure controller design method is given accordingly. Finally, the validity of the attained results are provided by simulation examples.

This article explores the distributed set-membership state estimation problem over sensor networks (SNs) with chaotic encrypted privacy-preserving scheme and multistrategy injection attacks (MIAs). Since potential eavesdroppers in communication networks may intercept the transmitted measurement signals, chaotic encryption is adopted as a privacy-preserving scheme to protect the system state information from being revealed. The measurement signals are encrypted before transmission and decrypted upon reception by the remote estimator. A newly devised attack model is developed to characterize the injection attacks, which occur randomly and involve a combination of multiple attack strategies. By employing matrix inequality techniques, a unified set-membership estimation scheme is developed when both the privacy-preserving scheme and the MIAs coexist. Subsequently, based on the sufficient condition of constraining the estimation error within an ellipsoidal range, an optimization problem is formulated to achieve the optimal estimation performance at each time step, along with the development of a recursive algorithm for computing the required estimator parameters. Finally, simulation is provided to verify the set-membership estimation approach under the chaotic encryption scheme.

This article proposes a novel data-driven approach for studying the stabilization of continuous-time delayed systems (CDSs) with both unknown model parameters and denial-of-service (DoS) attacks. Due to the effects of DoS attacks, such controlled CDSs can be viewed as a switched system with two modes: a stable mode during dormant intervals and an unstable one during attack intervals. Because of the switching character of the CDS with DoS attacks, some well-known data-driven control methods can not be used directly. To tackle this issue, a new relationship between the dormancy and activation periods of DoS attacks is built, which offers a more reasonable description of DoS attacks. Additionally, in the presence of measurement noise, a novel lemma (Lemma 3) is introduced to establish a data-based representation of CDSs using noisy measurement data. Without the need of explicit system parameter identification, stabilization criteria and control gain matrix are derived solely based on the input-state data. Finally, the effectiveness and good performance of the proposed method are illustrated through a numerical example and a power system.

In this paper, an attack-defense framework is proposed for the remote H ∞ state estimation of delayed recurrent neural networks (RNNs). Firstly, an important-data-based (IDB) attack strategy is constructed, which can identify the important packets that play essential roles in the estimation and selectively attack them based on their importance degree from the perspective of the attackers. By targeting the important packets, larger attack damages can be achieved. Then, a resilient state estimator that can resist IDB attacks is developed from the defenders' point of view. Notably, some unrealistic assumptions (e.g., the attacker knowing the system structure and full parameters, the defender knowing the attack rate/parameters) are removed, which makes the proposed method easy to implement. At last, simulation results are presented to show the larger destructive effect of the constructed IDB attack and the efficiency of the proposed resilient H ∞ state estimator .

This paper investigates the optimal decentralized control issue for a class of networked interconnected systems (NISs) under communication and control constraints. First of all, the decentralized optimal control problem for NISs is transformed into the optimal control issue for nominal systems. And then the event-triggered Hamilton-Jacobi-Isaac (HJI) equations are derived with optimal control theory based on the constructed cost function and adopted adaptive event-triggered mechanism (AETM). Furthermore, a Reinforcement Learning (RL) based event-triggered optimal control algorithm with actor-critic networks is proposed to obtain numerical solutions of the HJI equations and the Uniformly Ultimately Bounded (UUB) stability of NISs is proved to be guaranteed with Lyapunov stability theorem. Eventually, a simulation experiment is conducted to verify the effectiveness of proposed RL-based optimal decentralized algorithm with AETM.Note to Practitioners—Actually, the limitation of communication resource is one of the key factors leading to performance degradation of NISs. It is crucial to cope with this issue in the control of NISs. Employing proper communication protocol is an effective way to diminish the adverse effects of undesirable phenomena, such as data loss and out-of-order. In addition, given the scale is gradually expanded and the structure is gradually complex for NISs, the operation cost is a problem that cannot be ignored in the control process of NISs. Thus, it is significant and challenging to minimize the operation cost on the basis of ensuring system stability. In view of the communication constraints and optimization problem, the HJI equations based on AETM are derived to model the decentralized optimal control problem for NISs. Moreover, a RL-based iterative algorithm is proposed to derive the numerical solutions to HJI equations and further effectively stabilize the targeted control constrained NISs in the sense of UUB.

The inductive power transfer (IPT) technology, as one of the main wireless power transfer methods, has been prevalent in both industrial and commercial applications. However, the circuit parameter perturbations and communication delays will deteriorate the transfer performance, including output fluctuation and efficiency drop. To deal with the above mentioned issues and maximize the transfer efficiency, a boost converter is first cascaded after series-series IPT topology, then a novel control strategy is proposed to fulfill the constant power output with maximum efficiency tracking (MET). In the proposed strategy, the MET condition can be derived regardless of coupling coefficient and load resistance through elaborate design. Next, the stability of nonlinear IPT systems subjected to communication delays is modeled and described based on the concept of Takagi-Sugeno (T-S) fuzzy models. Furthermore, an event-triggered$\bm{H_{\infty}}$fuzzy controller is designed to improve the robustness (against perturbations of coupling coefficient, loads and communication delay), and reduce the computation and transmission burden. Finally, the effectiveness of the suggested strategy is shown using the simulation results.Note to Practitioners—Maintaining a constant output power and high transmission efficiency remains a challenging task in IPT technology due to parameter variations, communication delays and model nonlinearity, especially in the charging scenarios of drones and electric vehicles. Therefore, an event-triggered$\bm{H_\infty}$fuzzy controller is proposed to cope with aforementioned situations such that the constant output power with high efficiency is guaranteed. Even in the presence of communication delays, the proposed strategy can still maintain stable output with high efficiency. In addition, the proposed method is equipped with the event-triggered mechanism to reduce the frequency of controller updates and transmission burden while maintaining the transient performance. The outcome of this paper provides a simple but useful MET condition and controller design procedure that can be employed to maintain the reliable and stable operation of IPT systems and optimize their transfer efficiency.

This paper investigates the${H}_{\infty}$tracking control problem for the series-parallel (SP)-inductive power transfer (IPT) system with coil misalignment. To describe the nonlinear characteristic caused by large-scale coil misalignment, a Takagi-Sugeno (T-S) fuzzy modeling approach is successfully applied to the SP-IPT system. By introducing an integrator related to the error between the actual and the reference system output signals, an augmented T-S fuzzy-based SP-IPT system model is established. Then an${H}_{\infty}$parallel distributed compensation (PDC) fuzzy control strategy with a valid event-triggering scheme (ETS) is put forward to guarantee desired tracking control performance and decrease unnecessary packet transmission at the same time. To ensure the asymptotic stability of the augmented T-S fuzzy-based SP-IPT system and the disturbance suppression index, sufficient conditions are employed by utilizing an extended Lyapunov functional method. Finally, a simulation example with regard to the SP-IPT system is developed, which demonstrates that the event-based${H}_{\infty}$PDC fuzzy control strategy proposed in this paper can maintain the constant output voltage of the SP-IPT system while saving communication resources.Note to Practitioners—For IPTs, the coil misalignment phenomenon unavoidably affects their desired performance, such as constant voltage and current output, constant power output, maximum efficiency tracking, and so on. Therefore, the corresponding tracking controllers have brought wide attention in the power transmission research. The proposed control strategies have been utilized to suppress the negative influence caused by coil misalignment, whereas it is difficult for these strategies to ensure the robustness and the stabilization under large-scale coil misalignment. To deal with this problem, this paper is devoted to design a T-S fuzzy-based${H}_{\infty}$control strategy with an ETS to guarantee the stabilization of practical inductive power transfer systems. It is worth mentioning that the proposed robust control strategy is proposed based on the IPT system with T-S fuzzy rules and the ETS, which can simultaneously guarantee desired tracking control performance and decrease unnecessary packet transmission in real applications. In this paper, only constant voltage tracking control are considered for the IPT system with coil misalignment, but some other tracking performance still need to be researched in practice. Therefore, we will take the tracking performance indexes of constant current and constant power into account in the future research.

This paper investigates the${L}_{2}$$-$${L}_{\infty}$robust control problem for the series-series-inductive power transfer system with parameter uncertainties, external disturbances, and time-varying delays. To describe the randomness of large-scale parameter variation more precisely, a switched model is skillfully built by dividing the varying ranges of circuit parameters into several subintervals. Considering that the sojourn probabilities related to all subsystems of the switched model are much more easier to be obtained, a closed-loop switched control framework with known sojourn probabilities is designed for practical inductive power transfer systems. With the help of the robust mean-square stabilization and${L}_{2}$$-$${L}_{\infty}$disturbance suppression performance indexes, the proposed${L}_{2}$$-$${L}_{\infty}$controller with sojourn probabilities can solve the problems of large-scale parameter uncertainties, time-varying delays, and external disturbances much better. Finally, a simulation example of the series-series-inductive power transfer system is utilized to verify that the effectiveness of the designed control method in this paper.Note to Practitioners—For inductive power transfer systems, parameter uncertainties unavoidably affect their desired performance and thus the corresponding robust controller design has brought wide attention in the power transmission research. The proposed robust control strategies have been utilized to suppress the negative influence caused by parameter uncertainties, whereas it is difficult for these strategies to ensure the robustness and the stabilization under large-scale parameter uncertainties. To deal with this problem, this paper is devoted to design a robust${L}_{2}$$-$${L}_{\infty}$control strategy with sojourn probabilities to guarantee the stabilization of practical inductive power transfer systems. It is worth mentioning that the proposed robust control strategy is proposed based on a sojourn-probability-based switched inductive power transfer systems with time-varying delays and external disturbances, which is relatively practical in real applications. In this paper, load variation and coil misalignment are considered as parameter uncertainties in the system, but some hostile environment still result in capacitance drift in practice. Therefore, we will take the capacitance drift phenomenon into account in the future research.

This paper investigates the${H_{\infty }}$dynamic composite nonlinear output feedback control of the inductive coupled power transfer (ICPT) system with load resistance variation, coil structural perturbation, and energy-bounded external disturbance under event-triggering mechanism. A small-signal model is first developed to characterize the dynamic behavior of the ICPT system under S/S resonance, wherein the system mismatch is considered as a type of stochastic process and described as a Markov jump model. In view of the difficulties in measuring system state, poor transient performance, limited controller computation and communication capabilities, an event-triggered dynamic composite nonlinear output feedback controller is designed, and sufficient conditions are presented to guarantee the mean square stability and${H_{\infty }}$performance of the ICPT system. The controller gains can be obtained by solving a set of linear matrix inequalities. Finally, the effectiveness and reliability of the proposed control method is verified through simulation examples.

Fault-tolerant control (FTC) is vital for the safety and reliability of automatic systems. Most of the existing FTC methods are developed for open-loop systems subject to additive faults, regardless of the widely present control loops and multiplicative faults within systems. In this article, a performance-based FTC strategy is proposed for the closed-loop systems with multiplicative faults. Considering the high efforts in modeling complex systems, the proposed FTC strategy is realized in the data-driven context. Specifically, a nominal feedback-feedforward controller is first established for the fault-free systems. By selecting the system stability and reference tracking behavior as the key performance indices, two performance evaluators are constructed to detect and classify the occurred multiplicative faults based on the fault-induced effects on the system performance. Then, with the aid of the coprime factorization technique, the multiplicative faults, in the form of additive perturbations to the system coprime factors, are estimated utilizing the closed-loop process data. Furthermore, based on the fault knowledge, a hierarchical fault-tolerant tracking controller is developed according to the levels of system performance degradations, where the functional controller parameters are reconfigured with different priorities. Finally, case studies are provided to validate the effectiveness of the proposed method.

This article is concerned with the security problems for networked Takagi–Sugeno (T–S) fuzzy systems with asynchronous premise constraints. The primary objective of this article is twofold. First, a novel important-data-based (IDB) denial-of-service (DoS) attack mechanism is proposed from the perspective of the adversary for the first time to reinforce the destructive effect of the DoS attacks. Different from most existing DoS attack models, the proposed attack mechanism can utilize the information of packets, evaluate the importance degree of packets, and only attack the most “important” ones. As such, a larger system performance degradation can be expected. Second, corresponding to the proposed IDB DoS mechanism, a resilient$H_{\infty }$fuzzy filter is designed from the defender’s point of view to alleviate the negative effect of the attack. Furthermore, since the defender does not know the attack parameter, an algorithm is designed to estimate it. In a word, a unified attack-defense framework is developed in this article for networked T–S fuzzy systems with asynchronous premise constraints. With the help of the Lyapunov functional method, sufficient conditions are successfully established to compute the desired filtering gains and ensure the$H_{\infty }$performance of the filtering error system. Finally, two examples are exploited to demonstrate the destructiveness of the proposed IDB DoS attack and the usefulness of the developed resilient$H_{\infty }$filter.

In this article, the quasi-consensus control problem is investigated for a class of stochastic nonlinear time-varying multiagent systems (MASs). The innovation points of this research can be highlighted as follows: first of all, the dynamics of the plant are stochastic, nonlinear, and time varying, which resembles the natural systems in practice closely. Meanwhile, an energy harvesting protocol is put forward to collect adequate energy from the external environment. Second, as a generalization of the existing result, the ultimate control objective is quasi-consensus in a probabilistic sense, that is, designing a distributed control protocol in order that the probability of centering the allowable region for the states of each agent is larger than some predetermined values. Third, the MASs are subject to false data-injection (FDI) attacks, and a more general multimodal FDI model is proposed. On the basis of the probabilistic-constrained analysis technique and the recursive linear matrix inequalities (RLMIs), sufficient conditions are provided to guarantee the probabilistic quasi-consensus property. To derive the controller gains, an optimal probabilistic-constrained algorithm is designed by solving a convex optimization problem. Finally, two examples are provided to substantiate the validity of the proposed framework.

stability of a class of networked control systems (NCSs) under random denial of service (DoS) attacks and design a sampled-data-based state feedback security controller to mitigate the influence of attacks. Different from the existing random attacks, the information about the maximum duration time of DoS attacks can be captured by introducing a predesigned logical processor. Then, based on the periodic sampling technique, the probability of attack occurrence and the resultant number of maximum allowable consecutive packet dropouts can be calculated, which is quite significant to investigating the security problem of NCSs. A DoS-dependent security controller which makes full use of the attack probability information and the number of attack-induced packet dropouts is designed. A novel networked sampled-data system model is first established that enables us to deal with the random DoS attacks phenomena and the time-varying delay induced by attacks under a uniform framework. By structuring a suitable Lyapunov-Krasovskii functional, the relationship between mean square asymptotic stability and attack characteristics is obtained. Finally, the reliability and applicability of the presented control strategy in eliminating the influence of DoS attacks are validated by two practical engineering applications.

This article addresses the fuzzy filtering problem for nonlinear systems based on the interval type-2 fuzzy model. To make better use of the constrained resources for communication, a novel event-triggering weighted try-once-discard protocol (ET-WTODP) is proposed. Different from the existing diverse protocols, ET-WTODP only allows a portion of components with relatively greater deviation to transmit at each triggering moment. The fuzzy filter is constructed with imperfectly matched membership functions and mode-dependent parameters. Denial-of-service (DoS) attacks frequently occur in communication networks are also considered. The overall fuzzy filtering error system (OFES) model is established taking into account ET-WTODP and DoS attacks. The sufficient conditions for the OFES to be asymptotically stable with a preset$H_{\infty }$performance level are identified by using the Lyapunov stability theory to obtain the desired asynchronous filter. Finally, numerical simulations are presented for verification that the proposed filtering approach is feasible and valid.

This article is concerned with the secure event-triggered (ET) control issue for interval type-2 (IT-2) fuzzy networked systems under stochastic communication (SC) protocol and random false data injection attacks. On account of the realistic physical sensor constraint, a saturation function is utilized in the measured output. In an effort to mitigate the network burden and reduce the incidence of data collision, a novel dynamic ET SC protocol is proposed by integrating with the major merits of the dynamic ET strategy and SC protocol. Moreover, the fuzzy observer-based controller in the presence of different membership functions is constructed. Subsequently, some sufficient design conditions are put forward for satisfying the asymptotically stable performance of the augmented closed-loop IT-2 fuzzy model. In addition, the gain parameters of fuzzy observer and controller can be obtained by virtue of solvable sufficient criteria without nonlinear terms. Eventually, the effectiveness of the theoretical secure fuzzy control scheme is verified by a numerical example.

In this article, a decentralized event-triggered (ET) regulation problem is tackled for networked interconnected systems (NISs) with control constraints and unmatched interference. Foremost, the decentralized regulation issue is converted into the optimal control problems for the associated auxiliary subsystem. In confronting the unavailability of system dynamics, the utilization of generalized fuzzy hyperbolic models-assisted identifier provides a novel perspective to devise the efficacious control policy for the constrained NISs. For the sake of mitigating the communication workload, a new dual threshold functions-based adaptive ET scheme (DTAETS) is put forward by incorporating the current data and latest ET signal. Moreover, we present a data-driven decentralized reinforcement learning algorithm to acquire the solution of DTAETS-boosted Hamilton–Jacobi–Isaacs equation. Then, the uniformly ultimately bounded stability of auxiliary subsystem and the weight estimation error is assured. Ultimately, a numeral experiment is conducted to substantiate the validity of the theoretical results.

This paper is concerned with the reinforcement learning-based tracking control problem for a class of networked systems subject to denial-of-service (DoS) attacks. Taking the effects of DoS attacks into consideration, a novel value function is proposed, which considers the cost of the control input, external disturbance and tracking error. Then, using the structure of the value function, the tracking Bellman equation and Hamilton function are defined. By employing the Bellman optimality theory, the optimal control strategy and the game algebraic Riccati equation (GARE) are solved with the Hamilton function. Next, the desired tracking performance is guaranteed as the solution of the GARE is found. Furthermore, an attacks-based Q-learning algorithm is projected to find the solution to the optimal tracking problem without the system dynamics and the convergence of the Q-learning algorithm is given. Finally, the F-404 aircraft engine system is given to verify the effectiveness of the proposed control strategy.

In this article, the outlier-resistant (OR) observer-based secure control problem is investigated for nonlinear networked systems under randomly activated denial-of-service (RADoS) attacks and stochastic communication (SC) protocol. In virtue of zero-order holder (ZOH) technique, an attack compensation scheme is adopted to alleviate the adverse effects brought by RADoS attacks. By introducing multiple internal dynamic variables (MIDVs), a novel multi-variable event-triggered stochastic communication (MVET-SC) protocol is put forward to enhance the design flexibility and prevent the occurrence of data conflicts. Moreover, the non-fragile fuzzy controller is applied due to the inaccuracies in practical applications. Comprehensively taking the above factors into account, the asymptotic stability of the augmented fuzzy system can be ensured under the presented sufficient criteria. Meanwhile, the parameters of the OR observer-based secure controller are derived according to the obtained design conditions. In the end, a simulation example of mass-spring-damping system (MSDS) demonstrates the validity of the non-fragile secure control approach with OR observer.

Probability forecasting is a powerful tool for quantifying uncertainty in short-term load forecasting. However, its performance may be hampered by excessive feature redundancy and the quantile crossing phenomenon. To overcome these challenges, this study proposes a novel deep noncrossing quantile method with multi-information fusion for day-ahead load probabilistic density forecasting. This method extracts different types of input features through distinct neural networks, and can reduce the redundancy of feature information. Based on the positive differences among output values from neural networks, a novel quantile noncrossing strategy is introduced. This strategy, integrated within the neural network, eliminates quantile crossing phenomena and enhances the interpretability of model during the training process. Experimental results show that the proposed model reduces the quantile loss by 11% to 31%, produces prediction intervals with higher quality, precision, and no crossovers.

In this article, the cooperative output regulation problem (CORP) for unknown linear multiagent systems (MASs) is studied on the basis of a data-driven approach. Different from existing literature, first, the proposed method does not require prior knowledge of the system matrices and the leader system where the control gains are designed using input and state data. Next, the solution to the unknown regulator equations is derived using the data informativity approach. Then, to speed up the convergence rate, a distributed deadbeat control law is proposed for the MASs. For the purpose, the leader's system matrix is identified according to the measured data, and then a distributed observer is employed to estimate the state of the leader system in a distributed manner. Furthermore, the CORP is converted to a stabilization problem by using the data-based solution of the regulator equations. On the basis of the distributed observer, a data-driven deadbeat control law is designed in the context where the system matrices of the plant are completely unknown. It is guaranteed that the tracking error for each agent can converge to the origin asymptotically. Finally, an example is conducted to illustrate the effectiveness of the proposed data-based framework.

The primary purpose of this article is to design an intelligent false data injection (FDI) attacks detection, isolation, and mitigation scheme for a class of complex microgrid systems with electric vehicles (EVs). First, a networked microgrid with an EV model is well established, which takes load disturbance, wind generation fluctuation, and FDI attacks into account so as to truly reflect the operation process of the complex system. Then, an intelligent hyper basis function neural network (HBF-NN) observer is designed to accurately estimate the state of the microgrids, learn, and reconstruct the possible attack signal online. Subsequently, a novel HBF-NN-based${\mathcal {H}}_{\infty}$controller is skillfully designed to mitigate the negative impact of FDI attacks online, so as to ensure the normal operation of the complex systems in an unreliable network environment. Finally, a two-stage integrated intelligent detection and maintenance algorithm is summarized and one simulation is presented to provide tangible evidence of the feasibility and superiority of the proposed FDI attacks detection, isolation, and mitigation methodology.

Synchronization control is an important issue in complex networks (CNs), but the specific realization is generally influenced by many factors. In this article, by taking into accounts of the coupling effect and cyber attacks, we dedicate to design an efficient synchronization control approach for discrete-time CNs. Given that the coupling among nodes in CNs inevitably introduces the internode data exchange, round robin (RR) protocol is adopted to prevent data collision caused by the limitation of communication resources. Moreover, a probabilistic interval model is employed to capture the characteristics of the coupling delay, i.e., the delay of the internode data transmission. The deception attacks launched on the multichannel-enabled communication network between controllers and actuators of nodes in CNs are also considered. Then, a synchronization error model is established to describe the concerned synchronization control problem. By defining appropriate Lyapunov–Krsasovskii function, the sufficient conditions for the stability of the envisioned synchronization error system are obtained, and the design method for controllers is proposed subsequently. Simulations are finally conducted to demonstrate the validity of the work.

This article addresses a security control issue for discrete-time networked control systems (DTNCSs) with uncertainty under stochastic communication protocol (SCP) characterizing a class of networked systems, such as autonomous dynamics. Specially, in order to properly take account of the constructed system in practical application, the time-varying uncertainty with norm-bounded parameter is adopted in DTNCSs. To avoid data conflict in the communication network with limited bandwidth, SCP modeled by discrete-time Markov chain is introduced to schedule the transmitted signals, which are subjected to deception attacks described by a random process. In addition, the mode-dependent observer and controller are utilized in line with the scheduling situations led by SCP. Moreover, the stochastic stability of the augmented dynamics is ensured by the presented sufficient condition. Meanwhile, the corresponding gain parameters of the observer and controller can be computed. Ultimately, two simulation experiments are utilized to validate the availability of the proposed security control strategy.

This article investigates the FlexRay (FR) protocol-based dynamic event-triggered (DET) secure filtering problem with the high-rate communication (HRC) network for the interval type-2 (IT-2) fuzzy networked system. For fear of data collisions and preempting limited communication resources, a novel DET-FR (DETFR) protocol is adopted to schedule sensor nodes. Different sensors are employed for distinct protocols and data-holding strategies to reinforce the efficiency of network transmission. Moreover, considering the random occurrence of cyber attacks and fading measurements in the HRC channel, the attack parameters are represented by the Bernoulli process, and measurement attenuation coefficients are expressed by the random variables of equidistribution on a fixed interval. Based on this, to ensure that the error dynamics meet the disturbance attenuation level, several sufficient conditions are given. Subsequently, the DETFR protocol-based secure filtering algorithm is developed to acquire the fuzzy filter parameters. Eventually, the validity of our designed secure filtering algorithm is proved through two examples.

In this article, we address the quasi-bipartite consensus problem concerning a type of time-varying multiagent systems (MASs), which are subjected to energy harvesting protocols and random occurring faults. The creative aspects of this study can be emphasized as follows: first, the dynamics of the considered system are time-varying and stochastic, which is in accordance with the practical application closely. Meanwhile, a differential privacy protection mechanism is proposed, which realizes the goal of sensitive information protection via adding random noises following the Laplace probability distribution, and an energy collecting procedure is proposed to gather external energy for information exchange. Furthermore, the ultimate control objective is to implement an appropriate quasi-bipartite consensus controller to ensure that the probability of the state/output error residing within an allowable range exceed a fixed scalar. By means of recursive linear matrix inequalities (RLMIs) and the matrix analysis theory, sufficient conditions for guaranteeing quasi-bipartite consensus are derived, and an optimal constraint set is obtained by addressing a convex minimization problem. Finally, the availability of the employed approach are upheld by two illustrative examples.

This article explores the state estimation problem of memristive neural networks (MNNs) with mixed delays subject to multichannel round-robin protocol (MRRP) and multimodal injection attacks (MIAs). MRRP allows for multichannel-enabled signal transmission between sensors and remote estimators, thereby significantly mitigating network congestion. Different types of attacks are supposed to be encountered during the sensor data transmission via network. Both the impacts of the MRRP and the attacks are reflected in modeling the addressed system. The purpose of this article is to design an estimator capable of constraining the estimation error within an ellipsoidal region in the presence of noise interference, mixed delays, MRRP, and MIAs. Using recursive matrix inequality (RMI) techniques, sufficient conditions are derived for achieving the desired performance and the expected state estimator gains are derived by minimizing the constrained ellipsoid region. Ultimately, the efficacy of the developed estimate strategy is demonstrated through numerical simulation.

In this study, an innovative extended dissipative scalable control approach is proposed for monitoring islanded microgrids (ImGs), which has the advantage of flexibility for adding and removing distributed generation units (DGUs). Firstly, an arbitrary topology is developed in order to circumvent the connected constraints of subsystems in previous results. Secondly, the impact of subsystem indexing on total subsystem communications is investigated during the execution of scalable control algorithms, and an optimizable cost function is created to determine the cost related to subsystem indexing. Then, by using Lyapunov stability theory, several sufficient conditions under the subsystem-level are established to ensure the studied system are globally exponentially stable with the desired extended dissipative performance, which can guarantee that when adding DGUs in ImGs, only directly connected controller gains needed to be designed, and when removing DGUs in ImGs, no controller gains needed to be redesigned, that is, plug-and-play performance is achieved. Finally, the considered ImGs containing three DGUs and a numerical example are presented to demonstrate the effectiveness of the proposed control approach.

This paper is concerned with the privacy-preserving distributed optimization problem for a class of cooperative-competitive multi-agent systems. Each agent only knows its own local objective function and interacts the state information with neighbors through a communication network. By means of the signed graph theory, the antagonistic interactions among agents are considered to characterize both the cooperative and the competitive relationships. With the help of the gauge transformation technique, a structurally balanced undirected signed graph is firstly transformed into a standard undirected graph. Then, the distributed optimization problem subject to signed network is converted into the traditional distributed optimization problem. Subsequently, a novel privacy-preserving distributed optimization algorithm is put forward to 1) minimize the sum of local objective functions; 2) achieve the bipartite consensus for all agents; and 3) avoid the information leakage caused by message exchange among agents, simultaneously. Finally, a simulation example is given to verify the effectiveness of the proposed optimization algorithm.

This article focuses on designing a novel stochastic data-based denial-of-service (DoS) attack strategy that can intercept measurements and reduce the quality of remote state estimation in interval type-2 (IT2) T–S fuzzy systems. The new proposed DoS attack strategy has two main characteristics. First different from most of the existing DoS models, it is data-based, which mainly attacks the packets playing important roles in the system performance. Therefore, compared with the existing indiscriminate DoS attack approaches, the data-based DoS attack is more intelligent and can cause larger disruptions. Second, the proposed attack strategy is random, which has better concealment than the periodic or consecutive DoS attack models. Then, by using a stochastic method, the relationship among attack effect, attack probability, and attack parameter are analyzed and shown in Theorem1. Furthermore, if the attacks are energy-constrained, the relation between the attack rate threshold and the upper bound of the attack parameter is expressed analytically, which is shown in Theorem2. Simulation results demonstrate that, compared with some existing DoS models, the proposed stochastic data-based DoS attack strategy brings about greater destructiveness to the estimation quality of the systems, which demonstrates the effectiveness of the proposed attack strategy from the attacker’s perspective.

This article is concerned with the observer-based security fuzzy control problem for a class of nonlinear networked systems with imperfectly matched membership functions (MFs) under weighted try-once-discard (WTOD) protocol. The nonlinear networked system is constructed based on interval type-2 Takagi–Sugeno model, which can properly handle parameter uncertainties via lower and upper MFs. To mitigate the communication load, WTOD protocol is exploited to schedule the transmission order of the signals, in which those signals may be subjected to randomly occurring deception attacks. The observer is constructed under immeasurable premise variables, and then the controller with imperfect matching MFs is designed according to the estimated states. Moreover, the observer-based controller is derived with Lyapunov stability theory such that the stochastic stability of the augmented closed-loop system with predefined disturbance attenuation performance can be guaranteed. Subsequently, sufficient conditions are provided for the desired observer and controller gain matrices based on linear matrix inequalities technique. Numerical simulation example demonstrates the validity of the proposed observer-based security fuzzy control design scheme.

This article mainly studies the performance-driven fault detection for general uncertain Takagi–Sugeno fuzzy feedback control systems, where the infinite-horizon quadratic index is introduced to describe the concerned system performance. Different from the existing output-driven fault detection methods, the proposed approach aims to detect the performance degradations induced by faults. For this purpose, the performance index embedded with the information of system dynamics is reformulated for linear feedback control systems with reference inputs. The performance residual, which is adopted as the evaluation function, is derived based on the Bellman equation and further represented into an explainable form. Then, for uncertain linear feedback control systems, the boundaries of the performance residual are analyzed via the linear matrix inequality technique, and an advantaged threshold setting scheme is proposed with the aid of randomized algorithm. Concerning the main objective, a novel approximation method that combines the fuzzy blend of local performance indexes and the radial basis function neural network is developed to approximate the global performance index for Takagi–Sugeno fuzzy systems. Based on the result in the linear case, the performance residual is constructed along the closed-loop system trajectory, and the threshold is determined for the fuzzy control systems with reference inputs and model uncertainties. Finally, simulation studies are provided to show the effectiveness of the proposed theoretical results.

This article is focused on the adaptive event-based interval type-2 (IT-2) fuzzy security control problem for networked control systems with multiple network attacks. In order to save the network resources, this article proposes an improved adaptive event-triggered model, which can adjust the threshold dynamically according to the change of current signal and previous triggering signal. In addition, due to the change of external environment, the impact of randomly occurring output bias is considered in the systems. Taking the effects of multiple network attacks and adaptive event-triggered scheme into consideration, a class of augmented error fuzzy system model is established. Next, some sufficient conditions are obtained for the stochastic stability with an$H_{\infty }$performance index via the Lyapunov stability analysis method. The corresponding parameters of the IT-2 fuzzy controllers and observers are also derived by some linear matrix inequalities. Finally, two simulation examples are given to support the advantages of the proposed method.

This article develops a joint diagnosis and estimation algorithm for state of charge of lithium-ion batteries subject to sensor measurement outlier. By means of the chi-square test mechanism, an online-outlier-detection method is put forward to detect and further diagnose the type of outliers. Compared with the traditional data-driven-based fault detection approach that relies on a great amount of historical data for training in which each iteration only requires the information from the previous moment such that the computational complexity relieves fairly. Different from the existing filtering methods, which are vulnerable to the corrupted measurements from the current and voltage sensor caused by unexpected outliers, this research involves measurement outliers in the design of the estimator. Then, combined with the extended Kalman filtering algorithm and the Holt's two-parameter linear exponential smoothing method (Holt method), an outlier-resistant Kalman filtering algorithm is proposed to prevent the outlier-induced effect from degrading the estimation performance. Finally, extensive experiments are conducted to validate the serviceability and practicability of the proposed strategy.

In this article, the chance-constrained$H_{\infty }$state estimation problem is investigated for a class of time-varying neural networks subject to measurements degradation and randomly occurring deception attacks. A novel energy-constrained deception attack model is proposed, in which both the occurrence of the attack and the selection of released faked packet are random and the energy of the deception attack is introduced, calculated, and analyzed quantitatively. The main purpose of the addressed problem is to design an$H_{\infty }$estimator such that the prefixed probabilistic constraints of the system error dynamics are satisfied and the$H_{\infty }$performance is also ensured. Subsequently, the explicit expression of the estimator gains is derived by solving a minimization problem subjected to certain recursive inequality constraints. Finally, a numerical example and a practical three-tank system are utilized to demonstrate the correctness and effectiveness of the proposed estimation scheme.

This article concentrates on optimal tracking control for a class of nonlinear networked systems subjecting to limited network bandwidth and unmatched disturbance. Given the models of the control and reference systems, the considered optimal tracking control issue is initially formulated as a minimax optimization problem. Then, with the introduction of an event-triggered mechanism used for saving bandwidth, the formulated problem is transformed into solving an event-based Hamilton-Jacobi-Isaacs (HJI) equation by recurring to the Bellman optimality theory. Based on the HJI equation, we demonstrate that the stability of the concerned system in the sense of uniformly ultimately bounded (UUB) can be guaranteed with the envisioned optimal control and worst disturbance policies. Here, the disturbance policy can be varied periodically while the control policy can only be updated at event-triggering instants, which differs from the existed researches. Furthermore, we propose a reinforcement learning (RL)-based algorithm to handle the constructed HJI equation and thus settle the studied tracking control problem. The effectiveness of the algorithm is finally validated by both theoretical analysis and simulations.

This paper is concerned with the secure estimation problem for the state of charge of Lithium-ion batteries subject to malicious attacks during the data transmission from sensors to cloud-based battery management system terminal. First, the second-order resistance-capacitance equivalent circuit model, whose parameters are identified by Kalman filter in an off-line manner, is introduced to describe the internal dynamics of lithium-ion batteries. Then, by applying the$\chi ^{2}$detection mechanism, real-time malicious attacks are first detected and then a secure estimator is designed to suppress the influence of attacks on the estimation performance. An upper bound of the filtering error covariance is determined by solving certain coupled Riccati-like equations, and the filter parameter is obtained by minimizing such an upper bound at each time step. Finally, the validity of the proposed attack detection approach and the effectiveness of the developed estimation scheme are verified by experiment results under Federal Urban Driving Schedule condition.

This article is concerned with the problem of the$H_{\infty }$output feedback control for a class of event-triggered networked systems subject to multiple cyber attacks. Two dynamic event-triggered generators are equipped at sensor and observer sides, respectively, to lower the frequency of unnecessary data transmission. The sensor-to-observer (STO) channel and observer-to-controller (OTC) channel are subject to deception attacks and Denial-of-Service (DoS) attacks, respectively. The aim of the addressed problem is to design an output feedback controller, with the consideration of the effects of dynamic event-triggered schemes (DETSs) and multiple cyber attacks. Sufficient condition is derived, which can guarantee that the resulted closed-loop system is asymptotically mean-square stable (AMSS) with a prescribed$H_{\infty }$performance. Moreover, we provide the desired output feedback controller design method. Finally, the effectiveness of the proposed method is demonstrated by an example.

This article presents a novel stochastic event-triggered communication scheme (SETS) and a switching-like$H_{\infty }$control method for networked control systems (NCSs) through an open bandwidth-limited network. To overcome the stochastic Denial of Service (DoS) attacks and save the limited network resource, a SETS is first proposed to decrease the number of packets transmitted while considering the malicious DoS attacks. Compared with the existing event-triggered schemes under a precondition that all triggered packets must be successfully transmitted, the proposed SETS has outstanding flexibility since it allows part event-triggered packets to be lost during the communication process. To fully use the dynamic features of communication in an open network, a switching-like control scheme is well designed, which can actively choose the different controller gains based on the current network Quality of Services (QoSs). Then, a networked closed-loop model is constructed, which considered both the effects of SETS and stochastic DoS attacks in a unified framework. Sufficient conditions for the existence of the switching-like controller are presented to ensure the stability of the networked control system while achieving the prescribed performance index. Gain matrices of the desired switching-like controller and the SETS parameters are reached in the light of the solutions to certain matrix inequalities. Compared with the existing time-invariant control strategy in a transmission interval, higher control performance can be expected since both the dynamic network information and the latest available state are well considered in the proposed communication and control framework. Finally, an illustrative example is given to verify the effectiveness of the proposed scheme.

In this paper, the robust$H_{\infty }$control problem is studied for the inductively coupled power transfer (ICPT) process with coil misalignment, energy bounded interference and time-varying delay. The problem of coil misalignment between the primary and secondary coils are considered, and the time varying coupling coefficient are described in a sojourn-probability-based switch model. A set of random variables are utilized to describe the sojourn probabilities of the finite modes of the coil misalignment. It should be pointed out that the sojourn probability of the$i$th subsystem is obtained by the off-line statistical test instead of prescribed by hand, which is of more closer physical significance due to its engineering practice. Corresponding to the proposed switching systems, a set of controllers with time-varying delays are constructed. By resorting to the Lyapunov stability theory, the matrix analysis, and the stochastic analysis tools, the delay-dependent sufficient criteria with less conservatism are derived to guarantee the mean square stability of the ICPT system and the given interference attenuation level$\gamma $. The feedback gains of the desired controller can be readily obtained by using the control toolbox in Matlab software. Three cases of the illustrative examples without exception verify the effectiveness of the suggested modeling method and control scheme.

In this study, the probabilistic-constrained$\mathcal {H}_{\infty }$tracking control problem is addressed for a class of stochastic nonlinear systems subject to measurement outliers, stochastic nonlinearities and DoS attacks. The considered systems have the following characters: 1) an unequal redundant-channel communication protocol is adopted to resist the DoS attacks and enhance the reliability of packet transmission. Different from some existing redundant-channel approaches, the considered two channels have different transmission abilities. 2) The measurement outliers (e.g., some unexpected large amplitude disturbances) are concerned and a saturation-function-based observer is proposed to avert the side effects from measurement outliers. 3) The DoS jamming attacks are considered in this paper, and an aperiodic DoS model is utilized. 4) Both sector-bounded nonlinear function and stochastic nonlinear function are involved in the considered systems, which makes the considered systems more general. To compensate the negative effect of the DoS jamming attacks and enhance the reliability of the packet transmission, a novel redundant-channel-based switching protocol is proposed to schedule data transmission when the plant suffers malicious cyber attacks. The primary objective of the present study is to design tracking controller such that the prescribed$\mathcal {H}_{\infty }$control performance is reached and probabilistic constraints on the tracking error are satisfied simultaneously. To achieve this objective, a probabilistic-constrained$\mathcal {H}_{\infty }$tracking control algorithm is established to obtain tracking controller gains and minimize the prescribed set of the constraint. Finally, two simulation examples are used to validate the practicability of our devised strategy.

This paper is concerned with the problem of bipartite consensus tracking for multi-agent systems with both cooperative and antagonistic interactions. These interactions among vertices in a signed digraph are first investigated with the signs of the weighted products of associated paths in their augmented undirected graph. To improve the convergence rate, a finite-time protocol and a fixed-time protocol are developed to solve the bipartite consensus tracking problem with a finite-time and a settling time independent of any initial states, respectively. Moreover, an M-matrix based method is well proposed to estimate all parameters in the constructed Lyapunov function, therefore, the upper bounds of the settling times for finite-time and fixed-time cases can be analytically determined. Finally, numerical examples are provided to demonstrate the effectiveness of proposed methods.

This article is concerned with the problem of observer-based dynamic event-triggered control for a networked control system (NCS) under a class of power-constrained denial-of-service (DoS) attacks that aim at impeding the network communication from time to time. First, by carefully modeling such DoS attacks as aperiodic pulse-width-modulated (PWM) jamming signals, a switching observer, adapting to the DoS attacks, is delicately constructed to deal with the unavailability of full-state information. Second, to economize the limited bandwidth resources, a dynamic event-triggered communication scheme is designed under the aperiodic DoS jamming attacks, whose duration and frequency are assumed to be restricted. Third, a switching system model with artificial state delay is formulated, which characterizes the effects of the aperiodic DoS attacks and event-triggered communication scheme in a unified framework. Then, the asymptotic stability analysis and controller/observer synthesis conditions of the resulting switching system are obtained by using a piecewise Lyapunov-Krasovskii functional approach. Furthermore, a co-design method of the dynamic triggering parameters, controller, and observer gains is presented. Finally, an example is employed to verify the effectiveness of the obtained results.

X-ray images of castings are widely used in manufacturing for quality assurance. This article investigates the X-ray-image-based defective detection. The main contributions in this article are twofold: first, a new full-image method is proposed to classify defective castings and nondefective ones; and second, by combining two technologies, spatial attention mechanism and bilinear pooling used in deep convolutional neural networks (CNNs), a new spatial attention bilinear CNN is proposed to enhance the representation power of CNN. To validate the above initiatives, extensive experimental studies have been carried out to show the advantages of the new method over a number of existing ones.

Automated herb identification plays an important role in protecting and investigating the herbs for botanists, which has been widely applied in the field of cosmetic, medical and food industry areas. Traditionally, due to the complicated background and various herb patterns, herb discriminative feature extractions is a hard work. And some existing methods may not be applicable when the herbs exist in actual wild environment. Therefore, how to locate the valid herb regions and extract the effective features is an open issue. In this paper, a novel CNN model is proposed for herb identification by using the part-information perception module (PPM) and species classification module (SCM). A new attention mechanism, namely part priority attention mechanism (PPAM), is proposed by training PPM independently with herb part labels. It should be pointed out that the proposed PPAM can guide the model to focus on the position of herb parts and suppress the irrelevant noisy regions. Moreover, depthwise separable convolution and label smoothing technique are introduced to decrease the model complexity and regularize the impact of mistake labels. In the experiment part, a large-scale herb dataset is constructed, which consists many kinds of challenging herb species in the wild environment. Additionally, these images contains both species labels and part labels. Experimental results demonstrate that our model achieves obviously improvement in term of accuracy and model size compared with other classical deep learning models.

This article proposes a memory-based eventtriggering H∞load frequency control (LFC) method for power systems through a bandwidth-constrained open network. To overcome the bandwidth constraint, a memory-based event-triggered scheme (METS) is first proposed to reduce the number of transmitted packets. Compared with the existing memoryless event-triggered schemes, the proposed METS has the advantage to utilize series of the latest released signals. To deal with the random deception attacks induced by open networks, a networked power system model is well established, which couples the effects of METS and random deception attacks in a unified framework. Then, a sufficient stabilization criterion is derived to obtain the memory H∞LFC controller gains and event-triggered parameters simultaneously. Compared with existing memoryless LFC, the control performance is greatly improved since the latest released dynamic information is well utilized. Finally, an illustrative example is used to show the effectiveness of the proposed method.

This paper examines quantized stabilization for Takagi-Sugeno (T-S) fuzzy systems with a hybrid-triggered mechanism and stochastic cyber-attacks. A hybrid-triggered scheme, which is described by a Bernoulli variable, is adopted to mitigate the burden of the network. By taking the effect of the hybrid-triggered scheme and stochastic cyber-attacks into consideration, a mathematical model for a closed-loop control system with quantization is constructed. Theorems for main results are developed to guarantee the asymptotical stability of networked control systems by using Lyapunov stability theory and linear matrix inequality techniques. Based on the derived sufficient conditions in theorems, the controller gains are presented in an explicit form. Finally, two practical examples demonstrate the feasibility of designed algorithm.

This study deals with the problem of robust H ∞ finite‐time boundedness analysis and design of finite‐time H ∞ feedback control for discrete switched systems. By using sojourn probability information, a new kind of switched system model is proposed. Based on this model, sufficient conditions for the H ∞ finite‐time boundedness are obtained by using Lyapunov functional method and the corresponding controllers are also designed. Moreover, when considering partially known probabilities and unknown system uncertainties, the analysis and design methods are also given. Finally, an example is given to illustrate the efficiency of the proposed method.

This study is concerned with the reliable filtering problem for the sampled‐data system subject to a class of probabilistic sensor signals distortion. A new distortion model is developed by introducing a diagonal random matrix whose elements obey the Gaussian distribution. The main purpose in this study is to design a filter such that the error dynamics of the filtering process subject to the probabilistic sensor signal distortion is mean‐square asymptotically stable. Based on the modified delay‐central‐point (DCP) method and the convexity property of the matrix inequality, new criteria are derived for the existence of the desired H ∞ filters, by which it leads to much less conservative analysis results. Simulation results are provided to illustrate the effectiveness of the proposed method.

This study considers robust H ∞ filtering for discrete system with uncertain multiple missing measurements and probabilistic non‐linearities. The measurement missing rate is assumed to be known with uncertainties and the non‐linearities’ variation information between different bounds is utilised to build new type of non‐linearity model. Employing these new characteristics, new filtering error dynamics with time delay and uncertainties are proposed. By using Lyapunov functional approach, sufficient conditions on the H ∞ performance analysis are obtained, and the desired filter parameters can also be obtained by solving certain linear matrix inequalities. Finally, an example is proposed to show the effectiveness of the proposed model and design procedures.

This study considers H ∞ filter design for a class of linear networked control systems with quantisation. The effects of network‐induced delay, data loss and signal quantisation on the system is considered, where the quantiser is selected to be logarithmic. Based on the modified delay‐central‐point method and using the convexity property of the matrix inequality, new criteria are derived for the H ∞ performance analysis of the filtering‐error systems, which can lead to a less conservative analysis results and thus reduce the overdesign of the filter. Two examples are provided to show the less conservatism of the derived analysis results and also to illustrate the design procedures of the filters’ parameters.

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

This paper investigates robust stability analysis and stabilization of delay and uncertain systems approximated by a Takagi-Sugeno (T-S) fuzzy model. An innovative approach is proposed to develop delay-dependent stability criteria of the systems, which makes use of less-redundant information to construct Lyapunov function, employs an integral equation method to handle the cross-product terms, and alleviates the requirements of the bounding technique and model transformations that have been popularly adopted in many existing references. This leads to significant improvement in the stability performance with far fewer unknown variables in the stability computation. From the derived stability criteria, a new memoryless state-feedback control is further developed. The controller gain and the maximum allowable delay bound of the closed-loop control system can be obtained simultaneously by solving an optimization problem. Numerical examples are also given to demonstrate the theoretical results.

Motivated by the study of a class of networked control systems, this correspondence paper is concerned with the design problem of stabilization controllers for linear systems with stochastic input delays. Different from the common assumptions on time delays, it is assumed here that the probability distribution of the delay taking values in some intervals is knowna priori. By making full use of the information concerning the probability distribution of the delays, criteria for the stochastic stability and stabilization controller design are derived. Traditionally, in the case that the variation range of the time delay is available, the maximum allowable bound of time delays can be calculated to ensure the stability of the time-delay system. It is shown, via numerical examples, that such a maximum allowable bound could be made larger in the case that the probability distribution of the time delay is known.

In this paper, we are concerned with the problem of stability analysis and stabilization control design for Takagi-Sugeno (T-S) fuzzy systems with probabilistic interval delay. By employing the information of probability distribution of the time delay, the original system is transformed into a T-S fuzzy model with stochastic parameter matrices. Based on the new type of T-S fuzzy model, the delay-distribution-dependent criteria for the mean-square exponential stability of the considered systems are derived by using the Lyapunov-Krasovskii functional method, parallel distributed compensation approach, and the convexity of some matrix equations. The solvability of the derived criteria depends not only on the size of the delay but also on the probability distribution of the delay taking values in some intervals. The revisions of the main criteria in this paper can also be used to deal with the case when only the information of variation range of the delay is considered. It is shown by practical examples that our method can lead to very less conservative results than those by other existing methods.

This brief is concerned with the analysis problem of global exponential stability in the mean square sense for a class of linear discrete-time recurrent neural networks (DRNNs) with stochastic delay. Different from the prior research works, the effects of both variation range and probability distribution of the time delay are involved in the proposed method. First, a modeling method is proposed by translating the probability distribution of the time delay into parameter matrices of the transformed DRNN model, where the delay is characterized by a stochastic binary distributed variable. Based on the new method, the global exponential stability in the mean square sense for the DRNNs with stochastic delay is investigated by using the Lyapunov-Krasovskii functional and exploiting some new analysis techniques. A numerical example is provided to show the effectiveness and the applicability of the proposed method.