Dynamic Event-triggered Strategy Research Articles

Consensus Control for Stochastic Multi-Agent Systems with Markovian Switching via Periodic Dynamic Event-Triggered Strategy

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.

Input-to-state stabilization of discrete-time delayed fuzzy systems via aperiodically intermittent control

This paper studies input-to-state stabilization of delayed discrete-time Takagi–Sugeno (T-S) fuzzy systems via aperiodically intermittent control. We first consider aperiodically intermittent time-triggered control, where we present sufficient conditions via the mathematical induction under the hypotheses of the quasiperiodicity condition. Based on the derived sufficient conditions, we apply a Lyapunov-Krasovskii (L-K) method together with the descriptor method to derive the explicit linear matrix inequalities (LMIs) that ensure the exponential stability and input-to-state stability (ISS), and show the existence of the aperiodically intermittent time-triggered controller that leads to efficient results with much less numerical complexity. We next consider aperiodically intermittent dynamic event-triggered control with an additional parameter that is larger than one. This strategy allows that the introduced dynamical variable does not remain constant but increases during the control rest interval. As a result, the proposed dynamic event-triggered strategy leads to a smaller number of sent signals than that for the case of the additional parameter which equals to one. Finally, numerical examples including a practical inverted pendulum on a cart are presented to verify the validity of the proposed method.

Distributed Two-Channel Dynamic Event-Triggered Adaptive Finite-Time Fault-Tolerant Containment Control for Multi-Leader UAV Formations

This paper addresses the distributed adaptive finite-time containment control problem in multi-leader UAV formations with actuator failures, limited communication, and external disturbances. A two-channel dynamic event-triggered strategy based on adaptive and sliding mode control is proposed as a containment control scheme, which solves the contradiction between the need to include containment error in the continuous term of the sliding mode surface in the adaptive law and the discontinuity of the communication between neighboring subsystems (follower UAVs) by constructing intermediate variables. Unlike the traditional event triggering condition that can only be applied to asymptotically convergent systems, the dynamic triggering condition designed in this paper, based on auxiliary variables, hyperbolic tangent function, and adaptive techniques, realizes finite-time convergence during the adjustment of the system and sets a more reasonable lower limit for the triggering thresholds, thus further reducing the communication frequency and ensuring the finite-time convergence of the system. The stability of the closed-loop system can be proved by the Lyapunov theorem. Zeno behavior can be avoided. The simulation results verify the effectiveness of the developed control algorithm.

Event-triggered secure control for Markov jump neural networks with time-varying delays and subject to cyber-attacks via state estimation fuzzy approach

The investigation uses a state estimation fuzzy technique to address the problem of dynamic event-triggered secure control in Markov jump neural networks with time-varying delays subject to cyber-attacks. Two different deception attacks are given, each employing stochastic variables with a Bernoulli distribution. Specifically, this article presents a dynamic event-triggered strategy and energy restrictions to reduce network traffic and protect network resources during deception attacks. To prevent the system state from becoming unavailable, a state estimator is concurrently developed using the output signal. The application of Lyapunov stability theory yields sufficient criteria that ensure neural network stability. The study also accurately describes the controller gain's by solving Linear matrix inequality. Numerical examples are provided to demonstrate the theoretical results.

Dynamic event-triggered adaptive neural control for MIMO nonlinear CPSs with time-varying parameters and deception attacks

This paper provides a dynamic event-triggered adaptive neural controller for the multi-input–multi-output (MIMO) non-strict feedback nonlinear cyber–physical systems (CPSs) with time-varying parameters encountering deception attacks (DAs). The single parameter learning method (SPLM) and the dynamic surface technology are combined under backstepping framework with adroitness, which not only resist the malicious sensor DA, but also reduce the complexity of the design scheme. Furthermore, a dynamic event-triggered strategy is added to update the threshold dynamically to further reduce the communication load. Theoretical analysis presents that the proposed control scheme can guarantee that all signals are bounded regardless encountering the DAs. Eventually, the validity of the developed methods is illustrated by a simulation case.

Event-triggered exponential stabilization of delayed systems on time scales

This paper addresses exponential stabilization issue for delayed systems on time scales. A dynamic event-triggered strategy (DETS) is proposed that relies on both the last sampled state and an exponential threshold, which can reduce the transmission frequency of the feedback control signal. Through inequality techniques and comparison method, a criterion of exponential stabilization for the underlying systems is presented. Besides, the measurement errors and estimation of system states are used to prevent Zeno behavior in DETS design. Two simulations are provided to illustrate the theoretical outcomes.

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.

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.

Event-triggered interval type-2 fuzzy control of networked systems with extended dissipative under Markovian switching

In this technical note, the problem of the extended dissipative control for interval type-2 fuzzy model with nonlinear network systems in the event-triggered framework is investigated. A novel dynamic event-triggered strategy is developed to ascertain the instants of activation. It has the advantage of reducing data transmission and optimising the utilisation of communication resources. On the basis of the proposed mechanism, the stochastic Lyapunov function including internal dynamic variables is constructed. Based on this function, coupled with the inequality scaling technique and the application of Dynkin's formula, we derive a sufficient condition for the system's stochastic asymptotic stability and extended dissipativity. The design of the dynamic event-triggered extended dissipative controller becomes challenging because the new sufficient condition is nonlinear for some matrix variables. However, utilising the Projection Lemma and the co-design approach, such a controller can be developed based on the obtained analysis result. The methodology is demonstrated through a rigorous numerical simulation case study.

Distributed Dynamic Event-Triggered Control to Leader-Following Consensus of Nonlinear Multi-Agent Systems with Directed Graphs.

This paper investigates achieving leader-following consensus in a class of multi-agent systems with nonlinear dynamics. Initially, it introduces a dynamic event-triggered strategy designed to effectively alleviate the strain on the system's communication resources. Subsequently, a distributed control strategy is proposed and implemented in the nonlinear leader-follower system using the dynamic event-triggered mechanism, aiming to ensure synchronization across all nodes at an exponential convergence speed. Thirdly, the research shows that under the dynamic event-triggered strategy the minimum event interval of any two consecutive triggers guarantees the elimination of Zeno behavior. Lastly, the validity of the calculation results is verified by a simulation example.

Stabilizability of multi-agent systems under event-triggered controllers

In view of the problems of large consumption of communication and computing resources in the control process, this paper studies a fundamental property for a class of multi-agent systems under event-triggered strategy: the S-stabilizability of a group of multi-agent systems with general linear dynamics under weakly connected directed topology. The results indicate that the S-stabilizability can be described in some way that the stabilizability region and feedback gain can evaluate the performance of the protocol. Firstly, a new distributed event-triggered protocol is proposed. Under this protocol, a kind of hybrid static and dynamic event-triggered strategy are presented, respectively. In particular, by using Lyapunov stability theory and graph partition tool, it is proved that the proposed event-triggered control strategy can guarantee the closed-loop system achieve S-stabilizability effectively, if at least one vertex in each iSCC (independent strongly connected component) cell receives information from the leader, which reflects the ability of distributed control law. Further, we demonstrate that the stabilizability can be realized if the initial system matrix A is Hurwitz. Moreover, it is confirmed that the designed static event-triggered condition is a limit case of dynamic event condition and can guarantee Zeno-free behavior. Finally, the validity of the theoretical results is proved by numerical simulation.

Dynamic Threshold Finite-Time Prescribed Performance Control for Nonlinear Systems With Dead-Zone Output.

This article investigates the tracking control problem for nonlinear systems. An adaptive model is proposed to represent the dead-zone phenomenon and solve its control challenge with a Nussbaum function in conjunction. Drawing inspiration from the existing prescribed performance control schemes, a novel dynamic threshold scheme is developed that fuses a proposed continuous function with a finite-time performance function. A dynamic event-triggered strategy is applied to reduce the redundant transmission. The proposed time-varying threshold control strategy has fewer updates than the traditional fixed threshold and improves the efficiency of resource utilization. A command filter backstepping approach is employed to prevent the complexity explosion faced by the computation. The suggested control strategy ensures that all system signals are bounded. The validity of the simulation results has been verified.

Output-Feedback Adaptive Control of a Class of Uncertain Nonlinear Systems With Dynamic Event-Triggered Strategy

Output-Feedback Adaptive Control of a Class of Uncertain Nonlinear Systems With Dynamic Event-Triggered Strategy

Distributed fixed-time event-triggered containment control for nonlinear multiagent systems with actuator faults

This paper is concerned with distributed fixed-time containment control for nonlinear multiagent systems with actuator faults via static and dynamic event-triggered strategies. A distributed fixed-time static event-triggered fault-tolerant controller is developed by employing the sign function, which eliminates the requirement for continuous control signal updates. To address the chattering phenomenon caused by the sign function, a distributed saturation controller is presented to enhance the control performance. To further reduce energy consumption and frequency of controller updates, a dynamic event-triggered strategy with an auxiliary parameter is proposed for each agent to dynamically regulate its threshold. The control scheme guarantees that the containment control objective is achieved within the settling time, and does not exhibit Zeno behavior. The effectiveness of the proposed control scheme is illustrated by simulation results.

Fuzzy Dynamic Event-Triggered Tracking Control for Semilinear Time-Delay Parabolic Systems

In this article, the fuzzy dynamic event-triggered tracking control problem of semilinear parabolic systems (SLPSs) with time-varying delay is investigated. Firstly, T-S fuzzy partial differential equation (PDE) models are introduced to describe the SLPSs. Secondly, a less conservative and more general fuzzy dynamic event-triggered strategy (DETS) is proposed to reduce communication consumption and avoid unnecessary continuous signal monitoring. Since the dynamic threshold is closely related to the currently sampled signal and the latest successfully transmitted signal, it can be promptly dynamically adjusted. In addition, on the basis of a reasonable assumption, a novel linear matrix inequality (LMI) relax technique is introduced to deal with the mismatched premise variables between the fuzzy systems and the fuzzy controller. By constructing the appropriate Lyapunov-Krasovskii candidate functional (LKCF), the criteria that the SLPSs can asymptotically track the target systems is derived, and the desired dynamic event-triggered control (DETC) gains can be obtained by solving a set of LMIs. The DETS reduces effectively communication resource consumption. Finally, the control problem of temperature distribution of catalytic rod in practical engineering application is given to verify the effectiveness and superiority of the proposed control scheme.

Distributed Optimization for Resource Allocation Problem with Dynamic Event-Triggered Strategy.

This study aims to unravel the resource allocation problem (RAP) by using a consensus-based distributed optimization algorithm under dynamic event-triggered (DET) strategies. Firstly, based on the multi-agent consensus approach, a novel one-to-all DET strategy is presented to solve the RAP. Secondly, the proposed one-to-all DET strategy is extended to a one-to-one DET strategy, where each agent transmits its state asynchronously to its neighbors. Furthermore, it is proven that the proposed two types of DET strategies do not have Zeno behavior. Finally, numerical simulations are provided to validate and illustrate the effectiveness of the theoretical results.

Event-triggered secure control of nonlinear multi-agent systems under sensor attacks

In this paper, a security consistent tracking control scheme with event-triggered strategy and sensor attacks is developed for a class of nonlinear multi-agent systems. For the sensor attacks on the system, a security measurement preselector and a state observer are introduced to combat the impact of the attacks and achieve secure state estimation. In addition, command filtering technology is introduced to overcome the “complexity explosion” caused by the use of the backstepping approach. Subsequently, a new dynamic event-triggered strategy is proposed, in which the triggering conditions are no longer constants but can be adjusted in real time according to the adaptive variables, so that the designed event-triggered mechanism has stronger online update ability. The measurement states are only transmitted through the network based on event-triggered conditions. The proposed adaptive backstepping algorithm not only ensures the security of the system under sensor attacks but also saves network resources and ensures the consistent tracking performance of multi-agent systems. The boundedness of all closed-loop signals is proved by Lyapunov stability analysis. Simulation examples show the effectiveness of the control scheme.

Asynchronous Control of Fuzzy Singularly Perturbed System With a Dynamic Event-Triggered Strategy

This paper is mainly concerned with the asynchronous control problem of fuzzy singularly perturbed systems (FSPSs) by designing an effective dynamic event-triggered control (ETC) strategy. Different from the existing results, a sample-based dynamic eventtriggering condition related to the singular perturbation parameter (SPP) " is proposed. Particularly, an auxiliary variable is employed in the eventtriggered mechanism (ETM), which changes with the fluctuation of the system state. By constructing a novel parameter-dependent Lyapunov functional, some sufficient conditions are derived to stabilize FSPSs and solve the "-dependent control gain. Furthermore, the upper bound of SPP _" related with the proposed control gain is determined. The Zeno behavior can be naturally avoided by applying the sample-based dynamic ETC. Finally, two examples including a nonlinear circuit are presented to validate the effectiveness of obtained results.

Event-based fault estimation and compensation for discrete-time systems via zonotopes

A fault estimation and compensation approach is presented for discrete-time systems with event-triggered scheme based on zonotope techniques in this paper. Instead of energy-bounded uncertainties, the dynamic system is supposed to be subjected to unknown but amplitude-bounded disturbance and noise, which is more in line with engineering practice. By introducing dynamic interval variables, a component-wise dynamic event-triggered strategy is utilized to reduce the consumption of communication resources. With considering the amplitude-bounded uncertainties and event-triggered scheme, a joint state-fault estimator is constructed to estimate the system state as well as possible system faults simultaneously, which are utilized to construct the fault-tolerant controller by compensating the effects of system faults. A co-design approach of the estimator, compensator, and event-triggered strategy is proposed to guarantee the stability and l1 performance of the system, based on which a zonotope-based interval estimation algorithm of system state and possible faults is provided to obtain the tight estimation of system state and faults. Eventually, simulation results on aircraft engine systems indicate that the designed method achieves a satisfactory fault estimation and fault tolerance performance, and also reduce the consumption of communication resources significantly.

Dynamic event-triggering sliding mode resilient control for multi-agent systems

The consensus problem for a multi-agent system (MAS) is investigated in this paper via a sliding mode control mechanism subject to stochastic DoS attack, which may occur on each transmission channel independently and randomly according to the Bernoulli distribution. A distributed dynamic event-triggered strategy is implemented on the communication path among agents, where dynamic parameters are introduced to adjust the threshold of event-triggered condition. After that, a distributed sliding mode controller is proposed for ensuring the stochastic consensus of the MAS. Meantime, a minimization problem is solved to obtain the correct controller gain matrix. At last, a numerical example is shown to demonstrate the presented results.