Dynamic Event-triggered Mechanisms Research Articles

Distributed leader-following bipartite consensus for one-sided Lipschitz multi-agent systems via dual-terminal event-triggered mechanism

This article analyses leader-following bipartite consensus for one-sided Lipschitz multi-agent systems by dual-terminal event-triggered output feedback control approach. A distributed observer is designed to estimate unknown system states by employing relative output information at triggering time instants, and then an event-triggered output feedback controller is proposed. Dual-terminal dynamic event-triggered mechanisms are proposed in sensor–observer channel and controller–actuator channel, which can save communication resources to a great extent, and the Zeno behavior is ruled out. A new generalized one-sided Lipschitz condition is proposed to handle the nonlinear term and achieve bipartite consensus. Some stability conditions are presented to guarantee leader-following bipartite consensus. Finally, one-link robot manipulator systems are introduced to demonstrate the availability of the designed scheme. The results demonstrate that the agents of the robot manipulators can track the reference trajectories bi-directionally, and effectively reduce communication resources by 61.22% and 68.04% at the sensor–observer and controller–actuator channels, respectively.

Cooperative Path Tracking-Based Learning Control for Unknown Multi-Agent Systems via Dynamic Event-Triggered Mechanisms

Cooperative Path Tracking-Based Learning Control for Unknown Multi-Agent Systems via Dynamic Event-Triggered Mechanisms

Dynamic Event-Triggered Formation Control for Heterogeneous Multiagent Systems With Nonautonomous Leader Agent.

In this article, the time-varying output formation issue of heterogeneous multiagent systems is investigated by the event-triggered control scheme. Only the outputs of all agents, including leader agent and follower agents, are measurable. The leader agent contains an unknown input signal to generate flexible reference trajectory. Also, only a subset of follower agents have the direct access to the leader agent. First, for each follower, the leader-state compensator is designed to estimate the state of leader. Two kinds of dynamic event-triggered (DET) mechanisms, i.e., node- and edge-based event-triggered schemes, can be equipped on the compensator to save the communication bandwidth of leader-follower and follower-follower interactions, respectively. Then, the distributed formation controller is built to drive each follower achieving formation tracking. The presented control protocol consisting of the DET state compensator and formation controller is fully distributed, which is independent of the global information of communication topology, such as the eigenvalues of Laplacian matrix of communication topology and amount of whole agents. Finally, the numerical experiments and comparison experiments are exhibited to verify the effectiveness of the presented control protocol.

Adaptive backstepping control for multiple Euler–Lagrangian systems with independent dynamic communication

This paper presents a novel backstepping formation control strategy for multiple Euler–Lagrange (EL) systems. To enhance parallel communication capability, the communication channels for systems’ position vectors and filters’ states are independent. Subsequently, the corresponding dynamic event-triggered mechanisms are designed specifically for them to reduce the frequency of interactive communication networks, and a distributed filter is devised to eliminate the non-differentiability problem of event-triggered signals, respectively. Furthermore, we introduce a new barrier Lyapunov function (BLF) to indirectly restrict the velocity vector. The key novelty of this BLF is that the controller gain matrix caused by the designed BLF can be approximated as an identity matrix, when the distance between the velocity component and the designed boundary is substantial. To prevent the designed dynamic event-triggered mechanisms from being triggered infinitely for a period of time, Zeno behavior is excluded. By leveraging Lyapunov stability theory and Barbalat lemma, we demonstrate that all signals of the closed-loop system are bounded and the formation error asymptotically converges to the origin. Finally, simulation results are provided to demonstrate the efficacy of the proposed control schemes.

Formation Tracking Control for Heterogeneous Multiagent Systems With Multiple Nonautonomous Leaders via Dynamic Event-Triggered Mechanisms.

This article considers the time-varying formation (TVF) tracking issue of heterogeneous multiagent systems (HMASs) with the dynamic event-triggered control. The HMASs contain heterogeneous multiple leaders, all of which have the input signals to generate flexible reference, and only the output information can be measured. All leaders do not have access to the same followers, that is, the well-informed follower assumption is removed in this article. In this setting, the adaptive multileader state compensator is designed for each follower to estimate the integrated state information of all leaders, which can equip with two kinds of dynamic event-triggered mechanisms, that is, node-based event-triggered mechanism and edge-based event-triggered mechanism, to save communication bandwidth. Then, the TVF controllers are built by some estimation values to regulate the followers to achieve and maintain the geometric shape while tracking the reference which is the convex combination of outputs of leaders. The event-triggered compensator and TVF controller constitute the control protocol of HMASs, which are independent of global information with the fully distributed manner. The stability analysis and numerical simulations are given to verify the presented control protocol.

$$H_{\infty }$$ controller synthesis for interval type-2 fuzzy network systems with hybrid attacks and disturbances via dual-channel dynamic event-triggered mechanisms

$$H_{\infty }$$ controller synthesis for interval type-2 fuzzy network systems with hybrid attacks and disturbances via dual-channel dynamic event-triggered mechanisms

Neural learning-based dual channel event-triggered deployment control of space tethered system with intermittent output

In this paper, we investigate the dual-channel event-triggered deployment control for the space tethered system with intermittent output. Two different dynamic event-triggered mechanisms are designed to implement the event-based state sampling and the event-based input sampling, in which the data transmission frequencies are reduced at the dual channel, namely sensor-to-controller and controller-to-actuator. Then, the neural network (NN) state observer based on the intermittent output is designed to estimate the unmeasurable state under external disturbance, and the observer-based sliding mode controller is designed. Furthermore, because of the non-periodic sampling of the state signal and the output signal, the closed-loop system is proved via the analysis of the hybrid system, and the Zeno behavior is avoidance under these two event-triggered conditions. Finally, the simulation tests are implemented to verify the effectiveness of the proposed scheme.

Formula omitted] Consensus of multi-agent systems under hybrid cyber attacks via a sampled-data-based dynamic event-triggered resilient consensus protocol

This paper studies the H∞ consensus problem of multi-agent systems (MASs) under external disturbances and hybrid cyber attacks. First, to save limited network resources, a sampled-data-based dynamic event-triggered mechanism is proposed for MASs under hybrid cyber attacks. Different from the existing dynamic event-triggered mechanisms, the negative influences of deception attacks and denial-of-service attacks are considered in the proposed dynamic triggering function. The communication frequencies between agents are reduced by the proposed event-triggered mechanism. Then, based on the proposed sampled-data-based dynamic event-triggered mechanism, a corresponding resilient consensus protocol is designed to ensure that MASs with external disturbances and hybrid cyber attacks can achieve consensus with a weighted H∞ performance. Finally, simulation examples are conducted to verify the effectiveness of the proposed methods.

Dynamic event-triggered predictive control for DoS attacks-based discrete-time networked linear systems

This paper studies a novel method of the dynamic event-triggered mechanisms and the predictive control scheme for discrete-time networked linear systems subject to network delays and DoS attacks in both sensor-to-controller ([Formula: see text]) and controller-to-actuator ([Formula: see text]) channels. First, by assuming maximum allowable duration of DoS attacks, predictive control scheme is used to proactively offset the adverse impacts of communication constraints. Second, to further save communication resources, dynamic event-triggered mechanisms are configured on sensor and controller sides, respectively. Next, sufficient condition is derived for discrete-time networked linear systems under predictive control and dynamic event-triggered mechanisms, which guarantees the resulting closed-loop system’s asymptotical stability. Finally, a simulation example is used to confirm the availability and applicability of the proposed method.

Distributed finite-time adaptive fault-tolerant formation–containment control for USVs with dynamic event-triggered mechanism

This paper presents a distributed finite-time adaptive fault-tolerant formation–containment control strategy for unmanned surface vessels (USVs) with dynamic event-triggered mechanism in the presence of total disturbances and actuator failures. A two-layer framework consisting of reference trajectory estimators and trajectory tracking controllers is proposed. In the first layer, two sets of distributed finite-time reference trajectory estimators (DFRTE) are developed using graph theory to estimate trajectory information (position and velocity) for leaders and followers. In the second layer, the fast integral terminal sliding mode control (FITSMC) is introduced to ensure that the leaders form the desired formation, and the followers converge to the convex hull formed by the leader, the robust exact differentiators (RED)-based disturbance observers are constructed to estimate the total disturbances composed of model uncertainties and external disturbances, and adaptive laws are designed to compensate actuator failures. To reduce communication resource consumption between the actuator and the controller channels, dynamic event-triggered mechanisms (DETMs) are designed to update the actuators only when the triggering conditions are satisfied. The Lyapunov analysis method is employed to prove the finite-time stability of the closed-loop system. Simulation results demonstrate the effectiveness of the proposed control scheme.

Distributed resilient fusion filtering for nonlinear systems with multiple missing measurements via dynamic event-triggered mechanism

This paper investigates the distributed resilient fusion filtering (DRFF) issue under inverse covariance intersection (ICI) fusion criterion and dynamic event-triggered mechanisms (DETMs), where the physical plant is described by stochastic nonlinear multi-sensor networked systems (MSNSs) with time-varying system parameters and multiple missing measurements (MMMs). The measurements from various sensor nodes to the fusion center may undergo the missing data, where this phenomenon is depicted by means of random variables governed by certain statistical principles. In addition, the DETM is adopted to regulate the communication process from each sensor node to fusion center, which can alleviate the network transmission situations with communication overload and energy consumption limitation. The purpose of the addressed issue is to construct a set of local resilient filters (LRFs) for stochastic nonlinear MSNSs with MMMs via the DETM, which can guarantee that the minimized upper bounds are derived and the desirable filter gain with easy-to-implementation form is given. Subsequently, via the obtained LRFs, a unified framework of the DRFF approach is formulated through using the ICI fusion criterion. In addition, the monotonicity analysis of the obtained upper bound in regard to the triggered parameter is examined by providing rigorous theoretical proof. Finally, the simulations with comparison experiment are provided to illustrate the validity of presented DRFF technique.

Fully Distributed Dynamic Edge-Event-Triggered Current Sharing Control Strategy for Multibus DC Microgrids With Power Coupling

Although the current sharing control of dc microgrids has been widely studied, the high communication bandwidth and global communication network structure information demands hander the renewable energy consumption. Thus, this article proposes a fully distributed dynamic edge-event-triggered current sharing control strategy for multibus dc microgrids with power coupling. First, the system model with power coupling is built, which is further switched to the linear heterogeneous multiagent systems with unknown disturbance. It is an indispensable preprocessing for controller design. Furthermore, the fully distributed current sharing control strategy is proposed through adaptive coupling weights. Note that the global communication network structure information demand is eliminated. Moreover, the fully distributed dynamic edge-event-triggered mechanism is proposed to reduce communication bandwidth. Compared with the previous dynamic event-triggered mechanisms applied into dc microgrids, the continuous communication between neighboring agents is avoided, and controller updating frequency is reduced. Finally, the simulation and experimental results verify the proposed control performance.

Event-Triggered Sliding Mode Control for Spacecraft Reorientation With Multiple Attitude Constraints

This paper addresses the event-triggered attitude control problem for spacecraft anti-unwinding reorientation with multiple attitude constraints in the presence of external disturbance. Based on a designed novel potential function(PF), a new sliding mode controller is proposed to guarantee the completeness of anti-unwinding reorientation with multiple attitude constraints and external disturbance. Different from existing continuous attitude reorientation controller, the static and dynamic event-triggered mechanisms, related with the sliding mode variable and disturbance bound, are then constructed. With the designed event-triggered sliding mode control schemes, the attitude system asymptotic stability, the satisfaction of attitude constraints with anti-unwinding action and free of Zeno phenomenon despite on external disturbance are proved theoretically. Numerical simulations results confirm that the proposed methods can significantly reduce resource usage while guaranteeing the desired performance.

Neural-Network-Based Control With Dynamic Event-Triggered Mechanisms Under DoS Attacks and Applications in Load Frequency Control

The paper is concerned with the supplementary control based on adaptive dynamic programming (ADP) for a class of discrete-time networked system with the simultaneous presence of dynamic event-triggered mechanisms and Denial-of-Service (DoS) attacks. The dynamic behavior of DoSs is described by a model with the appropriate frequency and durations. A neural network (NN)-based observer is first designed to estimate system states in order to resolve the limitation in ADP-based control due mainly to data sparsity. The performance analysis and gain design of the NN-based observer are systematically discussed in light of the switched system theory combined with the average dwell-time method. Subsequently, the policy iteration algorithm with an actor-critic structure is developed to implement the designed supplementary ADP controller, and the corresponding condition on learning rates in weight updating rules is derived by virtue of the well-known Lyapunov stability. Finally, the effectiveness of the developed approach is demonstrated by an application in load frequency control of power systems.

Guest Editorial: Learning, optimisation and control of cyber‐physical systems

Guest Editorial: Learning, optimisation and control of cyber‐physical systems

Distributed Event-Triggered Synchronization of Interconnected Linear Two-Time-Scale Systems With Switching Topology.

This article investigates the synchronization problem of interconnected linear two-time-scale systems (TTSSs) with switching topology. By utilizing the Chang transformation, a distributed synchronization protocol is proposed with event-triggered communication. Static and dynamic event-triggered mechanisms are proposed successively, which both contain two separated event-triggering conditions corresponding to the slow and the fast subsystems. The existence of a strictly positive time period between any two successive transmissions is ensured regardless of the initial states. The main difficulty of this study lies in that the state jump and parametric uncertainty appear because of the system transformation. To overcome the difficulty, the system is first modeled as an uncertain hybrid system. Then, the control gain is properly designed by solving Riccati-like equations dependent on the rough bounds of the eigenvalues of communication graph Laplacians, and a piecewise quadratic Lyapunov function is proposed with which the jump caused by the switching topology is subtly evaluated. Sufficient conditions are thus established to achieve the event-triggered synchronization. Furthermore, the results are also extended to solve the synchronization problem of the interconnected impulsive linear TTSSs. Finally, three numerical examples are provided to demonstrate the effectiveness of the proposed theoretical results.

Simultaneous State and Unknown Input Estimation for Complex Networks With Redundant Channels Under Dynamic Event-Triggered Mechanisms.

This article addresses the simultaneous state and unknown input estimation problem for a class of discrete time-varying complex networks (CNs) under redundant channels and dynamic event-triggered mechanisms (ETMs). The redundant channels, modeled by an array of mutually independent Bernoulli distributed stochastic variables, are exploited to enhance transmission reliability. For energy-saving purposes, a dynamic event-triggered transmission scheme is enforced to ensure that every sensor node sends its measurement to the corresponding estimator only when a certain condition holds. The primary objective of the investigation carried out is to construct a recursive estimator for both the state and the unknown input such that certain upper bounds on the estimation error covariances are first guaranteed and then minimized at each time instant in the presence of dynamic event-triggered strategies and redundant channels. By solving two series of recursive difference equations, the desired estimator gains are computed. Finally, an illustrative example is presented to show the usefulness of the developed estimator design method.

Leader-Following and Leaderless Consensus of Linear Multiagent Systems Under Directed Graphs by Double Dynamic Event-Triggered Mechanism

This article proposes a unified framework to investigate the leader-following and leaderless consensus problem of general linear multiagent systems under the directed communication topology only containing a spanning tree. To further reduce the use of resources for the control objective, an energy-saving control algorithm is introduced composed of double dynamic event-triggered mechanisms that work independently: one intends to control the communication of agents with their neighbors and the other to decide the update of controllers. It is shown that the control algorithm performs well compared to most existing control algorithms in terms of the communication cost between agents and the update cost of controllers. In addition, a new procedure to choose parameters is obtained by the developed Lyapunov stabilty method, with the potential of achieving less conservative parameters than related results. Finally, the effectiveness of the proposed control scheme is verified by numerical simulations.

Predefined‐time distributed event‐triggered algorithms for resource allocation

The resource allocation problem in a distributed multi-agent system is considered in this study. First, the authors develop a predefined-time distributed algorithm and analyse its convergence analysis using the Lyapunov stability theory, in which the local constraint is ensured by a differential projection operator. Thus, a predefined time is obtained by a time-varying time-based generator. Second, to reduce the communication consumption between agents, the authors develop a static as well as a dynamic-based event-triggered control scheme, where the information broadcast only occurs at some discrete time instants. Moreover, the three proposed algorithms converge precisely to the global optimal solution. Besides, the Zeno behaviour is excluded in the above static and dynamic event-triggered mechanisms. Finally, the authors test the proposed algorithms' efficiency based on the provided numerical examples.

A framework for distributed control via dynamic periodic event-triggering mechanisms

Dynamic event-triggered mechanisms (ETMs) have shown potential in further reducing the number of events in comparison to their static counterparts while delivering similar system performance. In this paper, we provide a framework to design dynamic periodic event-triggered controllers for multi-agent systems (MASs) with nonlinear dynamics. A preliminary version of this work on single-agent systems was studied in Dhullipalla et al. (2020). The design methodology adopts an emulation-based technique that assumes the existence of a continuous-time state feedback controller that stabilizes the MAS. The dynamic ETMs are constructed based on an agent’s ability or inability to sense states (or relative states) of fellow agents in the network. To illustrate the design methodologies, two case studies on nonlinear MASs (with Lipschitz and one-sided Lipschitz dynamics), interacting over undirected and directed communication networks, are presented. Finally, the results of the case studies are demonstrated via numerical examples.