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This paper studies the quasisynchronization problems of reaction-diffusion neural networks (RDNNs) with time-varying delays via event-triggered control. Firstly, a static event-triggered mechanism and a dynamic event-triggered mechanism are designed to significantly reduce computation costs and save communication resources, respectively. These two different event-triggered control strategies are also able to meet the requirements of various situations. Based on the static event-triggered mechanism, the dynamic event-triggered mechanism is designed to further reduce the sampling frequency by introducing an internal dynamic variable, and several quasisynchronization criteria are derived. However, the quasisynchronization error bounds are related to triggering parameters and can be flexible adjusted, which reduces the conservatism of the existing quasisynchronization results and extends the application of proposed control strategies. Meanwhile, there exists positive lower bounds for the inter event time which can exclude the Zeno behavior. Finally, numerical simulations are given to demonstrate the superiority of the obtained theoretical results, and one example is given to show the chaotic quasisynchronization of the proposed RDNNs in the application of secure communication.

In this paper, we present a finite-time synchronization (FTS) for quantized Markovian-jump time-varying delayed neural networks (QMJTDNNs) via event-triggered control. The QMJTDNNs take into account the effects of quantization on the system dynamics and utilize a combination of FTS and event-triggered communication to mitigate the effects of communication delays, quantization error, and efficient synchronization. We analyze the FTS and convergence properties of the proposed method and provide simulation results to demonstrate its effectiveness in synchronizing a network of QMJTDNNs. We introduce a new method to achieve the FTS of a system that has input constraints. The method involves the development of the Lyapunov–Krasovskii functional approach (LKF), novel integral inequality techniques, and some sufficient conditions, all of which are expressed as linear matrix inequalities (LMIs). Furthermore, the study presents the design of an event-triggered controller gain for a larger sampling interval. The effectiveness of the proposed method is demonstrated through numerical examples.

The efficient utilization of computation and communication resources became a critical design issue in a wide range of networked systems due to the finite computation and processing capabilities of system components (e.g., sensor, controller) and shared network bandwidth. Event-triggered mechanisms (ETMs) are regarded as a major paradigm shift in resource-constrained applications compared to the classical time-triggered mechanisms, which allows a trade-off to be achieved between desired control/estimation performance and improved resource efficiency. In recent years, dynamic event-triggered mechanisms (DETMs) are emerging as a promising enabler to fulfill more resource-efficient and flexible design requirements. This paper provides a comprehensive review of the latest developments in dynamic event-triggered control and estimation for networked systems. Firstly, a unified event-triggered control and estimation framework is established, which empowers several fundamental issues associated with the construction and implementation of the desired ETM and controller/estimator to be systematically investigated. Secondly, the motivations of DETMs and their main features and benefits are outlined. Then, two typical classes of DETMs based on auxiliary dynamic variables (ADVs) and dynamic threshold parameters (DTPs) are elaborated. In addition, the main techniques of constructing ADVs and DTPs are classified, and their corresponding analysis and design methods are discussed. Furthermore, three application examples are provided to evaluate different ETMs and verify how and under what conditions DETMs are superior to their static and periodic counterparts. Finally, several challenging issues are envisioned to direct the future research.

This study focuses on stabilizing a bidirectional inductive wireless power transfer (WPT) system using an event‐triggered approach. It is only assumed the inductor currents on both the primary and pickup sides are measurable, and they are sent synchronously to the controller via a digital channel. To estimate the unmeasured states and maintain plant stability, a full‐order state observer and an observer‐based controller have been developed. The control parameters are optimized through a genetic algorithm to achieve the desired output response. An emulation methodology is then applied to create an output‐based event‐triggering condition. This condition ensures the stability of the closed‐loop system even in the presence of communication constraints. To prevent Zeno sampling, a minimal time interval between two transmissions is enforced using time‐regularization techniques. Furthermore, the performance of the event‐triggered controller is enhanced by solving a linear matrix inequality condition, which further reduces the number of transmission instances. The methodology offers a systematic and optimal design for the bidirectional inductive WPT system. It eliminates the need for manual tuning of control parameters, which is particularly beneficial given the system's complex nature. To address both continuous‐time and discrete‐time dynamics, the entire system is represented as a hybrid dynamical system, making it more intuitive for networked control systems. The efficiency of this approach is assessed through numerical simulations of a WPT system, demonstrating its effectiveness. The results show that the average intertransmission interval has been increased from 0.0799 to 0.1782 s, that is, the proposed event‐triggering strategy reduced the number of transmissions to more than 50% compared with conventional periodic sampling. Schematic equivalent circuit of the wireless power transfer system.

This paper investigates the adaptive event-triggered control problem for a class of nonlinear systems subject to periodic disturbances. To reduce the communication burden, a reliable relative threshold strategy is proposed. Fourier series expansion and radial basis function neural network are combined into a function approximator to model suitable time-varying disturbed function of known periods in strict-feedback systems. By combining the Lyapunov stability theory and the backstepping technique, the proposed adaptive control approach ensures that all the signals in the closed-loop system are bounded, and the tracking error can be regulated to a compact set around zero in finite time. Finally, simulation results are presented to verify the effectiveness of the theoretical results.

In this paper, a robust composite dynamic event-triggered formation control scheme is proposed for multiple underactuated surface vehicles (USVs) from two aspects, i.e., guidance and control. In the guidance module, a novel dual-layer line-of-sight (DLLOS) guidance principle is incorporated into the leader–follower framework to generate the reference path. To overcome the problem of unavailable leader velocity information, an adaptive speed controller is designed to adjust the navigational speed of followers. As for the control part, by utilizing the dynamic event-triggered method, the operational frequency of actuators can be reduced in a flexible manner. That can effectively avoid the excessive wear and chattering phenomenon of actuators. Furthermore, by the fusing of the radial basis function neural networks (RBF NNs) and the robust neural damping technique, the model uncertainty, environmental disturbances and some unknown parameters can be remodeled, and only two gain-related adaptive laws need to be updated online. The serial–parallel estimation model (SPEM) is established to predict the velocity variables, and the approximation performance of NNs can be enhanced by virtue of the derived prediction error. Through the Lyapunov stable theorem, all control signals in the closed-loop system are guaranteed semi-globally uniformly ultimately bounded (SGUUB) stability. Finally, digital simulations are illustrated to verify the effectiveness and superiority of the proposed algorithm.

This paper designs two novel event-triggered control (ETC) schemes based on the critic learning technique for constrained discrete-time nonlinear systems. First, starting from the stability of the constrained system, a static ETC method is developed to reduce the computational burden. Then, a nonnegative dynamic variable is introduced into the static event-triggered mechanism, so as to establish the dynamic ETC method, which further improves the resource utilization rate and possesses the anti-interference ability. Moreover, a speedy value iteration architecture is designed to obtain an initially admissible optimal control policy, which can ensure the normal execution of the designed ETC methods. Finally, two experimental examples are provided to illustrate the effectiveness and superiority of the developed schemes.

This paper investigates the problem of attitude synchronization tracking of multiple spacecraft in the presence of limited inter-spacecraft communication, model uncertainties and external disturbances. A distributed adaptive event-triggered control scheme for attitude synchronization tracking of multiple spacecraft is proposed. In the proposed control scheme, the controllers are updated in an aperiodic manner at the event-sampled instants when a defined event-triggered error exceeds a state-dependent threshold. The inter-spacecraft communication topology in the control scheme is assumed to be undirected. The stability of the resulting closed-loop systems can be guaranteed by application of the Lyapunov function, and no accumulation of triggering instants is also ensured. Finally, simulation results are given to illustrate the effectiveness of the proposed control scheme.

This paper investigates the event-triggered control problem of networked switched systems with unstabilizable subsystems subject to mode-dependent denial-of-service (MDDoS) attacks. Unlike previous results that consider the tolerance of the entire switched systems to DoS attacks, a more general MDDoS attack is defined to limit DoS attacks of each subsystem by a mode-dependent form and mode-dependent attack duty cycle (MDADC) is proposed to exploit the tolerance of each subsystem to DoS attacks. More important, by proposed MDDoS, the tolerance of switched systems to DoS attacks is improved. First, a mode-dependent event-triggered mechanism is designed, in which the upper bound of triggered intervals of each subsystem is mode-dependent. Multiple switchings are allowed to occur within one triggered interval and the DoS attack active interval, which results in the asynchronous switchings between the controllers and the subsystems, as well as the trigger parameters and the subsystems. Secondly, MDADC is derived to ensure the global exponential stability of switched systems, which reveals the trade-off among the MDDoS attacks, unstabilizable subsystems, maximum event-triggered interval and the performance of each subsystem. Moreover, Zeno behavior is eliminated by calculating the minimum triggered interval. Finally, a numerical example and a practical example are applied to verify the effectiveness of the proposed method.

This study focuses on a novel event-triggered boundary control design of an uncertain flexible manipulator subject to input constraints and external disturbances. To this end, a novel boundary control law is developed based on the Lyapunov stability theory to suppress elastic deflection and regulate the manipulator to track the desired angular position. Moreover, to approximate the unknown functions and compensate for the effect of input constraints, the neural network technique is adopted with an arbitrarily adjustable approximating error. Simultaneously, the event-triggering mechanism is incorporated with a controller design to alleviate the communication load and the execution rate of the actuator. Lastly, numerical simulations are performed to demonstrate the effectiveness of the derived scheme.