Event-triggered Communication Scheme Research Articles

Adaptive Event-Triggering ${H}_{\infty }$ Load Frequency Control for Network-Based Power Systems

Load frequency control (LFC) is a very important method to keep the power systems stable and secure. However, due to the introduction of communication networks in multi-area power systems, the traditional LFC method is not effective again. This motivates us to investigate an adaptive event-triggering ${H}_{\infty }$ LFC scheme for multi-area power systems. Compared with the existing time-invariant event-triggering communication scheme, an adaptive event-triggering communication scheme is presented, where the event-triggering threshold can be dynamically adjusted to save more limited network resources, while preserving the desired control performance. Compared with the existing emulation-based method, where the controller must be known a priori , the stability and stabilization criteria derived in this work can provide a tradeoff to balance the required communication resources and the desired control performance. The effectiveness of the proposed method is verified by two numerical examples.

A Threshold-Parameter-Dependent Approach to Designing Distributed Event-Triggered H∞ Consensus Filters Over Sensor Networks.

This paper is concerned with distributed event-triggered H∞ consensus filtering for a discrete-time linear system over a sensor network. Different from some existing event-triggered communication schemes (ETCSs), a new distributed ETCS is first developed to reduce the communication frequency of neighboring sensors, where the threshold parameter in an event triggering condition is time-varying with attainable upper and lower bounds. Then a threshold-parameter-dependent approach is proposed to derive criteria for designing the desired H∞ consensus filters and the ETCS such that the resultant filtering error system is asymptotically stable with the prescribed H∞ performance while maintaining satisfactory resource efficiency. Furthermore, a polytope-like transformation with regard to time-varying threshold parameters is performed and a recursive algorithm is presented to determine the threshold-parameter-dependent filter matrix sequences and event triggering weighting matrix sequence. Two illustrative examples are employed to show the effectiveness of the developed approach.

Event-triggered [formula omitted] filter design for Markovian jump systems with quantization

The problem of event-triggered H∞ filter design for Markov jump systems with output quantization is investigated in this paper. A dynamic event-triggered communication scheme is introduced to detect whether or not transmit the newly sampled data to the quantizer for different jumping modes. Time interval analysis approach is used to unify the Markov jump system, the event-triggered scheme and network-induced delays into a new Markov jump filtering error system with time-delay. Based on reciprocally convex approach, conditions are derived to guarantee the networked Markov jump system mean-square stable with an H∞ norm bound. The correspondent H∞ filter and the event-triggered parameters are also co-designed. Two numerical examples are given to show the effectiveness and better performance of the proposed analysis and design techniques.

Event-Triggered Dynamic Output Feedback Control for Networked T-S Fuzzy Systems With Asynchronous Premise Variables

This paper investigates the problem of event-triggered dynamic output feedback control for networked Takagi-Sugeno (T-S) fuzzy systems with asynchronous premise variables. Our attention is focused on the design of an event-triggered fuzzy dynamic output feedback controller that guarantees that the closed-loop system is asymptotically stable. First, with consideration of the limited bandwidth and restricted network resource, an event-triggered communication scheme is proposed to save the limited network resource. Second, different from the existing results in the literature, a fuzzy dynamic output feedback controller with asynchronous premise variables is constructed such that the premise membership functions are not necessarily the same as the networked T-S fuzzy systems. Then, by using the Wirtinger inequality and some slack matrices, a less conservative stability and stabilization conditions for the existence of fuzzy dynamic output feedback controller are derived in the form of linear matrix inequalities, which can be solved by the standard MATLAB toolbox. Finally, an example is given to demonstrate the effectiveness of the designed event-triggered fuzzy dynamic output feedback controller.

Distributed Event-Triggered Communication and Control of Linear Multiagent Systems Under Tactile Communication

This note is concerned with the consensus of linear multiagent systems under tactile communication. Motivated by the emerging tactile communication technology where extremely low latency has to be supported, a distributed event-triggered communication and control scheme is proposed for the data reduction of each agent. First, an event-triggered data reduction scheme is designed for the communication between neighbors. Under such a communication scheme, a distributed event-triggered output feedback controller is further implemented for each agent, which is updated asynchronously with the communication action. It is proven that the consensus of the underlying multiagent systems is achieved asymptotically. Furthermore, it is shown that the proposed communication and control strategy fulfils the reduction of both the frequency of communication and controller updates as well as excluding Zeno behavior. A numerical example is given to illustrate the effectiveness of the proposed control strategy.

Distributed event-triggered algorithm for optimal resource allocation of multi-agent systems

This paper is concerned with solving the distributed resource allocation optimization problem by multi-agent systems over undirected graphs. The optimization objective function is a sum of local cost functions associated to individual agents, and the optimization variable satisfies a global network resource constraint. The local cost function and the network resource are the private data for each agent, which are not shared with others. A novel gradient-based continuous-time algorithm is proposed to solve the distributed optimization problem. We take an event-triggered communication strategy and an event-triggered gradient measurement strategy into account in the algorithm. With strongly convex cost functions and locally Lipschitz gradients, we show that the agents can find the optimal solution by the proposed algorithm with exponential convergence rate, based on the construction of a suitable Lyapunov function. Finally, a numerical example is provided to demonstrate the effectiveness of the proposed scheme.

On designing of an adaptive event-triggered communication scheme for nonlinear networked interconnected control systems

This paper is concerned with the design of adaptive event-triggered scheme for networked nonlinear interconnected systems via T-S fuzzy models. The transmission of control signals is based on a novel adaptive event-triggered communication scheme, where the adaptive threshold is dependent on a dynamic error of the system rather than a predetermined constant as the one in the existing results. The amount of the releasing data is regulated with the adaptive threshold that plays a crucial role in decision of whether releasing the sampled data or not. This technique reflects an inherent dynamic balance between the control performance and the utilization of the network resource. A corresponding Lyapunov function is constructed to achieve sufficient conditions of stability and stabilization. Finally, a simulation example is given to show the effectiveness of the proposed theoretic results.

Event-Triggered State Estimation for Delayed Stochastic Memristive Neural Networks With Missing Measurements: The Discrete Time Case.

In this paper, the event-triggered state estimation problem is investigated for a class of discrete-time stochastic memristive neural networks (DSMNNs) with time-varying delays and missing measurements. The DSMNN is subject to both the additive deterministic disturbances and the multiplicative stochastic noises. The missing measurements are governed by a sequence of random variables obeying the Bernoulli distribution. For the purpose of energy saving, an event-triggered communication scheme is used for DSMNNs to determine whether the measurement output is transmitted to the estimator or not. The problem addressed is to design an event-triggered estimator such that the dynamics of the estimation error is exponentially mean-square stable and the prespecified disturbance rejection attenuation level is also guaranteed. By utilizing a Lyapunov-Krasovskii functional and stochastic analysis techniques, sufficient conditions are derived to guarantee the existence of the desired estimator, and then, the estimator gains are characterized in terms of the solution to certain matrix inequalities. Finally, a numerical example is used to demonstrate the usefulness of the proposed event-triggered state estimation scheme.

Resilient Event-Triggering $H_{\infty }$ Load Frequency Control for Multi-Area Power Systems With Energy-Limited DoS Attacks

This paper investigates a resilient event-triggering H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</sub> load frequency control (LFC) for multi-area power systems with energy-limited Denial-of-Service (DoS) attacks. The LFC design specifically takes the presence of DoS attacks into account. First, an area control error dependent time delay model is delicately constructed for multi-area closed-loop power systems. Second, a resilient event-triggering communication (RETC) scheme is well designed, which allows a degree of packet losses induced by DoS attacks and has the advantage of improving the transaction efficiency. Then, by using the Lyapunov theory, two stability and stabilization criteria for the multi-area power systems are derived under consideration of the energy-limited DoS attacks. In these criteria, the relationship between the allowable DoS attack duration and the resilient event-triggering communication parameters are clearly revealed. Moreover, an algorithm is also provided to obtain the RETC parameters and the LFC gains simultaneously. Finally, a case study shows the effectiveness of the proposed method.

Network-based fuzzy H∞ controller design for T-S fuzzy systems via a new event-triggered communication scheme

This paper is concerned with network-based fuzzy H∞ control for a class of T-S fuzzy systems under an event-triggered communication scheme. Considering the fact that the network-induced delays are inevitable, the closed-loop system is modeled with asynchronous membership functions. To deal with the asynchronous membership functions, a new condition that limits the deviation bounds of membership functions, is introduced into the event-triggered scheme (ET scheme). Besides, by introducing a fuzzy event-triggered weighting matrix, a new ET scheme is proposed. Based on the proposed ET scheme, a novel criterion for the asymptotic stability and H∞ performance analysis is established in terms of linear matrix inequalities. Then some sufficient conditions and an algorithm to co-design the controller and the parameters of the ET scheme are presented. The effectiveness of the proposed method is illustrated through a mass-spring-damper system.

Event-based H∞ filtering for discrete-time Markov jump systems with network-induced delay

The problem of event-based H∞ filtering for discrete-time Markov jump system with network-induced delay is investigated in this paper. For different jumping modes, different event-triggered communication schemes are constructed to choose which output signals should be transmitted. Through the analysis of network-induced delay’s intervals, the discrete-time system, the event-triggered scheme and network-induced delay are unified into a discrete-time Markov jump filter error system with time-delay. Based on time-delay system analysis method, criteria are derived to guarantee the discrete-time Markov jump error system stochastically stable with an H∞ norm bound. The correspondent filter and the event-based parameters are also given. A numerical example is given to show that the proposed filter design techniques are effective and event-triggered communication scheme can save limited network resources greatly.

Enabling resilient distributed power sharing in networked microgrids through software defined networking

Networked Microgrids (NMGs) offer a new, more resilient alternative to traditional individual Microgrids (MGs). Even though networking existing microgrids presents clear advantages, the scalable and resilient communication and control infrastructure necessary for supporting this innovation does not yet exist. This paper addresses this challenge by developing a Software-Defined Networking (SDN) enabled architecture that can achieve fast power support among microgrids, transforming isolated local microgrids into integrated NMGs capable of achieving the desired resiliency, elasticity and efficiency. Equipped with a novel event-triggered communication scheme, the SDN-based architecture enables distributed power sharing among microgrids in both the transient period and the steady state, a capability that is unattainable using existing technologies. Extensive experiments on a cyber-physical Hardware-in-the-Loop (HIL) NMGs testbed have validated the effectiveness and efficiency of the SDN-enabled distributed power sharing method.

Adaptive event-triggered control of a class of nonlinear networked systems

This paper investigates an adaptive event-triggered communication scheme (AETCS) for a class of networked Takagi–Sugeno (T–S) fuzzy control systems. The threshold of event-triggering condition has great influence on the maximum allowable number of successive packet losses. Different from the conventional method, the threshold, in this study, is dependent on a novel adaptive law which can be achieved on-line rather than a predefined constant, since the threshold with fixed value is hard to suit the variation of the system. The stability and stabilization criteria are derived by using a new Lyapunov function. Finally, an example is provided to demonstrate the design method.

Hybrid Active-Passive Robust Fault-Tolerant Control of Event-Triggered Nonlinear NCS

In this paper, the authors aimed to analyze uncertain nonlinear networked control systems (NCS) under discrete event-triggered communication scheme (DETCS), in which an integrated design methodology between robust fault detection observer and active fault-tolerant controller is proposed. Moreover, the problem of hybrid active–passive robust fault-tolerant control, which integrated passive fault-tolerant control, fault detection, and controller reconstruction, is researched. In consideration of the impact of uncertainties and network-induced delay on system performance, a new class of uncertain nonlinear NCS fault model is established based on T-S fuzzy model. By employing Lyapunov stability theory,H∞control theory, and linear matrix inequality method, the fault detection observer and hybrid fault-tolerant controller are both appropriately designed. In addition, the sufficient condition that guaranteed the asymptotically robust stability of nonlinear NCS against any actuator failures is deduced. Finally, a numerical simulation is provided to show the effectiveness of the proposed methods.

H∞ tracking control of nonlinear networked systems with a novel adaptive event-triggered communication scheme

This study investigates the problem of tracking control of nonlinear fuzzy systems with a limited network communication. A new adaptive event-triggered data transmission scheme is proposed to save the limited network bandwidth. The threshold of event-triggered condition has a great influence on the rate of data releasing. Different from the conventional method by presetting the threshold as a fixed value, the threshold, in this study, is regulated by the error state of nonlinear systems and the reference model adaptively, which denotes that the rate of data releasing is followed by the external variation. By constructing a proper Lyapunov function with consideration of the proposed adaptive event-triggering condition, an off-line co-design method to achieve the fuzzy controller gains and the parameters of event-triggering condition is developed. An example of Duffing forced-oscillation system with the limited network communication tracking the states of linear reference model is applied to demonstrate the effectiveness of the proposed method.

Network-based H∞ control for T–S fuzzy systems with an adaptive event-triggered communication scheme

This paper extends the prior work of discrete event-triggered communication for networked T–S fuzzy systems, where the next communication instant is determined by an event-triggered communication scheme with a constant threshold and the premises of T–S fuzzy systems and parallel distribution compensation (PDC) fuzzy control rules are synchronous. Firstly, an adaptive event-triggered communication scheme for NCSs is presented, which relaxes the assumption of the prior work that the threshold of event-triggered communication scheme is a preselected constant. Secondly, an asynchronous premise re-construct method for networked T–S fuzzy systems is proposed, which relaxes the assumption of the prior work that the premises of the plant and the PDC control rules are synchronous. We show that the number of transmitted packets generated by the proposed scheme is smaller than those generated by some existing ones while ensuring the desired control performance. Simulations show the effectiveness of the proposed method.

Distributed optimisation design with triggers for disturbed continuous‐time multi‐agent systems

A distributed optimisation problem is investigated for disturbed continuous-time multi-agent systems with discrete-time communication and gradient measurement. First, a distributed optimisation algorithm with time-triggered communication and gradient measurement is proposed. Then an event-triggered communication strategy and an event-triggered gradient measurement strategy are developed, and a distributed optimisation algorithm combining these two event-triggered strategies is designed, in which the two event-triggered strategies are free of Zeno behaviour. Moreover, the exponential convergence of system can be guaranteed by using the internal model design to reject the external disturbance. Finally, an example illustrates the effectiveness of the proposed algorithms.

Event-triggered distributed state estimation for a class of time-varying systems over sensor networks with redundant channels

This paper is concerned with the distributed state estimation problem for a class of time-varying systems over sensor networks. An event-triggered communication scheme is utilized to save the constrained computation resource and network bandwidth while preserving the desired performance. The measurements on each node are transmitted to the estimators only when a certain triggering condition is satisfied. Moreover, in order to improve the reliability of data transmission services, we exploit redundant communication channels during the transmission process. The purpose of this paper is to design a set of time-varying state estimators such that the dynamics of the state estimation error satisfies the average H∞ performance constraints. The specific gains of the estimator can be obtained by calculating a series of recursive linear matrix inequalities (RLMIs). Finally, a simulation example is presented to show the effectiveness of the state estimation method proposed in this paper.

Event-triggered dissipative filtering for networked semi-Markov jump systems and its applications in a mass-spring system model

This paper investigates the event-triggered dissipative filtering problem for a class of networked semi-Markov jump systems. As a first attempt, the event-triggered communication scheme is introduced to save the limited network bandwidth and preserve the fixed system performance. By using the stochastic analysis, the information on the sojourn time between the mode jumps of the underlying systems is fully considered. By employing time-delay approach, the filtering performance analysis for the considered systems is presented, and then a co-design approach for the event-triggered mechanism and the dissipative filter is adopted such that the filtering error system is strictly dissipative. Finally, a numerical comparative example is used and a mass-spring system model as a realistic example is also provided to show the reduced conservatism and applicability of the proposed filtering scheme.

Distributed learning for feedforward neural networks with random weights using an event-triggered communication scheme

This paper aims to develop distributed learning algorithms for feedforward neural networks with random weights (FNNRWs) by using event-triggered communication schemes. Based on this scheme, the communication process of each agent is driven by a trigger condition where the agents exchange information in an asynchronous manner, only when it is crucially required. To this end, the centralized FNNRW problem is cast as a set of distributed subproblems with consensus constrains imposed on the desired parameters and solved following the discrete-time zero-gradient-sum (ZGS) strategy. An event-triggered communication scheme is introduced to the ZGS-based FNNRW algorithm in order to avoid unnecessary transmission costs. This is particularly useful for the case when communication resource is limited. It is proved that the proposed event-triggered approach is exponentially convergent if the design parameter is chosen properly under strongly connected and weight-balanced agent interactions. Two numerical simulation examples are provided to verify the effectiveness of the proposed algorithm.