Dynamic Event-triggered Communication Research Articles

Distributed Estimator-Based Event-Triggered Neuro-Adaptive Control for Leader-Follower Consensus of Strict-Feedback Nonlinear Multiagent Systems.

This article investigates the leader-follower consensus problem for strict-feedback nonlinear multiagent systems under a dual-terminal event-triggered mechanism. Compared with the existing event-triggered recursive consensus control design, the primary contribution of this article is the development of a distributed estimator-based event-triggered neuro-adaptive consensus control methodology. In particular, by introducing a dynamic event-triggered communication mechanism without continuous monitoring neighbors' information, a novel distributed event-triggered estimator in chain form is constructed to provide the leader's information to the followers. Subsequently, the distributed estimator is utilized to consensus control via backstepping design. To further decrease information transmission, a neuro-adaptive control and an event-triggered mechanism setting on the control channel are codesigned via the function approximate approach. A theoretical analysis shows that all the closed-loop signals are bounded under the developed control methodology, and the estimation of the tracking error asymptotically converges to zero, i.e., the leader-follower consensus is guaranteed. Finally, simulation studies and comparisons are conducted to verify the effectiveness of the proposed control method.

Adaptive fixed-time containment control of MIMO nonlinear multiagent systems via dynamic event-triggered communication

Adaptive fixed-time containment control of MIMO nonlinear multiagent systems via dynamic event-triggered communication

Distributed optimization via dynamic event-triggered scheme with metric subregularity condition

In this paper, we present a continuous-time algorithm with a dynamic event-triggered communication (DETC) mechanism for solving a class of distributed convex optimization problems that satisfy a metric subregularity condition. The proposed algorithm addresses the challenge of limited bandwidth in multi-agent systems by utilizing a continuous-time optimization approach with DETC. Furthermore, we prove that the distributed event-triggered algorithm converges exponentially to the optimal set, even without strong convexity conditions. Finally, we provide a comparison example to demonstrate the efficiency of our algorithm in communication resource-saving.

Dynamic Event-Triggered-Based Adaptive Finite-Time Neural Control for Active Suspension Systems With Displacement Constraint.

In this article, we give an event-based control algorithm for nonlinear active suspension systems (ASSs) with the vertical displacement constraint. For in-vehicle communication networks, the controller area network (CAN) is a widely used standard for interconnecting electronic control units (ECUs). The main task in this work is, thus, to reduce the communication burden on the CAN. To this end, we develop a dynamic event-triggered communication mechanism in ASSs. Meanwhile, in practice, the vehicle safety is degraded when the body vibration exceeds the allowable maximum. Thus, the vertical displacement of ASSs should be constrained within a reliable range. For this purpose, we present a novel finite-time integral barrier Lyapunov function (FTIBLF), which not only guarantees that the vertical displacement constraint bounds are not violated, but also enables the position of the suspension to be stabilized in the neighborhood of a desired position in finite settling time. Furthermore, neural networks (NNs) are utilized to identify the unknown nonlinear characteristics in ASSs subjected to the modeling error and unknown mass. As a result, we propose an adaptive neural control technique based on the dynamic event-triggered condition, which helps to improve the ride comfort while ensuring the driving safety and handling stability. Finally, simulation results are provided to verify the validity of the presented methodology.

Communication Resource-Efficient Vehicle Platooning Control With Various Spacing Policies

Platooning represents one of the key features that connected automated vehicles may possess as it allows multiple automated vehicles to be maneuvered cooperatively with small headways on roads. However, a critical challenge in accomplishing automated vehicle platoons is to deal with the effects of intermittent and sporadic vehicle-to-vehicle data transmissions caused by limited wireless communication resources. This paper addresses the co-design problem of dynamic event-triggered communication scheduling and cooperative adaptive cruise control for a convoy of automated vehicles with diverse spacing policies. The central aim is to achieve automated vehicle platooning under various gap references with desired platoon stability and spacing performance requirements, while simultaneously improving communication efficiency. Toward this aim, a dynamic event-triggered scheduling mechanism is developed such that the inter-vehicle data transmissions are scheduled dynamically and efficiently over time. Then, a tractable co-design criterion on the existence of both the admissible event-driven cooperative adaptive cruise control law and the desired scheduling mechanism is derived. Finally, comparative simulation results are presented to substantiate the effectiveness and merits of the obtained results.

Adaptive Fuzzy Formation Control for Underactuated Multi-USVs With Dynamic Event-Triggered Communication

Adaptive Fuzzy Formation Control for Underactuated Multi-USVs With Dynamic Event-Triggered Communication

Distributed Joint Fault Estimation for Multi-Agent Systems via Dynamic Event-Triggered Communication

Distributed Joint Fault Estimation for Multi-Agent Systems via Dynamic Event-Triggered Communication

Bounding Uncertainty in State Estimation Under Dynamic Event-Triggered Communication

Bounding Uncertainty in State Estimation Under Dynamic Event-Triggered Communication

Adaptive formation control of nonlinear multi-agent systems with dynamic event-triggered communication

Adaptive formation control of nonlinear multi-agent systems with dynamic event-triggered communication

Dynamic Event-Triggered Consensus for Linear Multi-Agent Systems With Hybrid Intelligence

In this brief, consensus control is investigated for general linear multi-agent systems (MASs) over undirected communication topologies. Considering that the agents in MASs may have nonidentical intelligence characteristics, the concept of intelligent level is introduced to characterize the intelligence characteristics of agents. A novel dynamic event-triggered communication mechanism is developed based on periodic sampled data and the intelligent levels of agents, in which the threshold parameter of the event-triggered condition updates periodically in accordance with a prescribed dynamic law. By making use of a delayed system method and Lyapunov stability theory, some sufficient conditions are derived to guarantee the desired consensus under a novel delicately designed control protocol. Finally, the effectiveness of the proposed approach is validated by a simulation example.

Resilient Event-/Self-Triggering Leader-Following Consensus Control of Multiagent Systems Against DoS Attacks

This paper focuses on the problem of resilient leader-following consensus for multi-agent systems (MAS) against denial-of-service (DoS) attacks. Two update strategies based on event/self-triggering are proposed for the control protocols to handle the leader-following consensus in unreliable shared networks. We first propose a dynamic event-triggered communication (DETC) scheme to mitigate unnecessary information transfer through energy-limited and vulnerable networks. Continuous communication between agents can be avoided. DoS attacks are supposed to be aperiodic and asynchronous at different edges. The concept of valid DoS attack interval is introduced, and the frequency and duration of attacks are analyzed. The leader-following consensus can be achieved when there exist DoS attacks using the dynamic event-based control strategy. Then to avoid continuous event detection and save computation resources, a self-triggered communication (STC) function is developed. The next triggering moment is predetermined with the latest received state information. And the Zeno behavior is eliminated for the feasibility of the event/self-triggering scheme. Finally, a simulation is provided to verify the effectiveness of the proposed update strategies and control protocols.

Event-triggered group consensus for one-sided Lipschitz multi-agent systems with input saturation

This paper investigates event-triggered group consensus problem for multi-agent systems with input saturation under fixed topology and Markovian switching topologies respectively. The agent dynamics is described by one-sided Lipschitz nonlinear function, which covers a broad family of nonlinear systems and includes the well-known Lipschitz system as a special case. A novel distributed dynamic event-triggered communication scheme is proposed based on the local stochastic sampling information among the agents, which includes some existing schemes as special cases. A new stochastic sampled-data dependent error system is obtained by model transformation, based on the discontinuous Lyapunov stability theory, some local group consensus criteria in mean square can be derived for fixed topology and Markovian switching topologies, respectively. Moreover, an optimization algorithm is introduced to estimate the region of initial conditions. Finally, two specific numerical examples are employed to illustrate the effectiveness of the theoretical results.

Nonrecursive Control for Formation-Containment of HFV Swarms With Dynamic Event-Triggered Communication

This article proposes an output-feedback control protocol for hypersonic flight vehicle (HFV) swarms considering dynamic event-triggered communication. The peculiarities of the proposed method over existing ones consist in the following: 1) While carrying out scheduled maneuvers, the outputs of follower HFVs converge inside the convex hull spanned by leader HFVs whose task is to maintain a geometric space configuration; 2) a simple nonrecursive output-feedback design is established without involving any intermediate control laws or requiring full-state information; 3) an error-dependent monotonically decreasing exponential term is incorporated into the dynamic event-triggered threshold to reduce the communication bandwidth while preserving the desired track performance and excluding Zeno behavior. Comparative simulation results validate the effectiveness of the proposed methodology.

Novel Extended State Observer Design for Uncertain Nonlinear Systems via Refined Dynamic Event-Triggered Communication Protocol.

In this article, an extended state observer (ESO) design problem is investigated for uncertain nonlinear systems subject to limited network bandwidth. First, for rational information exchange scheduling, a dynamic event-triggered (DET) communication protocol is proposed. Different from the traditional static event-triggered strategies with fixed thresholds, an internal dynamic variable is introduced to be adaptively adjusted by a dual-directional regulating mechanism. Thus, more desirable tradeoff between observation performance and communication resource efficiency is achieved. Second, inspired by our early work on Takagi-Sugeno fuzzy ESO (TSFESO), a novel paradigm of event-triggered TSFESO is initially proposed. Third, under the DET mechanism, the TSFESO design approach is derived to carry out exponential convergence for estimation error dynamics. Finally, the effectiveness of the proposed method is verified by numerical examples. The nonlinear estimating efficiency and linear numerical tractability are integrated in TSFESO. In addition, a generalized ESO formulation is developed to allow some nonadditive uncertainties incompatible with total disturbance, such as improved event-triggered strategy, and thus, the application sphere of ESO is further expanded.

Distributed Filtering With Dynamic Event-Triggered Communication for Networked Periodic Systems

This article deals with the problem of distributed filtering of periodic systems over sensor networks under event-triggered communications, based on a piecewise impulsive method. The continuous-time periodic system is approximated by several linear time-invariant subsystems through a piecewise constant method. The sensor network deployed to the periodic system is taken into account for the actual needs of monitoring or estimation. A dynamic event-triggering mechanism is designed to reduce the unnecessary data transmission over the wireless network from the sensor to the filter as well as between each filter. Distributed peak-to-peak filters with periodic time-varying parameters are designed under the event-triggering mechanism to estimate the networked continuous-time periodic systems with the bounded and local differentiable disturbance. Then, a periodic piecewise filtering error system is modeled as an impulsive periodic piecewise system, for which sufficient conditions of a guaranteed peak-to-peak performance are proposed. The gains of the filters are obtained by the method of convex linearization. The validity of the proposed results is demonstrated by a pendulum tracking system.

Dynamic event-triggered consensus for discrete-time hidden Markov multi-agent systems with partially unknown probabilities

This paper addresses the consensus problem of Markov jump multi-agent systems under dynamic event-triggered communication. In order to utilize the limited network resources reasonably and improve the efficiency of data transmission, a dynamic event-triggered method is adopted. Considering the challenge of obtaining system mode information, a hidden Markov model is introduced. On this basis, because of the limitation of mode information acquisition, the case with partially unknown probabilities both exist in the transition probability matrix and the observation probability matrix is discussed, which makes the conclusion realistic. Moreover, a sampled-data consensus protocol is proposed, and based on the Lyapunov stability theory, several sufficient conditions are derived to ensure the consensus of the system under specified [Formula: see text] performance. Finally, a numerical example is given to demonstrate the effectiveness of the proposed protocol.

Distributed dynamic event-triggered communication and control for multi-agent consensus: A hybrid system approach

This paper investigates the output-based event-triggered communication and control for linear multi-agent consensus under a directed graph based on a co-design method. The communication among the agents, as well as the controller updates, are determined by some new event-triggering mechanisms, in order to reduce the use of the network resources. To simultaneously guarantee the significant properties including the asymptotic consensus, and strong Zeno-freeness (strictly positive inter-event times), a novel distributed dynamic event-triggered protocol is proposed. Unlike the most-existing emulation-based approaches in which the control gain is previously decided, a systematic co-design procedure is proposed to design the controller gain, the observer gain, and the event-triggering mechanisms altogether in terms of solving a linear matrix inequality optimization problem. Based on the resulting hybrid system framework, a hybrid model is established for the distributed closed-loop system and the asymptotic consensus is achieved. Finally, a numerical example is presented to verify our systematic design methodology.

A Dynamic Event-Triggered Approach to Recursive Nonfragile Filtering for Complex Networks With Sensor Saturations and Switching Topologies.

In this article, the nonfragile filtering issue is addressed for complex networks (CNs) with switching topologies, sensor saturations, and dynamic event-triggered communication protocol (DECP). Random variables obeying the Bernoulli distribution are utilized in characterizing the phenomena of switching topologies and stochastic gain variations. By introducing an auxiliary offset variable in the event-triggered condition, the DECP is adopted to reduce transmission frequency. The goal of this article is to develop a nonfragile filter framework for the considered CNs such that the upper bounds on the filtering error covariances are ensured. By the virtue of mathematical induction, gain parameters are explicitly derived via minimizing such upper bounds. Moreover, a new method of analyzing the boundedness of a given positive-definite matrix is presented to overcome the challenges resulting from the coupled interconnected nodes, and sufficient conditions are established to guarantee the mean-square boundedness of filtering errors. Finally, simulations are given to prove the usefulness of our developed filtering algorithm.

Adaptive dynamic event-triggered cluster synchronization in an array of coupled neural networks subject to cyber-attacks

This paper is concerned with cluster synchronization in an array of coupled neural networks subject to cyber-attacks, where a random variable is employed to reflect the success ration of the launched cyber-attacks. To save the energy consumption and alleviate the transmission load of the network, a novel adaptive distributed dynamic event-triggered communication scheme is presented based on the local stochastic the state sampling information, compared with some existing results, the different event-triggered matrices and auxiliary variables are introduced for each node to adjust its threshold dynamically, which can further reduce the sampled-data transmission, the proposed event-triggered scheme includes some existing event-triggered schemes as special cases. The cluster synchronization controllers are designed based on the states of neighboring nodes at event-triggered instants, a new stochastic sampled-data dependent error model is constructed, some mean-square cluster synchronization criteria can be derived by the Lyapunov stability theory and algebraic graph theory, and the feedback matrices and event-triggered matrices can be obtained by solving some linear matrix inequalities. Finally, two numerical examples are employed to show the validity and advantage of the theoretical results.

Adaptive fault-tolerant control of heterogeneous MASs based on dynamic event-triggered communication

This paper describes a solution to the problem of fault-tolerant control (FTC) problem for leader-following heterogeneous linear multi-agent systems (MASs) subject to loss of effectiveness and time-varying additive actuator faults. The main goal of the proposed FTC approach is to maintain the behaviour of the heterogeneous MASs in spite of actuator faults and to save on bandwidth required for the MASs communication by using a dynamic event-triggered communication mechanism. Firstly, a fault estimation observer, which deals with linear systems with matched disturbances, is constructed to estimate simultaneously the actuator faults and the followers' states. Then, an FTC with dynamic event-triggered communication is proposed. The latter is based on a dynamic internal reference for each agent and exploits the estimated faults. Thus, it is at first shown that the FTC problem can be indirectly solved through the consensus tracking of the defined internal references. Then, a sufficient condition of consensus tracking for the internal references with a dynamic event-triggered communication is proposed. Finally, a sufficient and necessary condition is derived for the FTC of linear heterogeneous multi-agent systems. The effectiveness of the proposed approach is illustrated with a numerical simulation.