Event-triggered Mechanism Research Articles

Event‐triggered differential private distributed fusion estimation for sensor networks with unknown cross‐correlations

AbstractRecently, preserving privacy and reducing energy consumption become important concerns of sensor networks. However, existing privacy‐preserving estimation methods cause many needless data transmissions, which results in energy waste. Besides, these methods generates fusion estimates with heavy computational burdens, due to needing calculating cross‐covariance matrices of each pair of local estimation errors. Worse still, these cross‐covariances are often unavailable in actual sensor networks. In view of these problems, a novel framework of event‐triggered (ET) differential private distributed fusion estimation is proposed. Within this framework, an ET mechanism is designed to schedule transmissions of local estimates for reducing communication consumptions. Then, with using local perturbation mechanism, conditions for achieving differential privacy and dealing with eavesdroppers which can eavesdrop and fuse local estimates are provided. Based on covariance intersection fusion rule, an ET differential private fusion method is developed, which can guarantee fusion results uniformly stable in spite of completely unknown cross‐covariances. Finally, simulation results verify that the proposed method can preserve privacy and reduce communication consumptions without using cross‐covariances, at the cost of only slight decline of estimation performance.

Event-triggered robust cooperative output regulation for a class of linear multi-agent systems with an unknown exosystem

This paper investigates the robust cooperative output regulation problem for a class of heterogeneous uncertain linear multi-agent systems with an unknown exosystem via event-triggered control (ETC). By utilizing the internal model approach and the adaptive control technique, a distributed adaptive internal model is constructed for each agent. Then, based on this internal model, a fully distributed ETC strategy composed of a distributed event-triggered adaptive output feedback control law and a distributed dynamic event-triggering mechanism is proposed, in which each agent updates its control input at its own triggering time instants. It is shown that under the proposed ETC strategy, the robust cooperative output regulation problem can be solved without requiring either the global information associated with the communication topology or the bounds of the uncertain or unknown parameters in each agent and the exosystem. A numerical example is provided to illustrate the effectiveness of the proposed control strategy.

A rumor propagation model in multilingual environment with time and state dependent impulsive control

In this paper we are interested in the problem of modeling, analysis and control of rumor spreading on online social networks. As the diversity of languages and the difference of user nodes in the network, a heterogeneous rumor spreading model with two language spreaders is proposed, and the impulsive isolation control strategy is considered. Firstly, the model with periodic impulsive isolation control and time-delay recovery rate is discussed. Based on the stability theory of impulsive differential equations, some conditions regarding the impulsive period and control gains are given for the rumor asymptotically disappearance and persistent existence. Secondly, a new type of event-triggered impulsive isolation control method is developed to address the tradeoff between restraining rumor and minimizing the cost of control, and some conditions are given to make the rumor disappear asymptotically. Furthermore, an improved event-triggered mechanism is established based on the fixed-time stability theory, which provides the conditions for the rumor to disappear within a fixed-time. Finally, some numerical simulations and an actual example are given to verify the rationality of the model and the effectiveness of the control methods.

Observer‐based event‐triggered H∞$$ {H}_{\infty } $$ control of networked switched systems under hybrid attacks

AbstractThis article is concerned with the issue of observer‐based event‐triggered control for networked switched systems (NSSs) under hybrid attacks. Since hybrid attacks can cause more damage to the system than a single cyber attack, this article considers a situation where stochastic deception attacks and aperiodic denial‐of‐service (DoS) attacks coexist in NSSs. To address this issue, the dynamics of the initial switched system combined with the event‐triggered mechanism (ETM), observers and hybrid attacks are formulated as a new augmented switched system. Thereinto, the unmeasurable states are estimated by a newly constructed gain adjustable observer and an observer state‐based adaptive ETM is designed under DoS attacks to save limited communication resources. Then, employing the Lyapunov stability theory and average dwell time (ADT) approach, sufficient conditions are obtained to guarantee the mean‐square exponential stability with a performance index of the new model. And the quantitative relationship between the switching signal and DoS attacks is formulated. Moreover, a co‐design method of ETM parameters and observer‐based controller gains is proposed. Compared with some existent results, restrictions on DoS attack signals and switching signals are relaxed. Finally, a numerical example is utilized to verify the feasibility and effectiveness of the proposed theory.

Command filtering event-triggered secure control for nonlinear cyber-physical systems under denial-of-service attacks and input saturation

For nonlinear cyber-physical systems (CPSs) with input saturation and denial-of-service (DoS) attacks, an adaptive secure control strategy is investigated in this paper. To estimate the unmeasurable states, a novel switched neural network (NN) observer is designed, where two sub-observers can be freely switched from each other. Considering unnecessary packet transmissions, an improved event-triggered mechanism (ETM) is introduced to reduce the communication burden in the controller-to-actuator channel. In addition, in order to simultaneously solve the input saturation and unknown control direction problems, a Nussbaum-type function is introduced to the control design process. Finally, it is proven that all closed-loop signals are the semiglobal uniformly ultimately bounded, and simulation results demonstrate the effectiveness of the proposed secure control scheme.

Event-triggered adaptive vibration control for a flexible satellite with time-varying actuator faults

In this study, vibration control is studied for the flexible satellite through wireless communication, and given the fact that the actuators are prone to unknown faults, the input signal event-triggering and time-varying actuator faults are considered for the flexible satellite simultaneously. The satellite system is composed of two symmetrical flexible panels attached to a centrebody, which is a distributed parameter system and the established model is represented by partial differential equations (PDEs). Based on the PDEs model of the flexible satellite, the input event-triggered mechanism using the relative threshold strategy is designed firstly to lower the communication burden, and then the event-triggered-based adaptive fault-tolerant control laws are developed with the aid of the designed auxiliary signals for the system with time-varying actuator faults. With the proposed control scheme, the vibration of the system is controlled to the small neighborhood of zero, and the angle of the panels is regulated to the desired position. Finally, numerical simulation results are given to further show the effectiveness of the designed control scheme.

CBF‐guided iterative learning control performance optimization for switched systems

AbstractThis article studies the control barrier function (CBF) guided tracking performance optimization for switched systems under iterative learning control (ILC). CBF is an effective method for handling control system state safety. If the safe set is designed as expected performance, and the unsafe set is not expected, then this may be a good choice for optimizing control system performance. We propose a tracking performance optimization method based on CBF to speed up the convergence of the tracking error. A safe set is used to describe the expected tracking performance, by utilizing CBF, safety is further transformed into a set of inequality conditions that constrain ILC controller gains, which ensures that the tracking error is always within the safe set. In addition, an event‐triggering mechanism (ETM) is considered to reduce unnecessary data transmission, and sufficient conditions for exponential stability of the tracking error of switched systems and ILC controllers design are obtained. Finally, a simulation example is given to verify the feasibility of the proposed method.

Event-based adaptive active disturbance rejection control for nonlinear large-scale systems with input delay

This paper explores the active disturbance rejection control (ADRC) issue for interconnected large-scale systems with unmeasured states, input delay and a total disturbance composed of unknown constrained external disturbances. An enhanced extended state observer is built via fuzzy logic systems to identify system uncertainties and states and keep restraining the total disturbance of uncertain systems. When the triggering condition is met, the tracking differentiator component of the ADRC is able to reduce the communication overhead by successfully applying the backstepping control approach and the dynamic event-triggered mechanism. Meanwhile, the integral compensation method eliminates the negative effect cased by the input delay. Furthermore, the developed decentralised fuzzy control scheme can ensure that all signals in the closed-loop systems are semiglobally uniformly ultimately bounded and Zeno behaviour does not occurs. Finally, an example is used to depict the validity of the control approach.

A novel distributed bearing‐only target tracking algorithm for underwater sensor networks with resource constraints

AbstractUnderwater sensor networks hold immense potential for advancing the field of underwater target tracking, yet they encounter significant resource constraints stemming from energy storage and communication methods. In order to balance tracking accuracy and energy consumption, the authors present a distributed bearing‐only target tracking algorithm that can be used in underwater sensor networks with resource constraints. Anchored in the diffusion cubature information filter framework, this algorithm achieves fusion for non‐linear bearing measurements and state estimation. During the incremental update stage, individual nodes leverage the Posterior Cramer‐Rao Lower Bound as a metric for tracking performance. Subsequently, a strategy for selecting neighbouring nodes is introduced, ensuring tracking accuracy while efficiently kerbing energy consumption. In the diffusion update stage, a multi‐threshold event triggering mechanism is employed to partially diffuse the intermediate estimation. Additionally, an adaptive convex combination weight is proposed for cases involving partial diffusion. Through theoretical analysis, the asymptotic unbiasedness and convergence of the algorithm have been proven. Through Monte Carlo simulation experiments, the authors verify that the algorithm is superior to existing algorithms. Furthermore, the algorithm significantly reduces energy consumption in information interaction, minimising tracking accuracy loss.

Self-tuning iterative learning control for an USV: Application to an autonomous berthing operation with an avoidance obstacle mechanism

This note focuses on the path planning and path-following control in the task of berthing for the underactuated surface vessel (USV), aiming to realize these functions that the accuracy of path-following can be preset and the smoother path planning based on the task of berthing. The proposed scheme is formulated as two components, i.e., the potential field virtual ship (PFVS) guidance part and the self-tuning iterative learning control (STILC) part. By utilizing one novel repulsive field applied around the obstacle and one corrected potential field, the PFVS guidance law is developed in the scheme to plan the reference path and program the real-time heading angle for the USV. The main superiorities of this design are that the guidance signal buffeting and target unreachable problem have been evaded in obstacle avoidance. In addition, the reference speed has been ingenious combined with the artificial potential field (APF). Furthermore, to improve the suitability of the closed-loop control system with ocean engineering, the iterative learning control (ILC) has been improved by introducing the input event-triggered mechanism. An STILC algorithm is designed for preset accuracy can achieve and reduce computing burden. In this algorithm, the number of iterations would self-turning based on the preset accurate. The computation burden of the proposed controller is reduce with the decreasing number of iterations. Through the Lyapunov analysis, we have exerted sufficient effort to ensure that all signals of the closed-loop control system are semi-globally uniformly ultimately bounded (SGUUB). The simulation experiments have been presented to verify the obstacle avoidance effectiveness and the burden-some superiority of the algorithm.

Finite-time Secondary Frequency Modulation of Microgrid Based on Event Triggering Mechanism

Finite-time Secondary Frequency Modulation of Microgrid Based on Event Triggering Mechanism

Practical Tracking Control for High-order Nonlinear Systems With Dynamic Uncertainties and Unknown Powers via Event-triggered Mechanism

Practical Tracking Control for High-order Nonlinear Systems With Dynamic Uncertainties and Unknown Powers via Event-triggered Mechanism

Secure Change Control for Supply Chain Systems via Dynamic Event Triggered Using Reinforcement Learning under DoS Attacks

In this paper, a distributed secure change control scheme for supply chain systems is presented under denial-of-service (DoS) attacks. To eliminate the effect of DoS attacks on supply chain systems, a secure change compensation is designed. A distributed policy iteration method is established to approximate the coupled Hamilton–Jacobi–Isaacs (HJI) equations. Based on the established reinforce–critic–actor (RCA) structure using reinforcement learning (RL), the reinforced signals, performance indicators, and disturbance input are proposed to update the traditional time-triggered mechanism, and the control input is proposed to update the dynamic event-triggered mechanism (DETM). Stability is guaranteed based on the Lyapunov method under secure change control. The simulation results for supply chain systems show the effectiveness of the secure change control scheme and verify the results.

Periodic event-triggered bipartite containment control for nonlinear multi-agent systems with input delay

ABSTRACT This paper studies the periodic event-triggered bipartite containment control problem of multi-agent systems with unmeasurable states and input delays. To deal with the system input delay problem, a Pade approximation technique is introduced. By utilising the sampled output of the system, a fuzzy state observer is constructed to estimate the unknown state. A periodic event-triggered mechanism is designed, which can effectively reduce the usage of communication resources. Compared with the traditional event-triggered control manner, a periodic event-triggered control manner only monitors the preset threshold condition at the sampling instant to determine whether to update the current control signal, which can automatically guarantee the minimum lower bound of the execution interval, avoiding the occurrence of Zeno behaviour. Stability analysis verifies that all signals in the closed-loop system are bounded, and the bipartite tracking error can converge to a small neighbourhood of the origin. Finally, a practical example further verifies the effectiveness of the designed control scheme.

Event-driven prescribed performance distributed formation control for MSVs based on second-order step response with actuator input saturation

This study aims to investigate the problem of event-driven distributed formation control for marine surface vehicles (MSVs) system, including prescribed performance constraint and actuator input saturation. By constructing a novel prescribed performance function based on second-order step response, the intuitive physical significance is realized, and the performance specifications imposed on the output tracking errors are achieved. Subsequently, a double-layer adaptive sliding-mode disturbance observer (ASMDO) is presented to compensate for lumped uncertain terms. Within the controller design stage, an auxiliary dynamic system is built to introduce the auxiliary intermediate control law, thus avoiding the need to directly design anti-saturation generalized control input for the MSVs system. Then, by incorporating external dynamic variable, a dynamic event-triggered mechanism (DETM) is proposed to regulate the transmission between the controller and actuators. Under the DETM, the designed controller only transmits the control force and torque into actuators at the specified triggering moment, and the trigger moment is independent of the internal state of the system. After that, the minimum inter-event time (MIET) of the proposed DETM is proved to be strictly positive, which ensures Zeno-free behavior. Lastly, the efficiency and advantages of the proposed event-driven distributed formation controller are verified through numerical simulations.

Finite-time guarantee-cost [formula omitted] consensus control of second-order multi-agent systems based on sampled-data event-triggered mechanisms

This study presents a solution to the challenges of tracking consensus and guarantee-cost H∞ control in a specific set of second-order multi-agent systems with external disturbances. A proposed event-triggered control method based on periodic sampling data is presented for second-order multi-agent systems that include external disturbances. In contrast to the real-time monitoring of system state information used in the previous event-triggered mechanism, this approach collects system state information through periodic sampling. This ensures that the interval between two consecutive triggering moments is at least one sampling cycle, thereby preventing the controller from triggering infinitely within a finite time frame. A finite-time controller based on the sampled-data event-triggered mechanism is designed, and sufficient conditions to ensure the finite-time stability of the closed-loop system at a specified attenuation level are established using theoretical methods such as matrix analysis. For the given sampled-data event-triggered control protocol with a finite-time controller, a quadratic guarantee-cost function is introduced, and by designing control inputs and determining the parameters such as the finite-time upper bound T∗ and the H∞ performance index γ , the exact value of the upper bound of the system’s guarantee-cost function under the action of the designed controller is derived. Finally, the feasibility of the proposed control scheme is verified through numerical simulation.

Resilient event-triggering adaptive neural network control for networked systems under mixed cyber attacks

This paper addresses the resilient event-triggering adaptive neural network (NN) control problem for networked control systems under mixed cyber attacks. Compared with the conventional event-triggered mechanism (ETM) with constant threshold, a novel resilient ETM is designed to withstand the affect of denial-of-service attacks and conserve communication resources. Different from the energy-bounded deception attacks, an unknown state-dependent nonlinear attack signal is considered in this work. To identify the deception attack, the NN technique is utilized to approximate the unknown attack signal. Subsequently, an adaptive controller is established to compensate for the malicious affects of deception attacks on the system. Furthermore, sufficient conditions for the boundedness of the system are derived via applying the Lyapunov functional, and a co-design strategy for control gain and event-triggering parameter is provided. Finally, the feasibility of the proposed approach is validated through a robot manipulator system.

Partial‐node‐based resilient set‐membership filtering for output‐coupled complex networks with event‐triggered mechanisms

AbstractThis paper studies the resilient set‐membership filtering for discrete‐time nonlinear complex networks with output‐coupled topologies. An event‐triggered mechanism (ETM) is employed to save communication resources from the sensor to the filter. A resilient set‐membership filter (RSMF) handles unknown bounded uncertainty, resists the gain perturbation and makes the estimation errors to a certain ellipsoid region. Sufficient conditions for the existence of the RSMF are derived using mathematical induction techniques. The filter gain can be determined on the basis of solving a specific set of recursive matrix inequalities. Then, by optimizing the constrained ellipsoid of the estimation error, the optimized filtering performance is ensured. Finally, a numerical example is given to demonstrate the effectiveness of the proposed RSMF.

Hybrid dynamic event-triggered leader-following consensus for multi-agent systems with external disturbances

Herein, we address the leader-following consensus for multi-agent systems with a directed graph while considering external disturbances. To optimize resource utilization, we propose a hybrid dynamic event-triggered mechanism (HDETM) that minimizes the inter-agent communication and reduces the frequency of controller updates. Furthermore, regularization techniques are employed to ensure that the considered system maintains a Zeno-free behavior even in the presence of an external disturbance by guaranteeing a strictly positive inter-event time. Additionally, we propose a hybrid system framework incorporating auxiliary variables to depict both flow and jump dynamics. A Lyapunov consensus analysis is performed within this framework to verify finite-gain L2 stability. Finally, we validate the efficacy of the HDETM design method through numerical simulations.

Observer-based MPC for fuzzy cyber–physical system with hybrid attacks via a dynamic event-triggered mechanism

In this article, a secure model predictive control (MPC) algorithm is addressed for a nonlinear cyber–physical system (CPS) subject to hybrid attacks and parameter uncertainties via a dynamic event-triggered mechanism (DETM). The nonlinear CPS with parameter uncertainties is represented by the interval type-2 Takagi–Sugeno (IT2 T–S) fuzzy model. Considering that the full states of system are unmeasurable and aiming at decreasing the transmission burden, a DETM is proposed in the observer-based MPC scheme. Moreover, the hybrid attacks, including denial-of-service (DoS) attacks and deception attacks, are investigated in a unified framework due to the vulnerability of communication network. Furthermore, a secure MPC algorithm is presented, which consists of an off-line designed state observer and an on-line optimized MPC algorithm. Besides, by considering the internal dynamic variable in DETM, a new formula for updating estimation errors bounds is proposed to guarantee the feasibility of proposed algorithm. Finally, a practical example and comparative studies confirm the effectiveness of the proposed scheme.