Search Results (227)

Climate change and aging infrastructure are undermining the resilience of urban flood management systems, reducing their reliability and increasing the likelihood of systemic failure that may culminate in regime shifts. This study develops a conceptual and practitioner-oriented screening framework based on a quantitative mathematical model to examine facility-level resilience and identify threshold conditions that may trigger regime transitions under external disturbances and varying pre-disturbance facility conditions. The framework adopts the composite sigmoid function (CSF) to capture nonlinear performance trajectories of infrastructure systems. Building on this model, this study extends its application by developing a parameterization scheme directly linked to four resilience dimensions: robustness, redundancy, rapidity, and resourcefulness (4Rs), which can be derived from field investigations or expert surveys. The normalized 4R scores are mapped to the CSF parameters, thereby converting static resilience assessment results into degradation and recovery curves. To search for threshold conditions, a parametric analysis was conducted by systematically varying the 4R values across their defined ranges. Rather than indicating a single universal threshold value, the results revealed critical threshold regions formed by specific combinations of the 4R dimensions. Lower robustness reduced the initial performance buffer, and low redundancy accelerated and extended performance degradation, while insufficient rapidity and resourcefulness delayed or limited recovery, increasing the likelihood of transition into an alternative degraded regime. For example, even when R1 and R2 were set to relatively high normalized values of 0.90, and R3 was set to its maximum value of 1.00, full recovery could not be achieved when R4 decreased below approximately 0.20. An illustrative application was conducted using preliminary 4R assessment results for flood-control facilities in three districts of Seoul, Korea. The model-derived trajectories were qualitatively compared with reported historical vulnerability patterns. While this comparison was intended as a contextual assessment rather than an event-specific empirical validation, our framework supports comparative, scenario-based screening of potentially vulnerable facilities for preliminary maintenance and investment prioritization. Full article

In DC microgrid (DC-MG) clusters, distributed generation units rely on electronic communication networks to exchange voltage measurements, current information, and coordination signals for voltage recovery and current sharing. As the degree of system clustering and communication coupling increases, nodal denial-of-service (DoS) attacks may interrupt the information exchange of leaders and followers, resulting in communication topology switching and degraded cooperative control performance. Accordingly, this paper proposes an event-triggered (ET) resilient control scheme with high communication efficiency for networked DC-MG clusters under nodal DoS attacks. First, a distributed secondary control model with a cross-layer communication mechanism is constructed in accordance with the requirements of the system’s overall power distribution, which incorporates the two-layer node architecture of leaders and followers in DC-MG clusters. Second, a statistical multimode nodal DoS attack model is developed to characterize heterogeneous communication interruptions through topology-dependent attack modes and their occurrence probabilities. Finally, an exponential threshold ET mechanism based on bus-voltage recovery errors is designed within the distributed secondary control framework to reduce redundant information transmission while preserving resilience against nodal communication attacks. Simulation results demonstrate that the proposed method can maintain accurate voltage recovery and current sharing in networked DC-MG clusters under large-scale DoS attacks, while improving communication efficiency through ET updates. Full article

Quantifying the soil moisture (SM) response to precipitation is pivotal for predicting hydrologic resilience and ecosystem stability in fragile cold regions. This is true in alpine permafrost environments characterized by variable precipitation phases and strong seasonal freeze–thaw dynamics. However, critical knowledge gaps persist due to the scarcity of high-resolution, multi-layer in situ observations in these remote areas. Using hourly data from three sites in the Tanggula Mountains (2020–2024), this study employs an event-based analytical framework combining logistic regression and linear regression to quantify multi-layer (10–100 cm) SM responses to rain, snow, and mixed-phase precipitation across seasons. Core findings indicate the following: (1) Precipitation thresholds with 80% probability of triggering SM responses rise sharply with depth during the cold period (10 cm: 1–11 mm; 50–100 cm: often >15 mm or unreachable) but increase gradually in the warm period (10 cm: 0.4–5 mm; 50 cm: <15 mm). Mixed-phase precipitation refers to the lowest amount of precipitation (0.4–2.5 mm at 10 cm), followed by rain (1–11 mm) and snow (2–5 mm). (2) Warm-period regression slopes are consistently steeper than cold-period slopes (at 10 cm, 0.0024 vs. 0.0010 for rainfall). Mixed-phase precipitation yields the steepest slopes, approximately 50% higher than rainfall at 10 cm in the warm period (0.0037 vs. 0.0024), due to its longer duration and dual-supply mode. For lag time, cold-period values are more widely dispersed due to multiple interacting factors, while warm-period values are concentrated; only warm-period rainfall exhibits a clear monotonic increase in lag time with depth, consistent with unsaturated flow theory. (3) The quantified regression slopes, threshold values, and phase-specific efficiencies provide transferable metrics for calibrating infiltration models and evaluating frozen-ground hydrology schemes. The finding that mixed-phase events are the primary driver of deep-layer recharge, despite accounting for a smaller fraction of the total event count, has direct implications for water resource assessment in high-altitude catchments where precipitation phase composition is often oversimplified. Overall, this study moves beyond qualitative descriptions by providing quantifiable, transferable metrics that advance the mechanistic understanding of precipitation–SM coupling in alpine permafrost regions. Full article

This study addresses an integrated security control and communication co-design problem for nonlinear CPSs subject to coexisting FDI attacks and actuator faults. A novel adaptive discrete event-triggered communication scheme (ADETCS) is proposed. Its triggering threshold adapts to the system state. State estimation, fault estimation, and attack detection are all migrated to the control unit. Based on this framework, a closed-loop T–S fuzzy model is established for active defense against actuator faults and dual-end FDI attacks. A robust augmented observer is then developed via Lyapunov stability theory to jointly estimate system states, actuator faults, and FDI attacks. Sufficient conditions are further derived for an integrated security controller that unifies attack tolerance and fault tolerance. Simulation results on a quadruple-tank system show that the proposed method effectively counteracts coexisting attacks and faults while significantly reducing resource consumption. Over an 800-s horizon, data transmissions drop to 712 (8.9% transmission rate). The sensor-node computational load is also reduced from 8000 time-triggered executions to 712 event-triggered ones. Full article

This work investigates the adaptive event-triggered distributed estimation problem for discrete time-varying nonlinear stochastic systems over sensor networks exposed to replay attacks within a finite-horizon setting. The sensor network comprises multiple nodes whose interaction structure is described by two randomly switching directed graphs. The plant under consideration is formulated as a discrete time-varying nonlinear stochastic system obeying a sector-bounded condition. To mitigate communication overhead, an adaptive event-triggered scheme is employed, where the triggering threshold is dynamically updated based on the triggering error. In addition, replay attacks are considered, wherein an adversary randomly replaces current data packets with previously recorded ones. A compensation mechanism is devised to neutralize the impact of such attacks. By building a distributed estimator and formulating an augmented estimation error system, sufficient criteria are established via Lyapunov theory and stochastic analysis to ensure the prescribed average $H_{\infty }$ performance level is attained. The estimator gains are computed recursively by solving a sequence of recursive linear matrix inequalities (RLMIs). A design algorithm for the distributed estimator is also provided to support online implementation. Finally, a numerical simulation example is given to demonstrate the effectiveness of the proposed estimation approach. Full article

Time synchronization plays a critical role in enabling coordinated control and accurate data fusion in the Industrial Internet of Things (IIoT). However, most existing time-triggered synchronization protocols rely on periodic information exchange, which leads to considerable communication and energy consumption, particularly in large-scale and resource-constrained deployments. To address these limitations, this study proposes an adaptive event-triggered time synchronization scheme that eliminates the need for periodic communication. Unlike conventional approaches that employ fixed or predefined time-varying thresholds, the proposed method constructs a fully distributed triggering mechanism based on both local clock evolution and synchronization discrepancies observed from neighboring nodes. The triggering threshold evolves automatically according to the network synchronization state and does not require additional coordination messages. Theoretical analysis shows that the logical clock skews asymptotically converge to a common value, while the logical clock offset disagreement is ultimately bounded within an explicitly characterized neighborhood. Simulation results demonstrate that the proposed scheme achieves a more effective balance between synchronization accuracy and communication overhead, while producing more evenly distributed triggering events than several representative event-triggered synchronization methods. Full article

This paper investigates an observer-based secure control method for networked non-Lipschitz nonlinear systems subject to unknown nonlinearities, external disturbances, sensor noises, and intermittent denial-of-service (DoS) attacks. Multi-layer neural networks (MNNs) are adopted to compensate for non-smooth, non-Lipschitz terms, guaranteeing bounded approximation errors. A resilient high-gain observer fused with the MNN is developed to continuously reconstruct system states. When DoS attacks block sensor channels, the observer acts as a virtual dynamic engine to substitute for lost real-time measurements, providing uninterrupted feedback to the controller. Furthermore, to optimize communication efficiency, an observer-based static event-triggered mechanism (SETM) coupled with a hold-input strategy is integrated. Employing the Lyapunov–Krasovskii functional method, sufficient conditions are derived to prove that the closed-loop system remains uniformly ultimately bounded (UUB) under the joint effects of approximation errors, disturbances, and attacks. Simulation results on a two-link manipulator demonstrate that the proposed secure control scheme effectively counters aggressive DoS attacks while achieving a 56.8% reduction in network transmissions compared with conventional periodic sampling paradigms, striking a favorable balance between tracking accuracy and resource efficiency. Full article

Underactuated and strongly coupled Quadrotor Unmanned Aerial Vehicle (QUAV) systems often face challenges in formation control due to actuator failures, external unknown disturbances, and limited communication resources. To address these issues, this paper proposes a periodic adaptive event-triggered fixed-time fault-tolerant control method based on a disturbance observer. First, a dynamic estimation and compensation scheme for actuator faults is developed by combining boundary layer theory with adaptive control techniques. Next, a fixed-time disturbance observer is designed to accurately estimate and compensate for external unknown disturbances. Furthermore, considering the communication burden imposed by real-time position updates, a Non-Monitoring Periodic Adaptive Event-Triggered Control (NM-PAETC) mechanism is proposed to reduce communication resource consumption, while ensuring that the formation system maintains the desired attitude angles under the influence of actuator faults and external disturbances. The proposed method enables fixed-time formation control under limited communication resources, and the system’s convergence time is independent of the initial state. Simulation results validate the effectiveness of the proposed method. Full article

This paper provides a comprehensive review of recent studies on event-triggered control schemes for DC microgrids. Several event-triggered mechanisms (ETMs) are thoroughly discussed, including static, dynamic, self-triggered, and edge-based algorithms. Considering the strengths and weaknesses of these algorithms, it is found that although such ETMs can decrease communication burden in the system, they are also susceptible to communication delays, Zeno behaviour, sensitivity to control parameter changes in triggering conditions, and inability to adapt to the fluctuating nature of renewable energy sources (RESs). Furthermore, this article examines implementation challenges, including data packet loss, quantisation effects, actuator faults, and a lack of cybersecurity measures, to provide readers with a clear vision of future trends in this field. Based on the main findings of the investigation, this review paper proposes possible areas for future research, highlighting the need for event-triggered control schemes that operate in discrete time, handle delays, and adapt to varying operating conditions. Other concepts, including adaptive control parameters for triggering conditions based on machine learning, the adoption of advanced cybersecurity measures, and data-aware transmission approaches that consider both communication frequency and total data volume, are also discussed. To conduct a comprehensive review of all the above-mentioned ETMs, several databases, including IEEE Xplore, Elsevier, and MDPI, were searched using the main keywords in this field, such as event-triggered, self-triggered, and edge-based ETMs, in conjunction with DC microgrids. This facilitated an in-depth analysis of such control schemes, including their strengths and weaknesses, providing readers with a strong basis for selecting a proper control scheme suited to their future research. Full article

To address the trajectory tracking problem of networked unmanned surface vessels (USVs) under false data injection (FDI) attacks, an adaptive neural network-based prescribed performance control scheme is proposed. First, considering the adverse effects of network attacks, system uncertainties, and time-varying disturbances, an adaptive neural network observer is designed to estimate and compensate for the lumped disturbances. Building on this, a dynamic event-triggered mechanism is separately developed for the sensor-to-controller and controller-to-actuator channels, forming a novel dual-channel dynamic event-triggered mechanism (DDETM). This mechanism reduces unnecessary communication overhead and actuator wear caused by frequent data exchanges while enabling thrust allocation for a quantitative analysis of actuator degradation. Furthermore, a control algorithm based on a second-order prescribed performance function (SOPPF) and dynamic surface control (DSC) is proposed to ensure transient and steady-state performance of the tracking error while mitigating the computational complexity associated with the traditional backstepping method. Using Lyapunov theory, it is demonstrated that all signals in the closed-loop system are uniformly ultimately bounded and that Zeno behavior is avoided. Simulation results further validate the effectiveness of the proposed control approach in solving the trajectory tracking problem of USV. Full article

This paper investigates the problems of anti-synchronization for a class of inertial neural networks (INNs) with time-varying delays under the influence of deception attacks and hybrid triggered control. A novel dynamic hybrid-triggered control (DHTC) scheme is developed to utilize communication resources and enhance network security efficiently for the model INNs. By integrating the extended dissipative approach with Lyapunov–Krasovskii functional (LKF) techniques, new sufficient conditions are established to ensure the quadratic stability of the resulting closed-loop system. The proposed framework not only unifies the anti-synchronization problems but also extends classical passivity, (Q, S, R)-dissipative, $H_{\infty }$, and $L_2-L_{\infty }$ results as special cases. Moreover, the DHTC mechanism dynamically switches between time-triggered and event-triggered modes, reducing unnecessary signal transmissions while maintaining system stability against deception attacks. Finally, simulation results on delayed INNs demonstrate the effectiveness and superiority of the proposed theoretical and control strategy. Full article

Multi-UAV formation control in low-altitude urban environments faces critical challenges from atmospheric boundary layer (ABL) disturbances, including turbulence, wind gusts, and communication inefficiency in resource-constrained swarms. This paper proposes an adaptive dynamic event-triggered formation control (ADETFC) strategy integrated with a finite-time disturbance observer (FTDO) for multi-agent hexarotor UAV formations operating under ABL conditions. The novelty of the proposed ADETFC lies in employing dual adaptive parameters to simultaneously account for tracking error magnitude and inter-agent formation geometry, dynamically adjusting communication frequency. A nonsingular terminal sliding mode manifold ensures rapid transient convergence and robustness against nonlinearities and inter-agent coupling. The FTDO estimates lumped disturbances with finite-time convergence to a bounded residual neighborhood, enabling reduced control gains that mitigate chattering and actuator wear. Lyapunov-based analysis establishes finite-time reachability of the sliding manifold and guarantees that the tracking error converges to a bounded residual set in finite time. The Zeno-free operation is guaranteed by a strictly positive minimum inter-event time analytically derived from system dynamics. Simulations under three ABL scenarios, including Dryden turbulence, wind gusts, and sinusoidal disturbances, demonstrate formation tracking RMSE reductions of up to 29.4%, disturbance estimation RMSE reductions of up to 54.3%, and communication-event reductions of 46.4–63.2% compared with benchmark schemes. These results confirm accurate formation tracking, efficient communication, and robust multi-agent networking under challenging wind conditions, making the framework suitable for networked UAV applications in complex environments. Full article

Micro quadrotor unmanned aerial vehicles (UAVs) are highly sensitive to external disturbances and model uncertainties because of their small mass, low moment of inertia, and limited onboard computational resources. To improve the disturbance rejection and trajectory tracking performance of micro quadrotor UAVs, this paper proposes an event-triggered extended state observer (ET-ESO)-based prescribed-time funnel control (PTFC) method. First, a control-oriented dynamic model of the micro quadrotor is established, in which wind disturbances, unmodeled aerodynamic effects, damping uncertainties, and parameter perturbations are represented as lumped disturbances in the translational and rotational subsystems. Then, two event-triggered ESOs are designed to estimate the lumped disturbances of the velocity and angular velocity channels. Compared with conventional continuously sampled ESO schemes, the proposed event-triggered mechanism reduces the frequency of sensor-to-controller information transmission while preserving disturbance estimation capability. Furthermore, a prescribed-time funnel control law is developed to constrain the position and attitude tracking errors within predefined performance boundaries and ensure convergence to the desired accuracy region within a user-specified time. Lyapunov-based stability analysis is provided to prove the boundedness of all closed-loop signals and the validity of the prescribed funnel constraints. Finally, MATLAB/Simulink simulations based on the Parrot Mambo mini-drone parameters are conducted to verify the effectiveness of the proposed method. The results demonstrate that the proposed controller achieves robust trajectory tracking, effective disturbance compensation, improved transient performance, and reduced control update frequency. Full article

This paper investigates the problem of non-fragile dissipative filtering for discrete-time nonlinear networked systems with dynamic quantization, a dynamic event-triggered mechanism and stochastic cyber attacks. The nonlinear networked system under investigation is described by an uncertain Takagi–Sugeno (T-S) fuzzy model. In this work, a novel fuzzy-dependent dynamic event-triggered communication scheme and the dynamic quantization strategy, integrated with an online adjustment rule, are introduced to reduce the frequency and volume of data transmission, thus realizing more rational utilization of the limited communication resources. In addition, the stochastic cyber attacks are characterized by a random variable obeying the Bernoulli distribution. The core focus of this paper is to design a non-fragile filter such that the resulting filtering error system is stochastically stable and meets the prescribed dissipative filtering performance. Based on the matrix inequality decoupling technique, the design conditions of the desired filter are derived and presented in the form of linear matrix inequalities (LMIs). Finally, the effectiveness and superiority of the proposed filter design approach is verified via two simulation examples. Full article

To meet the requirement for high-precision dynamic positioning of fully actuated vessels under wave-frequency disturbances, and to achieve clock-synchronous triggering for system analysis, decision-making and reliable communication, this paper proposes a minimum-time simultaneous triggering (MTST) scheme based on a modified self-adaptive observer. Firstly, the concepts of result-dependent event (RDE) and conflict are introduced to describe the internal coupling characteristics of the system and the continuous actuation behavior under the superposition of triggering signals. Then, for the estimation of yaw perturbation, a self-adaptive parameter algorithm is employed in the modified observer, whose stability is subsequently proven. To reduce channel occupancy during the cooperative transmission of distributed triggering signals, a multi-port scheme is proposed, including RDE, and a corresponding controller is designed. Furthermore, to avoid the computational explosion phenomenon and estimate complex nonlinear unknown terms, the dynamic surface method and radial basis function neural network are used in filtering and function approximation, respectively. Finally, theoretical derivations show that the multi-port processing ensures the stability of all system nodes without the Zeno phenomenon. Meanwhile, the MTST scheme also maintains system stability while effectively eliminating both the Zeno phenomenon and signal conflict. Numerical simulation results reveal that compared with the multi-port event-triggering (MET) scheme, the MTST scheme achieves performance improvements of 9.76%, 0.37%, and 43.15% in tracking precision, energy efficiency, and control smoothness, respectively, which demonstrates its prominent advantages in event-triggered control systems. While improving positioning accuracy, the scheme exhibits a slight slowdown in heading-direction convergence and introduces a heavier communication load. These characteristics reflect a fundamental trade-off: the MTST scheme provides superior control performance at the cost of an increased triggering frequency and greater communication overhead. Full article