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This paper investigates the event-triggered secondary regulation problem for islanded alternating-current (AC) microgrids with unknown communication link faults. In order to handle unknown time-varying communication link faults and avoid utilizing global information, a distributed secondary regulation strategy is proposed, which achieves voltage and frequency regulations, as well as power sharing. Meanwhile, to save system resources and relieve the communication burden, an adaptive event-triggered mechanism is designed. Finally, an islanded AC microgrid test system is built in MATLAB/SimPowerSystems to validate the proposed strategy. It indicates that the proposed strategy highly reduces the controller updates and increases the reliability of system.

This paper addresses state estimation issues of large-scale systems with measurements subject to deception attacks, where the communication topology among sub-estimators is the same as the physical coupling structure of the subsystems. In consideration of the limited channel bandwidth, a novel adaptive event-triggered scheme is proposed for governing the data transmission among sub-estimators. With the help of Lyapunov analysis approaches, sufficient conditions are derived to ensure the input-to-state stability of the dynamics of estimation errors. Meanwhile, the bound of the estimation errors is obtained in the mean-square sense. The desired estimator parameters are presented in an analytical form dependent on the solution of a set of matrix inequalities. The developed scheme is related to the local information of the subsystems and thus satisfies the requirement of scalability. Finally, a simulation example of power systems is given to reveal the usefulness and effectiveness of the developed design scheme.

A fixed-time control strategy based on adaptive event-triggered communication and force estimators is proposed for a class of teleoperation systems with time-varying delays and limited bandwidth. Two force estimators are designed to estimate the operator force and environment force instead of force sensors. With the position, velocity, force estimate signals, and triggering error, an adaptive event-triggered scheme is designed, which automatically adjusts the triggering thresholds to reduce the access frequency of the communication network. With the state information transmitted at the moment of event triggering while considering the time-varying delays, a fixed-time sliding mode controller is designed to achieve the position and force tracking. The stability of the system and the convergence of tracking error within a fixed time are mathematically proved. Experimental results indicate that the control strategy can significantly reduce the information transmission, enhance the bandwidth utilization, and ensure the convergence of tracking error within a fixed time for teleoperation systems.

This paper deals with the stochastic stability problem of the closed-loop system under deception attacks based on an adaptive event-triggering scheme (AETS) with saturation constraint. Some novel stability criteria related to networked control systems under deception attacks are devised by taking the fixed threshold which is difficult to adapt to changeable systems into account. An adaptive event-triggering scheme with saturation constraint involving a threshold variable with system states is proposed to reduce network load, and the desired controller gain matrix is obtained by employing linear matrix inequalities (LMIs) technique. Moreover, the Lyapunov-Krasovskii method is used to obtain sufficient conditions for ensuring the stability of the system. In the end, the simulation results are shown to indicate the validity of the proposed method.

Islanded DC microgrids face challenges in voltage stability and communication overhead due to renewable energy variability. A novel enhanced distributed coordinated control framework, based on adaptive event-triggered mechanisms, is developed for the efficient management of multiple hybrid energy storage systems (HESSs) in islanded DC microgrids (MGs). We propose a hierarchical distributed control framework integrating ANN-based controllers and adaptive event-triggered mechanisms to dynamically regulate power flow and minimise communication. This system utilises a hierarchical coordinated control method (HCCM) with primary virtual resistance droop control integrated with state-of-charge (SoC) management and secondary control for voltage regulation and proportional current distribution through optimised communication networks. The integration of artificial neural network (ANN)-based controllers alongside traditional PI control leads to an improvement in system responsiveness. The control approach dynamically adjusts the trigger parameters to minimise communication overhead with tight voltage regulation. An extensive simulation using MATLAB/Simulink shows how the system can effectively manage variability in renewable energy sources and maintain stable voltage profiles with precise power distribution and minimal bus voltage fluctuations. Simulations confirm enhanced voltage regulation (±0.5% deviation), proportional current sharing (98% accuracy), and 60% communication reduction under load transients (outcomes).

This paper proposes a Takagi–Sugeno (T–S) fuzzy power system with reaction–diffusion and controller failure based on a non-fragile proportional integral (NFPI) control strategy with an adaptive event-triggering mechanism (AETM). Firstly, a unified fuzzy load frequency control (LFC) model is established by analysing the nonlinear characteristics and the influence of reaction–diffusion. Secondly, an AETM is proposed to reduce communication bandwidth utilization by introducing dynamic threshold parameters. In addition, a more applicative controller is proposed that considers both controller presence fluctuations and controller failure. Furthermore, we introduce a new free matrix which helps us to establish some less conservative sufficient conditions for stability asymptotic stability. Finally, numerical examples are provided to showcase the effectiveness of the controller design under the influence of reaction–diffusion and the AETM in saving communications resources.

UAV formation navigation in complex environments such as narrow tunnels faces multiple challenges, including obstacle avoidance, formation maintenance, and communication constraints. This paper proposes a cooperative obstacle avoidance strategy for UAV formation based on adaptive event-triggered impulse control, achieving efficient navigation under limited resources. The strategy comprises four key modules: an adaptive event-triggering mechanism, optical flow-based obstacle detection, leader–follower formation structure, and dynamic communication topology management. The adaptive event-triggering mechanism dynamically adjusts triggering thresholds, ensuring control accuracy while reducing control update frequency; the enhanced optical flow perception model improves obstacle recognition ability through a sector-based approach, incorporating tunnel-specific avoidance strategies; the leader–follower formation structure employs dynamic weight allocation to balance obstacle avoidance needs with formation maintenance; and communication topology optimization enhances system robustness under limited communication conditions. Simulation experiments were conducted in an arc-shaped tunnel environment with 15 randomly distributed obstacles, and the results demonstrate that the proposed method significantly improves collision rates, formation errors, and communication overhead compared to traditional methods. Lyapunov stability analysis proves the convergence of the proposed control strategy. This research provides new theoretical and practical references for multi-UAV cooperative control in complex narrow environments.

Inspired by the practical operability and safety of unmanned aerial vehicles (UAVs) in confined areas, this paper investigates adaptive trajectory tracking control problems in multiple six-rotor UAV systems with asymmetric time-varying output constraints and input saturation. Under model and disturbance uncertainties, six-rotor UAV systems are modeled as two non-strict-feedback systems, including attitude (inner-loop) and position (outer-loop) regulation systems. For the inner-loop design, the neural-based distributed adaptive attitude consensus control protocol is employed to realize the leader-follower consensus. Adaptive first-order sliding mode differentiators and an auxiliary dynamic system are introduced to address the “explosion of complexity” and saturation nonlinearity issues, respectively. Then, an event-triggered condition is predefined to alleviate the communication loads and reduce the number of messages to be transmitted from the controller to actuator. In addition, a class of asymmetric time-varying barrier Lyapunov functions are constructed for preventing the violation of time-varying output constraints. Accordingly, the proposed double-loop control strategies guarantee that all signals of UAV systems are semi-globally and uniformly bounded. Simulation results demonstrate that the proposed control method is effective.

This paper investigates the optimization problem of two-player zero-sum differential game with control constraints in the framework of event triggering. Relying on reinforcement learning, an adaptive dynamic programming algorithm is developed to approximate the optimal solution of zero-sum game, i.e., the saddle-point equilibrium. A single-network structure is adopted, wherein a critic neural network (NN) evaluates the action. First, the constrained Hamilton–Jacobi–Isaacs equation is mathematically derived in the presence of control constraints; the event-triggering mechanism is then incorporated to reduce calculations and actions. Then, based on the gradient-descent technique, a novel weight updating law is designed for the critic NN, which ensures the solution can converge to the optimal value online. Moreover, the stability of closed-loop system is guaranteed and the unfavorable Zeno behavior is excluded by calculating the theoretical minimum triggering interval. Finally, two numerical examples are provided to verify the reliability and effectiveness of proposed algorithm.

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.