Search Results (119)

This paper explores an event-triggered load frequency control (LFC) strategy for smart grids incorporating electric vehicles (EVs) under the influence of random deception attacks. The aggressive attack signals are launched over the channels between the sensor and controller, compromising the integrity of transmitted data and disrupting LFC commands. For the purpose of addressing bandwidth constraints, an event-triggered transmission scheme (ETTS) is developed to minimize communication frequency. Additionally, to mitigate the impact of random deception attacks in public environment, an integrated networked power grid model is proposed, where the joint impact of ETTS and deceptive interference is captured within a unified analytical structure. Based on this framework, a sufficient condition for stabilization is established, enabling the concurrent design of the ${\mathcal{H}}_{\infty }$ controller gain and the triggering condition. Finally, two case studies are offered to illustrate the effectiveness of the employed scheme. Full article

This paper investigates the event-triggered dissipative fuzzy tracking control problem of nonlinear networked systems with dynamic quantization and stochastic deception attacks, where the Takagi–Sugeno (T-S) fuzzy system theory is utilized to represent the studied nonlinear networked systems. The event-triggered scheme and the dynamic quantization scheme with general online adjustment rule are employed to significantly decrease the data transmission amount and achieve the rational use of the limited communication and computation resources. A stochastic variable satisfying the Bernoulli random binary distribution is utilized to model the phenomenon of the stochastic deception attacks. The main purpose of this paper is to develop a secure event-triggered quantized tracking control scheme. This scheme guarantees the stochastic stability and prescribed dissipative tracking performance of the closed-loop system under stochastic deception attacks. Moreover, the design conditions for the desired static output feedback tracking controller are formulated in the form of linear matrix inequalities based on the matrix inequality decoupling strategy. Finally, two examples are exploited to illustrate the effectiveness of the developed tracking control scheme. Full article

This paper proposes a composite control method that integrates an active fault-tolerant predictive control scheme and an event-triggered mechanism for networked multi-agent systems. The approach considers random communication constraints in the forward and feedback channels as well as actuator faults. At each time instant, the event trigger determines whether to send system outputs based on the current system state. A Kalman filter is then utilized to estimate both the system state and potential faults by incorporating system output information transmitted through the feedback channel. Concurrently, iterative predictions are performed according to the established system model. Furthermore, a predictive sequence of control inputs is generated through the designed control protocol. Leveraging timestamping technology, the system precisely applies the appropriate control commands to the actuator at designated moments. As a result, the proposed control method compensates for both random communication constraints and actuator faults while effectively reducing data transmission over the communication network. Finally, the proposed method is validated through numerical simulations. Full article

This paper investigates the problem of interval estimation and fault detection for nonlinear cyber–physical systems (CPSs) subject to disturbances and random actuator/sensor faults. First, with the purpose of reducing the burden of data transmission, we introduce the dynamic event-triggered mechanism (DETM). For systems violating the non-negativity condition, a coordinate transformation method is applied to enhance the design flexibility. Then, the dynamic event-triggered interval observer (DETIO) is constructed and the effectiveness of DETIO is validated by demonstrating the ultimate uniform boundedness of the error system. The fault detection is achieved by considering faults in the CPSs and continuously monitoring residual signals. Finally, two numerical simulations under both faulty and fault-free conditions are shown to prove the effectiveness and superiority of the designed DETIO-based fault detection method. Full article

An electro-hydraulic servo system based on network control technology has the advantages of remote control, modularisation, and resource sharing. However, the electro-hydraulic servo system itself has the characteristics of strong nonlinearity and parameter uncertainty, combined with the limited communication bandwidth of the network control system, which leads to a poor control effect and limits its application. To solve the above problems, an event-triggered mechanism is introduced to filter redundant data and conserve the communication bandwidth, while a backstepping sliding mode control strategy integrating this event-triggered mechanism with a jamming observer is proposed. A model-based disturbance observer is designed to mitigate external interference effects on system control performance while enhancing robustness and disturbance response capabilities. The global stability of the closed-loop system is analysed using Lyapunov stability theory. The experimental results show that the system displacement tracking error of the controller designed in this paper can reach ±0.001 rad, and the system can reach stability in about 0.5 s. At the same time, it can significantly reduce the amount of data transmission, which effectively solves the problem of the network bandwidth limitation. Full article

This paper investigates the problem of robust fusion state estimation for multi-sensor systems under the influence of time-correlated fading channels, incorporating a dynamic event-triggered mechanism (DETM). The randomly occurring parameter uncertainties are characterized by a stochastic variable following a Bernoulli distribution, while sensor measurements are transmitted to the corresponding estimators through time-correlated fading channels and dynamic event-triggered mechanisms. The DETM dynamically adjusts the triggering threshold via regulation and memory factors, enhancing adaptability in data transmission while effectively reducing redundant communication overhead. Furthermore, an augmented state model is constructed by integrating system states, channel coefficients, and the event-triggering mechanism, thereby comprehensively capturing the impact of dynamic environments on state estimation. Based on this model, a local state estimation algorithm is designed to ensure the convergence of the upper bound of the local estimation error covariance, which is further minimized at each time step through adaptive adjustment of local estimator gains. Subsequently, the local estimates obtained from multiple estimators are fused using the covariance intersection fusion strategy, improving the overall estimation accuracy. Simulation experiments demonstrate that the proposed recursive fusion state estimation framework significantly reduces communication overhead and enhances estimation performance in the presence of both time-correlated fading channels and randomly occurring parameter uncertainties, while maintaining an acceptable computational cost. Compared to the traditional Kalman filtering method, the proposed recursive fusion state estimation algorithm improves estimation accuracy by 58% while increasing computational time by only 32.4%. Additionally, the DETM effectively reduces communication frequency by 36.7% Full article

Event-triggered state estimation has attracted significant attention due to the advantage of efficiently utilizing communication resources in wireless sensor networks. In this paper, an adaptive robust event-triggered variational Bayesian filtering method is designed for heavy-tailed noise with inaccurate nominal covariance matrices. The one-step state prediction probability density function and the measurement likelihood function are modeled as Student’s t-distributions. By choosing inverse Wishart priors, the system state, the prediction error covariance, and the measurement noise covariance are jointly estimated based on the variational Bayesian inference and the fixed-point iteration. In the proposed filtering algorithm, the system states and the unknown covariances are adaptively updated by taking advantage of the event-triggered probabilistic information and the transmitted measurement data in the cases of non-transmission and transmission, respectively. The tracking simulations show that the proposed filtering method achieves good and robust estimation performance with low communication overhead. Full article

Permanent magnetic linear synchronous motors (PMLSMs) have emerged as a promising solution for low-carbon urban rail transit systems due to their superior energy efficiency. However, their widespread adoption is hindered by significant challenges in achieving high-precision cooperative control and fault-tolerant operation across multi-PMLSMs. To address these issues, this paper proposed a novel composite observer-based adaptive fault-tolerant cooperative control framework, which enables reliable speed synchronization in multi-PMLSM urban rail traction systems through three key innovations. Initially, the stuck fault of the actuator is modeled based on the PMLSM dynamic model, and a composite observer is proposed to estimate lumped disturbances and actuator faults simultaneously, enhancing the system’s robustness against uncertainties and faults. A novel sliding mode control scheme with adaptive parameters is subsequently developed to compensate for disturbances and improve tracking accuracy. Furthermore, two event-triggered schemes are devised to reduce the communication burden, ensuring efficient data transmission without compromising control performance. The proposed method ensures high-precision synchronization and fault tolerance under actuator stuck faults, bias faults, and external disturbances, as validated by simulation results. By improving energy efficiency and reducing communication load, the proposed method contributes to the development of low-carbon urban rail transit systems, aligning with global sustainability goals. Full article

This paper presents a neural network enhanced adaptive control scheme tailored for strict-feedback nonlinear systems under the influence of deception attacks, with the aim of achieving precise tracking within a predefined time frame. Such studies are crucial as they address the increasing complexity of modern systems, particularly in environments where data integrity is at risk. Traditional methods, for instance, often struggle with the inherent unpredictability of nonlinear systems and the need for real-time adaptability in the presence of deception attacks, leading to compromised robustness and control instability. Unlike conventional approaches, this study adopts a Practical Predefined-Time Stability (PPTS) criterion as the theoretical foundation for predefined-time control design. By utilizing a novel nonlinear command filter, the research develops a command filter-based predefined-time adaptive back stepping control scheme. Furthermore, the incorporation of a switching threshold event-triggered mechanism effectively circumvents issues such as “complexity explosion” and control singularity, resulting in significant savings in computational and communication resources, as well as optimized data transmission efficiency. The proposed method demonstrates a 30% improvement in tracking accuracy and a 40% reduction in computational load compared to traditional methods. Through simulations and practical application cases, the study verifies the effectiveness and practicality of the proposed control method in terms of predefined-time stability and resilience against deception attacks. Full article

Herpesviruses are a group of DNA viruses capable of infecting multiple mammalian species, including humans. This review primarily summarizes four common alphaherpesviruses found in pets and livestock (feline, swine, canine, and bovine) in aspects such as epidemiology, immune evasion, and latency and reactivation. Despite the fact that they primarily infect specific hosts, these viruses have the potential for cross-species transmission due to genetic mutations and/or recombination events. During infection, herpesviruses not only stimulate innate immune responses in host cells but also interfere with signaling pathways through specific proteins to achieve immune evasion. These viruses can remain latent within the host for extended periods and reactivate under certain conditions to trigger disease recurrence. They not only affect the health of animals and cause economic losses but may also pose a potential threat to humans under certain circumstances. This review deepens our understanding of the biological characteristics of these animal alphaherpesviruses and provides an important scientific basis for the prevention and control of related diseases. Full article

Virtual synchronous generator (VSG) control, which can provide inertia output, damp power oscillations, and offer frequency and voltage support to power grids, has become a growing trend in the control field of wind power generation. As a new technology, there are still challenges that VSG control has not solved well, such as transient overvoltage suppression. A kind of transient overvoltage, which often occurs during the commutation failures of HVDC transmission systems, will trigger a mass of wind turbine generators (WTGs) disconnecting from grids. To reduce the grid-disconnection risk of the virtual synchronous generator control-based doubly fed induction generators (VSG-DFIGs), this paper first analyzes the mechanism of the automatic voltage regulation (AVR) control usually employed by VSG-DFIGs, then proposes measures to suppress the transient overvoltage. To solve the problem of the reactive power response lag issued by VSG-DFIGs, which will further aggravate the transient overvoltage in continuous low and high voltage faults, the time constant of the AVR control is switched. To fully exploit the potential of the DFIGs’ reactive power support, the droop coefficient of the AVR control is switched during the abnormal voltage stages. The switched droop coefficient will change the rotor excitation current magnitude, thus adjusting the internal potential of a DFIG, finally better supporting or suppressing the terminal voltage during the low or high voltage periods. Simulation results based on the DIgSILENT/PowerFactory platform demonstrate that the proposed method can effectively suppress the transient overvoltage that occurs in continuous low and high voltage events caused by the commutation failures of HVDC transmission systems, reducing the number of WTGs disconnecting from the grids. Full article

Recently, solar and wind power generation have gained attention as pathways to achieving carbon neutrality, and Renewable Energy Resource Management System (RERMS) technology has been developed to monitor and control small-scale, distributed renewable energy resources. In this work, we present an Event-Triggered Transmission (ETT) algorithm for RERMS, which transmits sensor measurements to the base station only when necessary. The ETT algorithm helps prevent congestion in the communication channel between RERMS and the base station, avoiding time delays or packet loss caused by the excessive transmission of sensor measurements. We design a hybrid state estimation algorithm that combines Kalman and Finite Impulse Response (FIR) filters to enhance the estimation performance, and we propose a new ETT algorithm based on this design. We evaluate the performance of the proposed algorithm through experiments that transmit actual sensor measurements from a photovoltaic power generation system to the base station, demonstrating that it outperforms existing algorithms. Full article

Across the power grid infrastructure, deployed power transmission systems are susceptible to incipient faults that interrupt standard operations. These incipient faults can range from being benign in impact to causing massive hardware damage and even loss of life. The power grid is continuously monitored, and incipient faults are recorded by Digital Fault Recorders (DFRs) to mitigate such outcomes. DFR-recorded data allow for power quality forensics and event analysis, but this ability comes at the cost of high data storage and data transmission requirements. It is common for data older than two weeks to be overwritten due to storage limitations, without being analyzed. This inhibits the creation of long-term data libraries that would enable incipient fault forensics and the characterization of behavior that precedes them, which limits the development and implementation of preventive measures; thus, there is a critical need to reduce DFR-recorded data’s storage requirements. This work addresses this critical need by leveraging the cyclic and residual histograms and introducing the frequency and Root Means Squared (RMS) histograms, which alleviate the current high data storage requirements and provide effective Incipient Fault Prediction (IFP). The residual, frequency, and RMS histograms are an extension of the cyclic histogram, reduce the data storage requirement by up to $99.58$%, can be generated on the DFR without interrupting its normal operations, and are capable of predicting voltage arcing six hours before it is strong enough to trigger a DFR-recorded event. Full article

For dealing with the energy consumption in multi-agent systems (MASs), an event-triggered (ET) methodology is promising, which relies on the activation of communication devices only when communication of data is needed. This paper explores the leaderless consensus for nonlinear MASs using an adaptive ET approach via an output feedback methodology. This adaptive ET scheme is preferred as it can adapt to the environment through setting a communication threshold. The proposed approach renders the observed states of agents by use of nonlinear observers in an output feedback control dilemma, making it more practical. Simple Luenberger observers are developed to avoid the problem of always measuring agents’ states. The strategy of adaptive ET-based control is employed to minimize resource use and information transmission. Design conditions for the observer-based adaptive ET consensus control of nonlinear MASs have been derived via a Lyapunov function, containing state estimation error, consensus error, adaptation term, and nonlinearity bounds. In contrast to the existing methods, the present approach applies a more practical output feedback schema, uses adaptive ET proficiency, and deals with nonlinear agents. An example of a formation of autonomous underwater vehicles achieving the basic consensus realization between displacement and velocity is included to illustrate the viability of the resultant approach. Full article

The consensus problem in stochastic multi-agent systems (MASs) with Markovian switching is addressed by proposing a novel distributed dynamic event-triggered (DDET) technique based on periodic sampling to reduce information transmission. Unlike traditional event-triggered control, the proposed periodic sampling-based DDET method is characterized by the following three advantages: (1) The need for continuous monitoring of the event trigger is eliminated. (2) Zeno behavior in stochastic MASs is effectively prevented. (3) Communication costs are significantly reduced. Based on this, sufficient conditions for achieving consensus in the mean-square sense are derived using Lyapunov–Krasovskii functions, providing a solid theoretical foundation for the proposed strategy. The effectiveness of the proposed DDET control is validated through two numerical examples. Full article