Event-triggered Communication Research Articles

Prescribed-time multi-coalition Nash equilibrium seeking by event-triggered communication

This article investigates the event-triggered prescribed-time Nash equilibrium seeking problem among multiple coalitions of agents in noncooperative games. Each coalition acts as a virtual player in the noncooperative game, with decisions made by its member agents. Agents lack complete information about others’ decisions and instead estimate them through a communication graph. An event-triggered prescribed-time multi-coalition Nash equilibrium seeking method is developed based on the leader-following consensus protocol, dynamic average consensus protocol, and gradient play. This method ensures the Nash equilibrium of the multi-coalition game is reached within a prescribed time, even when communication between agents only occurs under specific triggering conditions—effectively conserving communication resources. Unlike existing approaches, the proposed algorithm allows precise settling time assignment without prior knowledge of system parameters. This algorithm also prevents Zeno behavior. Lastly, the efficiency of the designed algorithm is demonstrated through simulation experiments.

Safety Flocking of Networked Lagrangian Systems With Event-Triggered Communication

Safety Flocking of Networked Lagrangian Systems With Event-Triggered Communication

Adaptive event-triggered stochastic estimator-based sampled-data fuzzy control for fractional-order permanent magnet synchronous generator-based wind energy systems

This study aims to develop an adaptive event-triggered estimation sampled-data control method for a fractional-order permanent magnet synchronous generator (PMSG)-based wind energy system (WES) using the Takagi–Sugeno (T–S) fuzzy approach. Unlike existing PMSG-based WES control schemes, we propose an aperiodic event-triggered communication scheme with an adaptive mechanism to reduce data transmission. To address the challenge of limited communication resources, we introduce an adaptive event-triggered (AET) estimation method with aperiodic sampling, significantly reducing communication overhead while maintaining stable WES performance. This method employs transmission techniques based on absolute errors, resulting in high data transmission efficiency. First, the fractional-order model for the PMSG-based WES is represented using linear sub-models through the T–S fuzzy approach. Next, a fuzzy aperiodic AET mechanism is proposed for the PMSG model with augmented estimation error systems. Then, the fractional Lyapunov function theory is employed to derive linear matrix inequalities (LMIs), ensuring bounded mean-square stability for the PMSG-based WES with the augmented estimation error systems. Additionally, the desired estimator control gains are determined through solvable LMIs. Finally, simulation studies are presented to demonstrate the superiority and feasibility of the proposed control scheme.

Dynamic event-triggered neuroadaptive fault-tolerant control of quadrotor UAV with a novel cosine kernel

The fault-tolerant control (FTC) for trajectory tracking of a quadrotor unmanned aerial vehicle (UAV) has attracted researchers. The non-linear model of the UAV, coupled with model uncertainties, external disturbances, and actuator failures, requires the function approximation of the lumped non-linearity for controller design. One of the most efficient ways to approximate non-linearity is using radial basis function neural networks (RBFNNs). To date, RBFNNs have been formulated, trained, and used directly for function approximation, which requires considerable computation to derive the control laws. The proposed control and parameter estimation laws approximate the non-linearity by using RBFNNs indirectly. The proposed laws with virtual parameter estimation do not require the actual formulation of RBFNNs and their weights, thus saving on computational resources. For kernel optimization, the Gaussian kernels with exponential terms in RBFNNs are replaced by cosine kernels with algebraic terms, which shows faster convergence as per simulation results. To save on communication bandwidth, static event-triggering communication mechanisms (SECM) and dynamic event-triggering mechanisms (DECM) have been proposed. As DECM works on dynamically changing variables, it saves more communication bandwidth, as tested in simulation. Lyapunov stability analysis proves that errors are uniformly ultimately bounded (UUB). The performance of the proposed algorithm has been tested through numerical simulations, which show superior performance when compared with similar studies. The proposed algorithm has been validated in a real-time Gazebo simulator.

Event-triggered distributed consensus control of heterogeneous multi-agent system under communication and actuator faults

In this paper, a heterogeneous leader-follower multi-agent system is studied under simultaneous time-varying communication faults and actuator faults. First, the state of the leader is modeled as the closed-loop reference model (CRM) where the states of the direct-connected followers are fed to the leader to improve the leader-follower tracking capability. An event-triggered communication mechanism is designed for the agent information sharing among its neighbors so as to reduce the communication burden. The designed event-triggered mechanism, capable of adjusting the triggering interval threshold within a certain range, can be applied in practical multi-robot collaborative control to accommodate the varying requirements for different triggering intervals. Considering the time-varying communication link failures, a new distributed event-triggered observer is designed for each follower to estimate the system states so as to reduce the state error, whereas the adaptive distributed event-triggered estimators are further designed for the non-directly connected followers to estimate the coefficient matrix of the leader system. Then, an estimator is designed for the actuator fault estimation to mitigate their impact on the system consistency. Finally, an adaptive control strategy is proposed to ensure the consistency of the leader-follower system under the time-varying communication link faults and actuator faults. It is also shown that Zeno behavior is excluded for each agent and the effectiveness of the proposed adaptive event-triggered control strategy is verified on a heterogeneous multi-agent system.

Event-triggered cascade predictor for a class of nonlinear MIMO systems with large communication delays

The event-triggered predictor design problem for a class of nonlinear MIMO systems with large time delays is investigated in this paper. A periodic event-triggered mechanism is designed to avoid unnecessary data transmission and save communication energy. The triggering condition is only determined by sampled outputs of the system, such that it is applicable in case of the communication delay. Then a novel observer-based cascade predictor is proposed to reconstruct the original system state based on the delayed and event-triggered measurements, which is composed of a continuous-discrete observer and several sub-predictors in a chain structure. The stability of proposed predictor is analyzed through the Lyapunov approach. By using a sufficient number of sub-predictors and applying appropriate parameters, the prediction errors converge to bounded regions exponentially under large time delays. Finally, simulations are performed to verify the effectiveness of the proposed predictor and the event-triggered communication mechanism.

Fixed-time control of teleoperation systems based on adaptive event-triggered communication and force estimators

Abstract A fixed-time control strategy based on adaptive event-triggered communication and force estimators is proposed for a class of teleoperation systems with time-varying delays and limited bandwidth. Two force estimators are designed to estimate the operator force and environment force instead of force sensors. With the position, velocity, force estimate signals, and triggering error, an adaptive event-triggered scheme is designed, which automatically adjusts the triggering thresholds to reduce the access frequency of the communication network. With the state information transmitted at the moment of event triggering while considering the time-varying delays, a fixed-time sliding mode controller is designed to achieve the position and force tracking. The stability of the system and the convergence of tracking error within a fixed time are mathematically proved. Experimental results indicate that the control strategy can significantly reduce the information transmission, enhance the bandwidth utilization, and ensure the convergence of tracking error within a fixed time for teleoperation systems.

Event-triggered design for optimal output consensus of high-order multi-agent systems

In this paper, we study the optimal output consensus problem for networked linear multi-agent systems. Existing distributed algorithms usually rely on continuous communication among neighbouring agents or continuous update of each agent's local controller. To save communication and computation resources, we apply event-triggered technique to this optimal output consensus problem. We first constructively develop an event-triggered algorithm with a set of applicable parameters relying on event-triggered communication and control and show its effectiveness in solving the problem by rigorous proofs. Then we extend it to the case where the triggering conditions only have to be checked in some sampling time instants. Two simulation examples are given to illustrate the efficacy of our designs.

Event-triggered model-free adaptive sliding-mode heading control for unmanned surface vehicles under DoS attacks

This article investigates event-triggered model-free adaptive sliding-mode heading control issues for unmanned surface vehicles with dual-channel denial of service (DoS) attacks and bounded disturbances. Initially, we establish a redefined output. Meanwhile, we establish a virtual linear data model based on the redefined output and pseudo-partial-derivative technology. In addition, to reduce the effects of the external disturbance, we introduce the sliding mode control method to enhance the system’s robustness against disturbances. Then, an event-triggered communication scheme is developed to reduce the waste of communication resources. Two compensation schemes are formulated to eliminate the effects of dual-channel DoS attacks. So, the main contributions of the presented method are the enhancement of accuracy, reduction of computational burden, and increased robustness, all of which are demonstrated in the simulation section.

Privacy-preserving distributed frequency control for AC microgrids with constrained communication

Distributed control of AC microgrids is one of the most popular methods in the islanded operation mode. Whereas, most of the existing studies either do not consider the potential threat of privacy security or rely on the assumption of ideal communication networks. To this end, this paper presents a novel privacy-preserving distributed secondary frequency control strategy for the privacy protection problem of an islanded AC microgrid with constrained communication. The key contributions of this paper are threefold. (1) Different from the existing privacy-preserving approaches used in AC microgrids, a time-varying function is introduced to mask interactive information such that the frequency cannot be reconstructed by malicious attackers. (2) An event-triggered communication scheme is employed to cope with the constrained communication environment. (3) A privacy-preserving distributed event-triggered control strategy with communication delay is developed such that the frequency restoration and active power sharing of the microgrid are guaranteed. Moreover, the maximum communication delay that the proposed control can withstand is analyzed. Simulation results show the properties of the privacy preservation, the decrease of communication load, and the bounded communication delay allowed in the proposed control strategy.

Distributed event-triggered algorithm for convex optimization with coupled constraints

This paper develops a distributed primal–dual algorithm via an event-triggered mechanism to solve a class of convex optimization problems subject to local set constraints, coupled equality and inequality constraints. Different from some existing distributed algorithms with the diminishing step-sizes, our algorithm uses the constant step-sizes, and is shown to achieve an exact convergence to an optimal solution with an ergodic convergence rate of O(1/k) for general convex objective functions, where k>0 is the iteration number. Based on the event-triggered communication mechanism, the proposed algorithm can effectively reduce the communication cost without sacrificing the convergence rate. Finally, a numerical example is presented to verify the effectiveness of the proposed algorithm.

Consensus algorithms for double-integrator dynamics with different velocity and actuator saturation constraints

This paper considers consensus strategy design for double-integrator multi-agent systems with matched disturbances under a directed graph. Specifically, we consider the case where both the control inputs and velocities of the agents are subject to different and asymmetric constraints. A novel distributed algorithm exploiting saturation functions is proposed to achieve asymptotic consensus without violating the predefined velocity and actuator saturation constraints. Subsequently, we extend the obtained results to event-triggered communication, where agent position broadcasting occurs only when specific triggering conditions are met. We rigorously prove that there exists a positive minimum inter-event time to rule out Zeno behavior. Finally, the simulation results confirm the theoretical findings and illustrate the achievable performance.

An Aperiodic-Sampling-Dependent Event-Triggered Control Strategy for Interval Type-2 Fuzzy Systems: New Communication Scheme and Discontinuous Functional.

This article studies the event-triggered control problem of interval type-2 (IT2) fuzzy systems. It proposes a new aperiodic-sampling-dependent event-triggered communication scheme, and introduces an improved sampling-dependent discontinuous functional. First, taking into account that the system is with aperiodic sampling, the weighting matrices used for judgement in the event-triggered scheme are designed to be related to each sampling interval or its upper and lower bounds, according to the sampling interval being predictable or not. This design makes the scheme more flexible, thereby improving control effectiveness. Second, a sampling-dependent discontinuous functional is introduced into event-triggered control, which contains matrix variables depending on the aperiodic sampling, and incorporates an improved discontinuous term and a vector related to event-triggered condition. Thus, the functional reduces the conservativeness of stability and stabilization criteria. Based on the above aperiodic-sampling-dependent strategy, the derived results achieve better event-triggered control effects compared with the recent literature. At the end, two examples are given to validate the theoretical effectiveness and advantages of the results under various parameters.

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 formation containment control for multi-agent systems via dynamic event-triggering communication mechanism

This paper focuses on resolving the distributed formation containment control issue in general multi-agent systems (MASs) possessing multiple leader agents subject to limited communication resources. The unnecessary data transmissions among agents in MASs are reduced by utilizing a unique dynamic event-triggering communication mechanism (DETCM), which can build communication time sequences for each agent automatically. Based on the sampled states of agents at event-triggering instants, a novel distributed formation containment control protocol is proposed. By mathematical analysis, the formation containment control problem is turned into the problem of stability in a reduced-order system. The global formation containment of the MASs is finally achieved through the application of Lyapunov-Krasovskii functionals and general inequalities. Moreover, a valid example is also given to support the validity and effectiveness of the findings.

Linear convergence of event‐triggered distributed optimization with metric subregularity condition

AbstractThis paper designs a continuous‐time algorithm with event‐triggered communication (ETC) for solving a class of distributed convex optimization problems with a metric subregularity condition. First, we develop an event‐triggered continuous‐time optimization algorithm to overcome the bandwidth limitation of multi‐agent systems. Besides, with the aid of Lyapunov theory, we prove that the distributed event‐triggered algorithm converges to the optimum set with an exact linear convergence rate, without the strongly convex condition. Moreover, we provide the discrete version of the continuous‐time algorithm and show its exact linear convergence rate. Finally, we give a comparison example to validate the effectiveness of the designed algorithm in communication resource saving.

Fully distributed observer-based scaled consensus of multi-agent systems with actuator saturation and edge-based event-triggered communication

This work uses an edge-based event-triggered technique to study the adaptive scaled consensus of general linear multi-agent systems with actuator saturation and unmeasurable internal states. By proposing an edge-based event-triggered scaled algorithm, constructing an improved Lyapunov function, and introducing a low-gain feedback technique, the difficulties generated by actuator saturation and scaling property are overcome, and a triggering condition based on the triggering function is obtained, under which the scaled consensus is achieved, and the Zeno behavior is excluded. This work’s results are more flexible than existing results; in other words, triggering numbers and convergence rates are adjusted in this work. Finally, several examples are proposed to verify the results and their interesting characteristics.

Event-triggered filtering for complex networks subject to random coupling strength and missing measurements: A partial-nodes accessible case

In this paper, the event-triggered filtering issue is addressed for a class of discrete-time nonlinear complex networks (CNs) considering random coupling strength (RCS) and missing measurements (MMs) under the condition that partial nodes information is accessible. To begin with, a uniformly distributed random variable over a settled interval is employed to model random changes in coupling strength. Then, a Bernoulli distributed random variable is considered to depict the phenomenon of MMs with uncertain occurrence probability. The event-triggered communication (ETC) protocol is applied to schedule data to relieve transmission burden. Subsequently, a novel partial-node-based recursive filtering algorithm is constructed by taking the influence of RCS, MMs and ETC mechanism into account, in which the filter gain is parameterized to achieve the minimization for the upper bound of filtering error covariance (UBFEC). Besides, performance discussion for the developed filter is provided through strict theoretical derivations, including the uniform boundedness and monotonicity relationship. Finally, two simulation examples are presented to demonstrate the effectiveness of the proposed estimation scheme.

Distributed formation tracking for multi-UAVs with unknown disturbances under event-triggered communication

This paper presents a novel distributed formation control scheme under event-triggered communication to solve the cooperative formation tracking problem of multiple unmanned aerial vehicles (multi-UAVs) with unknown disturbances. An event-triggered communication protocol whose triggering condition depends on the system’s current state is designed, and thus reduces the unnecessary communication consumption. Based on such communication protocol, a robust formation control is constructed and skillfully combined with disturbance estimator to further enhance the robustness to resist unknown disturbances. Moreover, an event-triggered strategy is further incorporated to update the controller. By employing Lyapunov theory, new sufficient conditions for guaranteeing the system’s stability are rigorously derived, while excluding Zeno behavior. The efficiency and advantage of our method are illustrated by numerical validation of formation tracking under unknown nonlinear disturbances and detailed comparisons with closely related works. Our method is also verified in an ROS/PX4/Gazebo platform, fully indicating its significant value for applications.

Watermarking-based remote secure sequential fusion estimation under the event-triggered mechanism

This paper investigates the problem of remote sequential fusion estimation using event-triggered communication mechanism in cyber–physical systems with deception attack interference. The watermarking defense strategy is used to encrypt and decrypt the data transmitted by sensors, which is convenient for the χ2 detector to detect deception attacks. The effectiveness of the χ2 detector is verified for three different attack scenarios. In addition, an event-triggered mechanism is designed based on the prediction error variance, which greatly saves the network resource while guaranteeing the accuracy of sequential fusion estimation. Finally, simulation results are given to illustrate the effectiveness of the proposed sequential fusion estimation method.