Dynamic Event-triggered Control Research Articles

Dynamic event-triggered asynchronous control for switched T-S fuzzy systems

The H∞ control problem for a class of continuous-time switched Takagi-Sugeno (T-S) fuzzy systems with mode-dependent average dwell time (MDADT) switching rules is the main topic of this article. Firstly, the transmission burden is relieved by efficiently reducing the number of signal transmissions by the application of a revolutionary dynamic event-triggered mechanism (ETM). Secondly, there is a thorough understanding of the dynamic event-triggered, asynchronous control behaviors that exist between the controllers and subsystems. To ensure H∞ performance of the asynchronous switched T-S fuzzy systems, certain appropriate adequate circumstances are derived by using the merged switching signal approach and Lyapunov-like functional (LKF) method. Lastly, a practical and an numerical example demonstrate the viability and implementation of the conclusions gained.

Dynamic Event-Triggered Control for GSES of Memristive Neural Networks Under Multiple Cyber-Attacks.

In this article, the dynamic event-triggered control problem of memristive neural networks (MNNs) under multiple cyber-attacks is considered. A novel dynamic event-triggering scheme (DETS) and the corresponding event-triggered controller are proposed by taking into consideration both denial-of-service and deception attacks (DoS-DAs). Then, a key lemma is established to show that the dynamic event-triggered controller can be used to solve the globally stochastically exponential stability (GSES) issue of concerned MNN under multiple cyber-attacks. Meanwhile, a novel Lyapunov functional is proposed based on the actual sampling pattern. It is shown that under our proposed dynamic event-triggered controller and Lyapunov functional, the concerned MNN can achieve GSES in the presence of DoS-DAs. In addition, our results include relevant results on event-triggered control of MNN with static event-triggering scheme (SETS) or without cyber-attacks as special cases. The effectiveness of the proposed event-triggered controller under multiple cyber-attacks is illustrated by a simulation example.

Adaptive secure output consensus tracking of linear multi-agent systems via dynamic event-triggered impulsive control under DoS attack

Adaptive secure output consensus tracking of linear multi-agent systems via dynamic event-triggered impulsive control under DoS attack

Dynamic Event-Triggered and Self-Triggered Fault-Tolerant Attitude Control for Multiple Spacecraft Systems With Uncertainties and Input Saturation

Dynamic Event-Triggered and Self-Triggered Fault-Tolerant Attitude Control for Multiple Spacecraft Systems With Uncertainties and Input Saturation

Quantization-Dependent Dynamic Event-Triggered Control for Networked Switched Systems Under Denial-of-Service Attacks

Quantization-Dependent Dynamic Event-Triggered Control for Networked Switched Systems Under Denial-of-Service Attacks

Observer-Based Dynamic Event-Triggered Control for CPSs Under Multiple Cyber-Attacks

Observer-Based Dynamic Event-Triggered Control for CPSs Under Multiple Cyber-Attacks

An Observer-Based Topology Identification and Synchronization in Finite Time for Fractional Singularly Perturbed Complex Networks via Dynamic Event-Triggered Control

This paper investigates the topology identification and synchronization in finite time for fractional singularly perturbed complex networks (FSPCNs). Firstly, a convergence principle is developed for continuously differential functions. Secondly, a dynamic event-triggered mechanism (DETM) is designed to achieve the network synchronization, and a topology observer is developed to identify the network topology. Thirdly, under the designed DETM, by constructing a Lyapunov functional and applying the inequality analysis technique, the topology identification and synchronization condition in finite time is established in the forms of the matrix inequality. In addition, it is proved that the Zeno behavior can be effectively excluded. Finally, the effectiveness of the main results is verified by an application example.

Dynamic event-triggered control for multi-channel cyber–physical systems under denial-of-service attacks

This paper studies the problem of dynamic event-triggered control in multi-channel cyber–physical systems subject to denial-of-service attacks, and the attacks are independent and asynchronous in different channels. A switching scheme is proposed to address the complexity of multi-channel network attacks, under which the sufficient conditions for closed-loop stability of the systems are obtained. Additionally, considering the different decay rates caused by the switching logic, an equivalent decay rates method, which reveals the relationship between the tolerable attack intensity and the dynamic event-triggering parameters, is proposed. Furthermore, it is demonstrated that the dynamic event-triggered strategy outperforms the traditional static approaches in terms of saving communication resources, which is also verified by a simulation example.

Dynamic event-triggered synchronization for semi-Markovian switching inertial neural networks with generally uncertain transition rates in finite-time interval

This paper investigates the finite-time synchronization for inertial neural networks with stochastic switching parameters based on dynamic event-triggered protocol. Due to the complexity of network environment, semi-Markovian process is introduced into the modeling of inertial neural networks, in which the transition rates vary with the operating time. The dynamic event-triggered protocol is developed to determine whether the signal is transmitted, in which Zeno phenomenon is eliminated under limited bandwidth resources. The objective is to construct an appropriate dynamic event-triggered control law such that the drive-response system maintains finite-time synchronization under generally uncertain transition rates. Based on the Lyapunov functional theory, finite-time synchronization criterion is proposed for the related inertial neural networks. Furthermore, a dynamic event-triggered controller is constructed in a finite-time interval. A numerical example and an image encryption process are given to show the efficiency of the proposed method.

Dynamic event-triggered control for CPSs under QoS-based variable sampling approach

In this article, a quality of service (QoS) dependent variable sampling dynamic event-triggered control method is designed for a cyber–physical system (CPS) with delays and packets dropout to cope with non-ideal network environments, maintain the desired control performance and improve the communication efficiency. To achieve the variable period sampling, a sampler is designed based on the QoS of the wireless network by using the delta operator discretization method. Then, a variable period sampling scheme for the delta operator system converted from the CPS is designed. Furthermore, a dynamic event-triggered mechanism (DETM) is proposed using the variable period sampling signal, which can reduce event triggered data calculations and increase event triggered intervals. By utilizing the average dwell time (ADT) approach, sufficient conditions contains the explicit variable sampling period are derived for the derived switched CPS. Finally, the effectiveness of the designed method is verified by numerical examples.

Dynamic event-triggered fixed-time consensus control for coupled multi-permanent magnet synchronous motors stochastic system with prescribed performance

With the ubiquity of permanent magnet synchronous motors (PMSM), multi-PMSMs are increasingly being elastically coupled to achieve greater output power. The authors establish a general mathematical model of coupled multi-PMSMs system. The dynamic analysis of the coupled multi-PMSMs system is analyzed using coupled three-PMSMs system as an example, and a dynamic event-triggered fixed-time consensus controller with prescribed performance is constructed for coupled PMSMs stochastic system. In addition, Interval Type-3 Fuzzy System (IT3FS) is employed to estimate unknown functions, the computing cost is reduced by updating the weights based on only one adaptive law per PMSM. To enhance the efficiency of communication resource utilization, a dynamic event-triggered mechanism is introduced, where the triggering condition depends on a dynamic auxiliary variable. Moreover, fixed-time control technical is applied to guarantee the setting time and a novel fixed-time prescribed performance function (PPF) is constructed to constrain consensus errors. To cope with the “complexity explosion”, a fixed-time tracking differentiator (TD) is introduced and the corresponding filtered error compensation signals is designed. Finally, the authors demonstrate the system is fixed-time stabilized in probability by applying fixed-time stabilization Lyapunov stability criterion, and the simulation results of a coupled three-PMSMs stochastic system show that the presented algorithm is feasible.

Integrating Dynamic Event-Triggered and Sensor-Tolerant Control: Application to USV-UAVs Cooperative Formation System for Maritime Parallel Search

Integrating Dynamic Event-Triggered and Sensor-Tolerant Control: Application to USV-UAVs Cooperative Formation System for Maritime Parallel Search

Co-design of distributed dynamic event-triggered scheme and extended dissipative consensus control for singular Markov jumping multi-agent systems under periodic Denial-of-Service jamming attacks

This paper introduces a distributed dynamic event-triggered (DDET) controller meticulously crafted to achieve resilient consensus control in singular Markov jumping multi-agent systems (SMJMAs) even in the face of periodic Denial-of-Service (DoS) jamming attacks. With a dual focus on optimizing network resource utilization and fortifying against cyber threats, we propose a distributed dynamic event-triggered scheme (DDETS) grounded in the principles of sampled data consensus. In the presence of cyber attacks, we demonstrate the transformation of the original consensus control challenge within the singular multi-agent system into a robust stability control problem, centering around a singular switching error system governed by the DoS attack signal. Acknowledging the disturbances that typify real-world scenarios, this paper establishes criteria for attaining exponential stability and stochastic admissibility within the singular Markov jumping switching error system under the extended dissipative performance index. Furthermore, we present a hybrid design for the consensus controller and DDETS, delineated through a set of solvable linear matrix inequalities (LMIs). Finally, a comprehensive array of numerical examples meticulously illustrates the compelling effectiveness and unequivocal superiority of our proposed methodology.

Fixed/Preassigned-Time Synchronization of Fuzzy Memristive Fully Quaternion-Valued Neural Networks Based on Event-Triggered Control

In this paper, the fixed-time and preassigned-time synchronization issues of fully quaternion-valued fuzzy memristive neural networks are studied based on the dynamic event-triggered control mechanism. Initially, the fuzzy rules are defined within the quaternion domain and the relevant properties are established through rigorous analysis. Subsequently, to conserve resources and enhance the efficiency of the controller, a kind of dynamic event-triggered control mechanism is introduced for the fuzzy memristive neural networks. Based on the non-separation analysis, fixed-time and preassigned-time synchronization criteria are presented and the Zeno phenomenon under the event-triggered mechanism is excluded successfully. Finally, the effectiveness of the theoretical results is verified through numerical simulations.

Dynamic event-triggered resilient network-level control for microgrids subject to FDI attacks

Dynamic event-triggered resilient network-level control for microgrids subject to FDI attacks

Observer-based bipartite consensus tracking of nonlinear fractional-order multi-agent systems with pull-based dynamic event-triggered mechanism

This paper investigates the observer-based bipartite consensus tracking of nonlinear fractional-order multi-agent systems (FOMASs) by employing a pull-based dynamic event-triggered mechanism (DETM). First, considering that the relevant state information of each agent is not always measurable, a class of distributed observers is considered for each agent to estimate its state information. Then, a pull-based DETM for FOMASs is proposed to avoid continuous controller updates, in which the dynamic threshold is modulated according to the preset conditions. The pull-based DETM constructed in this paper enables each agent to update the controller only based on its own trigger instants. Furthermore, an observer-based dynamic event-triggered control protocol is designed to guarantee the bipartite consensus tracking of FOMASs. Correspondingly, sufficient conditions are obtained by using graph theory and choosing suitable Lyapunov candidate functions. Moreover, the Zeno behavior is precluded. Finally, two simulation examples are presented to illustrate the theoretical results efficiently.

Robust H∞ consensus for uncertain linear multi-agent systems on directed topologies: A dynamic event-triggered strategy

Considering the asymmetry of the system matrix caused by directed communication networks, a dynamic event-triggered control protocol for the robust [Formula: see text] consensus of uncertain linear multi-agent systems accompanied by external disturbances on directed topologies is proposed. Through the introduction of the controlled output and generalized algebraic connectivity for directed graphs, the consensus problem is converted to the event-triggered stabilization of the uncertain linear closed-loop system with disturbance. On the basis of the relative state of neighbor agents at event-times and the introduced dynamic variables, a dynamic event-triggered control protocol is put forward, and the linear matrix inequality sufficient conditions for the system to achieve robust [Formula: see text] consensus are obtained after a rigorous proof. Then, linear matrix inequalities are further decoupled to greatly reduce the computational complexity, and the parameters of the control protocol are determined by solving the decoupled linear matrix inequalities. Finally, simulations are applied for the verification of conclusions.

Dynamic event-triggered control of large-scale nonlinear systems with sensor uncertainty

In this article, the dynamic event-triggered control problem is investigated for a class of large-scale nonlinear systems subject to sensor uncertainty. The unknown nonlinearities involved in each subsystem are assumed to be bounded by time-varying continuous functions multiplied by unknown constants and unmeasured states. To estimate the unmeasured states of each subsystem, a time-varying high-gain observer is constructed. Then, a dynamic event-triggered mechanism is proposed by introducing an internal dynamic variable in triggering function. Combined with the dual-domination approach, a dynamic event-triggered output feedback controller is developed for each subsystem. Subsequently, it is proved the convergence of all states is ensured based on the Lyapunov theory and the Zeno behavior can be avoided. Eventually, an example is presented to demonstrate the effectiveness of the proposed dynamic event-triggered control scheme.

Integral reinforcement learning-based dynamic event-triggered safety control for multiplayer Stackelberg–Nash games with time-varying state constraints

In this article, an integral reinforcement learning-based dynamic event-triggered safety control scheme is proposed to tackle the multiplayer Stackelberg–Nash games (MSNG) problem in continuous-time nonlinear systems with time-varying state constraints. Initially, a new barrier function (BF) is introduced by integrating traditional barrier functions with a novel smooth function to address the challenge of time-varying state constraints. Then, the constrained MSNG system is transformed into an unconstrained system through the application of the state transformation technique, which helps to characterize hierarchical decision problems as MSNG problems with the leader and followers. Meanwhile, the integral reinforcement learning (IRL) technique is also applied to ease the demand for precise system dynamics. Moreover, a new dynamic event-triggered control (DETC) mechanism is designed, resulting in coupled dynamic event-triggered Hamilton–Jacobi (HJ) equations. A single critic neural network (NN) is constructed to learn the optimal control laws for the leader and followers. By employing Lyapunov theory, the integral reinforcement learning-based dynamic event-triggered safety control method ensures that the system state and the critic NN weight errors are uniformly ultimately bounded (UUB). Finally, two simulation examples are provided to demonstrate the effectiveness of the proposed algorithm.

Dynamic event-triggered adaptive tracking control for stochastic nonlinear systems with deferred time-varying constraints

This article investigates the problem of adaptive tracking control for stochastic nonlinear systems with deferred state constraints. The novel unified universal barrier function (UUBF) and deferred funnel error transformation are developed to handle various state constraints and adjust the tracking error more precisely, respectively. Multi-dimensional Taylor network (MTN) and dynamic event-triggered mechanism (DETM) are employed to design controller in the backstepping process, which can achieve full-state constraints and the tracking control after the preassigned time while conserving more communication resources. Finally, two simulation examples are presented to verify the effectiveness of the proposed control scheme.