Event-triggered Communication Mechanism Research Articles

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.

Output Feedback-Based Consensus for Nonlinear Multiagent Systems: The Event-Triggered Communication Strategy.

The current investigation explores the leader-following consensus problem for nonlinear multiagent systems under the output feedback control mechanism and the event-triggered communication mechanism. Owing to the physical instrument constraints, a significant portion of the state variables is not readily available. Therefore, this article put forward a distributed event-based leader-following consensus protocol only using agents' relative output measurements and underlying neighbors. Furthermore, this article develops two event-triggered mechanisms simultaneously, one is the event-triggered communication mechanism in the sensor-to-controller channel, and another is the event-triggered controller update in the controller-to-actuator track. Besides that, it is proven that the developed event-triggered control protocol can settle the leader-following consensus problem of the nonlinear multiagent systems, and the Zeno behavior is excluded in both the channels. Finally, we perform two simulation examples to illustrate the efficacy of the obtained results.

Event-Triggered Federated Learning for Fault Diagnosis of Offshore Wind Turbines With Decentralized Data

Rapid developments of offshore wind industry offer a strong demand opportunity for offshore wind turbine remote diagnosis. As offshore wind turbines are often located in harsh and communication-constrained environments, the collection and transmission of data is severely restricted, which poses a serious challenge to the conventional centralized diagnostic paradigm that relies on data aggregation. To address this challenge, we propose a novel event-triggered federated learning framework for decentralized fault diagnosis of offshore wind turbines. Specifically, federated learning is first employed to learn decentralized local knowledge from geographically distributed offshore wind turbines, so that the communication objects are transformed from massive raw data into learned parameters, thereby relieving the communication burden. Then, we design an event-triggered communication mechanism and incorporate it into federated learning, the core of which is to modify the communication requirement from uploading all trained parameters periodically to communicating only when necessary. The proposed framework is verified by a real-world offshore wind turbine dataset from six large wind farms in China. An ablation study shows that the proposed framework can maintain high diagnostic performance while reducing communication costs. A comprehensive comparison based on three benchmark models demonstrates that the proposed framework can reduce the communication burden by up to 63% while obtaining better diagnostic performance. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —This study was motivated by the problem of collaborative diagnosis of distributed offshore wind turbines under the constraints of data privacy and communication overhead. The method employs a federated learning-based fault diagnosis framework, which permits to obtain global fault diagnosis knowledge without aggregating raw data scattered in each end device, thus avoids the risk of data leakage. Moreover, a strategy integrating parameter variation and accuracy gain is designed to avoid communication redundancy for collaborative training. The practicability and superiority of our proposed framework is demonstrated using extensive experiments against actual industrial data collected from six offshore wind farms.

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.

Asynchronous H∞ output tracking control for uncertain networked switched systems with mode-dependent average dwell time: an event-driven mechanism

This paper considers the problem of event-triggered H ∞ output tracking control for uncertain networked switched systems with both communication constraints and asynchronous switching. A new sampled delay-dependent error tracking uncertain networked switched system is constructed on the basis of event-triggered communication mechanism (ETCM) and the given reference model. By introducing a parameter-dependent Lyapunov–Krasovskii functional and employing the mode-dependent average dwell time (MDADT) method, results on exponential stability and robust H ∞ performance are proposed. Furthermore, the gains of the output tracking controller with matched system mode are obtained by solving a set of linear matrix inequalities (LIMs), at the same time, the parallel results with the mismatched case are also obtained. Simulation results show that the proposed design scheme not only guarantees tracking performance but also saves communication resources effectively.

Event-Triggered Adaptive Tracking With Guaranteed Transient Performance for Switched Nonlinear Systems Under Asynchronous Switching.

This article presents an event-triggered neural-network (NN) tracking control scheme, capable of ensuring transient performance for switched nonlinear systems. A mode-dependent event-triggered communication mechanism (MDETCM) is designed, and this significantly saves communication resources without limiting the number of switches between two consecutive triggering instants. Meanwhile, to solve the impact of asynchronous switching on system performance, the information of the switching signal is considered into the event-triggered mechanism (ETM). Also, by introducing a normalized function transformation and a tan-type barrier function, the transient performance is regarded as a tracking error constraint without considering the initial conditions of system output and reference signal required in traditional prescribed performance bound (PPB) control. At the same time, the improved admissible edge-dependent average dwell time (AED-ADT) method is cleverly connected with adaptive backstepping control, and a state-feedback tracking algorithm is proposed, under which all closed-loop signals are bounded and the transient performance of the controlled plant is ensured. Finally, the superiority of the proposed scheme is demonstrated through numerical studies, and the control scheme is available for a single-link robot.

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.

Distributed Moving-Horizon Estimation With Event-Triggered Communication Over Sensor Networks

This paper is concerned with the distributed moving-horizon estimation problem for linear discrete-time systems over wireless sensor networks. To reduce the communication burden, an event-triggered communication mechanism is proposed to govern the transmission of one-step predictions and measurements in each node. Under such a mechanism, a novel event-triggered distributed moving-horizon estimator is designed to deal with the state estimation problem subject to noise and state constraints. Each node computes its local state estimate by minimizing a cost function, which includes the event-triggered one-step predictions and measurements from the neighbors. It is proved that the developed distributed estimator is stable with the uniformly bounded estimation error in each node, provided by network connectivity, regional observability and small enough triggering parameters. A numerical simulation is given to verify the effectiveness of the proposed estimation algorithm.

Distributed online optimization for heterogeneous linear multi-agent systems with coupled constraints

This paper studies a class of distributed online convex optimization problems for heterogeneous linear multi-agent systems. Agents in a network, knowing only their own outputs, need to minimize the time-varying costs through neighboring interaction subject to time-varying coupled inequality constraints. Based on the saddle-point technique, we design a continuous-time distributed controller which is shown to achieve constant regret bound and sublinear fit bound, matching those of the standard centralized online method. We further extend the control law to the event-triggered communication mechanism and show that the constant regret bound and sublinear fit bound are still achieved while reducing the communication frequency. Additionally, we study the situation of communication noise, i.e., the agent’s measurement of the relative states of its neighbors is disturbed by a noise. It is shown that, if the noise is not excessive, the regret and fit bounds are unaffected, which indicates the controller’s noise-tolerance capability to some extent. Finally, a numerical simulation is provided to support the theoretical conclusions.

Distributed Formation Control for a Multirobotic Fish System With Model-Based Event-Triggered Communication Mechanism

This paper confronts the formation control problem for a multi-robotic fish system with event-triggered communication mechanism. A three-dimensional (3-D) distributed formation control framework is proposed to drive the robotic fish agents to an anticipated configuration aligning with a moving target. In particular, a consensus-based formation control law is intended to realize the two-stage formation control process. Taking the energy-constrained occasions into consideration, the communication structure and event-triggered protocols are initially tailored. Meanwhile, the Lyapunov function is employed and the globally asymptotic stability of the proposed method is fully demonstrated. Afterwards, making use of the local measurements of triggering times, the unscented Kalman filter (UKF) is introduced and a novel model-based event-triggered mechanism is put forward to further mitigate otiose communication consumption. Finally, adequate simulations and experiments are carried out to verify the effectiveness and robustness of the proposed scheme. Thereby, the proposed formation control frame offers great potential for future practical marine operations of the underwater multi-agent systems.

Network-Induced Asynchronous Fuzzy Control for Vehicle Steering Using Switching Event-Triggered Communication Mechanism

Network-Induced Asynchronous Fuzzy Control for Vehicle Steering Using Switching Event-Triggered Communication Mechanism

Event-triggered distributed Nash equilibrium seeking over directed graphs and its application to power management

In this paper, the problem of distributed discrete-time Nash equilibrium computation with event-triggered communication over directed graphs is studied. Each agent can only receive strategies from its neighbors through a directed and unbalanced graph, while its cost function may rely on the information of all agents. To this end, each agent needs to estimate the strategy profile. Meanwhile, an event-triggered communication mechanism is introduced, which can effectively compensate for network resource loss. Concretely, specific moments that each agent transmits its information are decided by the pre-designed event-triggered condition. In this setting, two discrete-time Nash equilibrium seeking algorithms with event-triggered communication are designed, respectively considering that the Perron-Frobenius eigenvector of the adjacency matrix associated the directed communication graph is known and unknown in advance. It is rigorously proved that both algorithms can linearly converge to the Nash equilibrium with a small error. Finally, a power management example in smart grids is presented to verify the performance of proposed algorithms.

Event-Triggered Distributed State Estimation: A Conditional Expectation Method

This paper mainly focuses on distributed filtering for a discrete time-varying system observed by a sensor network, where each sensor can measure some partial state information of the system and communicate with its neighbours. A novel distributed event-triggered communication mechanism is designed to reduce the communication rate among the sensors and guarantee the performance of the filter. With a data scheduler, the sensor is able to decide whether to transmit data to its neighbours. By applying Gaussian approximation, an evaluation of the effect caused by the non-transmission event is derived, which characterizes the tradeoff between communication rate and state estimation performance. Subsequently, a corresponding sub-optimal filtering gain design protocol is proposed. Compared with the literature, the filtering algorithm proposed in this paper is less conservative. Finally, numerical simulation is provided to illustrate the improvement of performance and the robustness of the approximation.

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.

Distributed event-triggered unadjusted Langevin algorithm for Bayesian learning

This paper presents a distributed event-triggered unadjusted Langevin algorithm (DETULA) to address the Bayesian learning problem. We consider a set of networked learning agents who have access to their own independently distributed data sets. The objective of each agent is to reconstruct the global posterior of the unknown model parameters through local learning along with interaction with neighboring agents. We propose an event-triggered communication mechanism for a distributed Langevin algorithm to limit the inter-agent interactions and thus reduce the communication overhead. We provide conditions on the algorithm step sizes and the triggering threshold to ensure mean-square consensus of the agents’ parameter estimates and convergence of the estimates to the global posterior as if the data sets were aggregated at a central location. A major improvement of our result over previous studies is the establishment of said consensus without imposing any bounded restriction of the gradient of the objective function. Additionally, we establish probabilistic guarantees to prevent consecutive triggering by any agent while maintaining the same rate of convergence as in the case without event-triggering We demonstrate the DETULA using distributed supervised-learning problems. Our results indicate that the agents successfully recover the global posterior by periodically sharing their samples with neighbors.

Distributed event-triggered finite-time H∞ filtering for switched systems on sensor networks with two-channel network attacks and asynchronous modes

This paper addresses the problem of distributed event-triggered finite-time H∞ filtering for switched systems on sensor networks, subject to two-channel network attacks and asynchronous modes. To tackle the issue posed by the switched event-triggered communication mechanism and network attacks, a distributed filter with switching signal is introduced. Additionally, considering the mismatched behavior between switched systems and distributed filters caused by the event-triggered communication mechanism and network attacks, a piecewise vector modal function which considers all switching conditions is proposed for handling the problem of the sensor network’s filtering error system containing multiple switching signals. By utilizing the multiple Lyapunov functional method and the average dwell time switching mechanism, sufficient conditions are derived to guarantee that the filtering error system with synchronous and asynchronous modes is both finite-time bounded and satisfies the H∞ performance index. Finally, an example is presented to demonstrate the validity of the results.

Distributed Consensus Hierarchical Optimization and Control Method for Integrated Energy System Based on Event-Triggered Mechanism

For integrated energy systems (IES) composed of a set of energy hubs (EHs), a consensus control method is usually adopted to achieve accurate sharing of electrical and thermal composite energies. To solve the communication redundancy problem of the consensus control method, a hierarchical optimization and distributed control scheme based on a dynamic event-triggered mechanism of EHs is proposed to realize stable operation of IES. An economic optimization strategy based on equal increment principle is improved to minimize the operation costs of IES in the second layer. Due to consensus control being integrated into the supply-demand power deviation calculations of EHs, the desired electrical and thermal power trajectories are accurately determined. To improve dynamic response performances in the presence of uncertain disturbances, an event-triggered communication mechanism is designed in the primary layer. The triggering threshold can be adjusted dynamically according to changes of electrical and thermal power outputs, and the redundant communication requirement in the electrical branches is reduced. Considering the coupling characteristics of IES energy networks, a consensus control method is promoted to synchronously track the desired electric and thermal power trajectories of EHs, and the goal of accurate power sharing is achieved. The frequency and pipeline pressure fluctuations are also limited within the allowable range. The economic optimization and coordinated operation of electrical and thermal composite energies in IES are guaranteed by the proposed hierarchical control structure. Additionally, only information from neighboring EHs at the event-triggered time is involved, so the computation simplicity and control performance can be obtained simultaneously. The hardware-in-loop experimental results are conducted to demonstrate the effectiveness of the proposed control strategy.

Refined fault tolerant tracking control of fixed-wing UAVs via fractional calculus and interval type-2 fuzzy neural network under event-triggered communication

The refined fault tolerant tracking control (FTTC) scheme is developed for multiple fixed-wing unmanned aerial vehicles (UAVs) against actuator faults and wind effects under event-triggered communication. To decrease the communication overload caused by the continuous information transmissions among neighboring UAVs, an event-triggered communication mechanism is incorporated to dynamically reduce and increase the triggering frequency at the pre-fault and post-fault phases, respectively, thus reducing the communication burden while ensuring the synchronization tracking performance of faulty UAVs. Moreover, refined FTTC performance with event-triggered communications can be achieved by skillfully invoking fractional-order calculus. Furthermore, interval type-2 fuzzy neural networks with fractional-order adaptive modulation factors are constructed to compensate for the unknown nonlinearities. Lyapunov stability analysis has revealed that all attitudes can synchronously track their references. Simulation results are presented to demonstrate that communication resource-saving refined FTTC performance can be guaranteed with different fractional-order operators.

Event-triggered non-fragile state estimator design for interval type-2 Takagi–Sugeno fuzzy systems with bounded disturbances

In this paper, the state estimator design problem of interval type-2 Takagi–Sugeno fuzzy systems suffering from bounded disturbances is studied. To enhance the resilience of the estimator, a non-fragile design scheme is proposed to tackle the estimator gain variations. Meanwhile, an event-triggered communication mechanism is introduced for relieving the transmission burden over networks. To settle down the non-fragile estimator design issue subject to bounded disturbances and event-induced error, we propose a new definition of quadratic boundedness via the multiple Lyapunov functions. Based on this definition, a novel co-design method of estimator and event generator for fuzzy system models in the presence of both measurable and immeasurable premise variables is presented. In virtue of quadratic boundedness framework, less conservative conditions of the existence and quadratic stability of the fuzzy estimators are obtained, and the upper bound of estimation error is given explicitly. The desired estimator gains are determined by convex optimization technique using slack matrices. Two illustrative examples are exploited to validate the availability and superiority of the addressed design approach.

Distributed Formation-Containment Control for Discrete-Time Multiagent Systems Under Dynamic Event-Triggered Transmission Scheme

In this article, the distributed formation-containment (FC) control issue is investigated for a class of discrete-time multiagent systems (DT-MASs) under the event-triggered communication mechanism. In order to save the communication cost and improve the utilization of communication resources, a novel dynamic event-triggered (DET) mechanism is developed by adding an auxiliary variable for each agent system, which is able to dynamically adjust the triggering threshold. A distributed FC control scheme under the DET mechanism is proposed for all the leaders and followers based on the available relative outputs. The purpose of the addressed problem is to design the FC controller such that all the leaders achieve a formation shape and all the followers converge into such a convex hull. To this end, the considered DT-MASs are first decoupled into a diagonal form by resorting to the property of the Laplacian matrix as well as the inequality technique. Then, two sufficient conditions are established to ensure the desired FC performance. Furthermore, the corresponding FC controller parameters are obtained in terms of the solutions to two matrix inequalities which only depend on the maximum and minimum nonzero eigenvalues of the Laplacian matrix. Finally, an illustrative example is provided to verify the validity of the developed control scheme.