Event-triggered Transmission Mechanism Research Articles

Attack-resilient fault detection for interconnected systems under DoS attack

In light of the expanding cyber-space applications, the imperative consideration of cyber-attack ramifications on system security is evident. This paper presents a resilient dynamic event-triggered fault detection scheme for a class of nonlinear interconnected systems subjected to denial of service (DoS) attacks. To counteract multifaceted threats, the co-design challenge involving switched-type fault detection filters and a resilient dynamic event-triggered transmission mechanism is addressed. In the design phase of the filters, the frequency information of the signal is considered comprehensively and linear solvable conditions ensuring desired augment system performance are delineated. Through a series of comparative simulation experiments, the findings support the conclusion that the proposed attack-tolerant fault detection mechanism not only conserves network resources but also demonstrates superior detection capabilities for specific frequency fault signals.

Event-Triggered Ultimately Bounded Filtering for Two-Dimensional Discrete-Time Systems under Hybrid Cyber Attacks

This paper investigates the design problem of event-triggered ultimately bounded filter for two-dimensional discrete systems with time-varying delays subject to hybrid cyber-attacks. A bi-directional time-sequence event-triggered transmission scheme is developed under hybrid cyber-attacks to lighten the load of network bandwidth while preserving a satisfactory filtering performance. The impact of hybrid cyber-attacks, which occur in random patterns, on the filtering performance is also taken into account. In such a framework, an augmented system model accounting for the simultaneous presence of hybrid cyber-attacks and event-triggered transmission mechanism is established with respect to two-dimensional shift-varying discrete systems with time-varying delays. On the basis of the augmented model, sufficient conditions ensuring dynamic filtering error systems exponentially ultimately bounded in the mean-square sense are obtained in virtue of Lyapunov stability theory and the linear matrix inequality technique. Then, the asymptotic upper bound of the dynamic filtering error can be specifically quantified in terms of noise variance and cyber-attacks intensity. Furthermore, criteria for simultaneously designing the weighting matrix of event-triggered scheme and the filter gain matrix are derived by minimizing the asymptotic upper bound of filtering error. Finally, the validity of proposed ultimately bounded filtering algorithm is demonstrated by utilizing an example of the industrial heating exchange process.

Adaptive Bipartite Event-Triggered Time-Varying Output Formation Tracking of Heterogeneous Linear Multi-Agent Systems Under Signed Directed Graph.

This study investigates the adaptive bipartite event-triggered time-varying output formation tracking for heterogeneous linear multi-agent systems (MASs) under signed directed communication topology. Both cooperative communication and antagonistic communication among agents are considered. The fully distributed bipartite compensator based on the novel composite event-triggered transmission mechanism is first put forward to estimate the state of the leader. Compared with the existing methods, our compensator can save communication resources using event-triggered transmission mechanism; is independent of the global information of the network graph; and is applicable for the signed directed graph. With the developed compensator, the distributed control protocol is designed to achieve the time-varying output formation tracking. Moreover, the case that the networked systems subject to external disturbances is also considered. To estimate the state of leader with disturbance, the fully distributed bipartite compensator based on an innovative composite event-triggered mechanism is presented. And the novel distributed control protocol is proposed to address the output formation tracking issue for linear MASs with heterogeneous dynamics and external disturbances. It is shown that the Zeno-behavior can be excluded in both transmission mechanisms. Finally, the effectiveness of the developed control methods is illustrated through three simulation examples.

Event-trigger-based cluster coordinated control of spacecraft swarm under switching topology

This paper investigates the cluster coordination problem of spacecraft swarms with non-negative defined directed communication topology in the context of multi-mission parallel operations. In this design, an intrinsic plasticity based echo state network is established to approximate the nonlinear term of relative attitude dynamics, and an improved event-triggered transmission mechanism is proposed to decrease the redundant communication among spacecraft. A novel cluster coordination control law under fixed non-negative topology is developed based on the echo state neural network and the event-triggered transmission mechanism. Furthermore, an extended cluster coordinated control law is proposed for the switching topology case. By using Lyapunov stability theory and graph theory, it is demonstrated that intra-cluster attitude coordination can be achieved under the proposed control laws and Zeno behavior can be excluded. Finally, numerical examples are given to illustrate the validity and superiority of our results.

The Bipartite Edge-Based Event-Triggered Output Tracking of Heterogeneous Linear Multiagent Systems.

This article focuses on the bipartite output tracking control for heterogeneous linear multiagent systems under the asynchronous edge-based event-triggered transmission mechanism. First, the distributed bipartite edge-based event-triggered compensator is established to estimate the state of the exosystem. The estimated state of the compensator is the same as the state of the exosystem in modulus and opposite in sign because of the existence of antagonistic communications. To be independent of the topology information, the adaptive compensator with an edge-based event-triggered mechanism is then established. And the observer is proposed to recover the unmeasurable system states. Then, the distributed control scheme based on the compensator and the observer is designed to address the bipartite output tracking problem. Moreover, the results in the signed fixed graph are extended to signed switching graphs. The Zeno behavior of each edge is ruled out. Finally, two numerical examples, one application example and one comparison example, are given to demonstrate the feasibility of the main theoretical findings.

Dynamic Event-Triggered State Estimation for Markov Jump Neural Networks With Partially Unknown Probabilities.

This article focuses on the investigation of finite-time dissipative state estimation for Markov jump neural networks. First, in view of the subsistent phenomenon that the state estimator cannot capture the system modes synchronously, the hidden Markov model with partly unknown probabilities is introduced in this article to describe such asynchronization constraint. For the upper limit of network bandwidth and computing resources, a novel dynamic event-triggered transmission mechanism, whose threshold parameter is constructed as an adjustable diagonal matrix, is set between the estimator and the original system to avoid data collision and save energy. Then, with the assistance of Lyapunov techniques, an event-based asynchronous state estimator is designed to ensure that the resulting system is finite-time bounded with a prescribed dissipation performance index. Ultimately, the effectiveness of the proposed estimator design approach combining with a dynamic event-triggered transmission mechanism is demonstrated by a numerical example.

Zonotopic set-membership state estimation for multirate systems with dynamic event-triggered mechanisms

This paper is concerned with the dynamic event-triggered set-membership state estimation issue for a class of multirate networked systems subject to unknown but bounded disturbance and noise. To economize the limited communication resource when performing the desired networked estimation task, a dynamic event-triggered transmission mechanism is proposed to significantly reduce the frequency of sensor data transmissions. A key issue of the addressed problem is to construct a zonotopic outer approximation set to bound the set of states that are consistent with the disturbed system model and the noisy measured outputs. With the help of a prediction-update-correction method, we design a zonotope that is guaranteed to enclose all possible system states due to the unknown disturbance and noise. Then, the size of such a zonotope is minimized by properly designing an introduced correlation matrix at each iteration. Subsequently, the estimation performance is analyzed under the developed zonotopic estimation method. A sufficient condition is further established to ensure the boundedness of the size of the obtained zonotope. Finally, an illustrative example is provided to demonstrate the effectiveness of the proposed method.

Finite-time asynchronous control of discrete-time switched linear systems with event-triggered H∞ filtering

In this paper, the event-triggered H∞ filtering problem for discrete-time linear switched systems is investigated under asynchronous switching. Under the mode-dependent event-triggered transmission mechanism (METM), the switching signal and filtering signal are combined into an augmented switching signal by merging signal technology, and the switched system and filtering system are modeled as a filtering error system (FES). Because the switching signal of the filtering system is determined by METM, there will be an asynchronous switching phenomenon between the switched system and filtering system. The novel sufficient conditions are given to ensure that the FES is finite-time bounded (FTB) and has a specified H∞ performance by the average dwell time (ADT) and multi-Lyapunov functional method. And based on this, the design method of the H∞ filter is given. Ultimately, the numerical examples are inspired to manifest the availability of the effects in the study.

Control of connected vehicles with event-triggered transmission and prescribed energy budget

This paper is concerned with event-triggered cooperative control of a platoon of connected vehicles via vehicular ad hoc networks (VANETs). To reduce communications among vehicles, we introduce a hybrid event-triggered transmission mechanism based on both time elapsed and state error. The effect of time-varying transmission delay and communication energy constraint can be also taken into account in the system modeling and design procedures. The on-board sensors use different power levels to transmit information resulting in different packet loss rates. The vehicular platoon system is proved to be exponentially mean-square stable under the hybrid event-triggering scheme and a constant time headway spacing policy. A framework for co-design of the hybrid event triggering scheme and the output feedback controller is given to guarantee platoon stability and spacing-error convergence along the stream. Numerical simulations are given to demonstrate the effectiveness of proposed method.

Distributed Auxiliary Particle Filtering With Diffusion Strategy for Target Tracking: A Dynamic Event-Triggered Approach

This paper investigates the particle filtering problem for a class of nonlinear/non-Gaussian systems under the dynamic event-triggered protocol. In order to avert frequent data transmission and reduce the communication overhead, a dynamic event-triggered transmission mechanism is adopted to decide whether the data should be transmitted or not. We first consider a scenario where all sensor nodes selectively transmit their newly obtained measurements to a central node, and a full likelihood function at the central node is derived by fusing the transmitted measurements and the information embodied in the non-triggered measurements. Based on the derived full likelihood function, a centralized auxiliary particle filtering algorithm is proposed to select those particles that are more likely to match the current measurement information. Next, based on the diffusion strategy, a distributed auxiliary particle filtering algorithm is further developed, where the local measurements and the local posteriors (approximated by the Gaussian mixture models) are exchanged among neighboring nodes under the dynamic event-triggered communication strategy. Finally, the effectiveness of the proposed filtering schemes is demonstrated via Monte Carlo simulations in a target tracking problem with received-signal-strength sensors.

Sliding mode control for uncertain active vehicle suspension systems: an event-triggered $$\varvec{\mathcal {H}}_{\infty }$$ control scheme

This paper considers the event-triggered sliding mode control problem of uncertain active vehicle suspension systems. A more comprehensive polytope approach is employed to model the uncertainties which generally exist in the sprung and unsprung masses. Moreover, the corresponding mathematical model is constructed for the quarter-vehicle active suspension system. Meanwhile, the event-triggered transmission mechanism is taken into account to schedule communication and save bandwidth. The main purpose of this paper is to develop a proper sliding mode controller which can guarantee the asymptotic stability and $$\mathcal {H}_{\infty }$$ performance for the suspension system with some constraints. By means of convex optimization technique, some sufficient conditions are derived to assure the constructed event-triggered sliding mode control law can not only ensure the corresponding sliding mode dynamics are asymptotically stable but also the predefined switching surface is reachable. Finally, the feasibility of the designed method is verified by a simulation example.

Event-triggered fault tolerant control for a class of state saturated systems subject to deception attacks

This study is concerned with event-triggered fault tolerant control for a class of state-saturated systems subject to stochastic faults, unknown but bounded disturbances and deception attacks. After formulating stochastic faults, state saturations, and deception attacks, a hybrid system model is developed to facilitate analysis and design. An event-triggered transmission mechanism is proposed to decide whether the measurement values should be sent to the controller via wireless network. Since the sensor data is attacked by the adversary during the transmission, the actual measurement value received by the controller needs to be recalculated. Expressions of the dynamic output feedback controller are presented, and criteria are used to design a dynamic feedback controller to ensure that the system is uniformly ultimately bounded. To deal with the current problem, an algorithm is also developed and tested using the linear matrix inequality (LMI) toolbox. Finally, two numerical examples are given to illustrate the validity and effectiveness of the proposed strategies.

Resilient Distributed Event-Triggered Control of Vehicle Platooning Under DoS Attacks

This paper deals with the resilient distributed control of a platoon of automated vehicles under DoS attacks. First, an event-triggered transmission mechanism resilient to energy-limited DoS attacks is proposed to save the possible bandwidth of vehicle-to-vehicle communication channels. A consensus-based distributed control strategy, which is implemented on each vehicle, is developed to account for the information interaction of leading and following vehicles under the proposed resilient event-triggered data transmission mechanism. Second, an attack-tolerant performance index with certain resilience level is put forward in such a way to achieve resilience evaluation of the vehicular platoon system. Third, sufficient conditions are derived for ensuring the consensus of the resulting vehicular platoon system while preserving the prescribed resilience performance requirement. Furthermore, a co-design criterion for determining the distributed platoon controller and triggering condition is presented. Finally, simulation under a predecessor-leader following topology is presented for validation of the obtained results.

The distributed output consensus control of linear heterogeneous multi-agent systems based on event-triggered transmission mechanism under directed topology

In this paper, the distributed output consensus control of linear heterogeneous multi-agent systems (LH_MASs) based on event-triggered transmission strategy under directed topology is addressed. Under the assumption that the state of each node is unmeasurable, the state observer is first designed. Then, in order to decrease the information interaction among agents, the event-triggered consensus protocol (ETCP) based on the designed state observer and dynamic compensator is proposed. Here, a novel triggering function is designed such that each agent does not need to monitor the states of its neighbors for event detection. In addition, on account of state-dependent threshold and time-dependent threshold, the combined threshold is presented to take both advantages into account at the same time. By selecting an appropriate Lyapunov function and regulation parameters, the output consensus of LH_MASs can be achieved and the Zeno behavior can be excluded. To avoid continuous monitoring of each agent’s state, we further propose the self-triggered consensus protocol (STCP). Furthermore, the novel event-triggered consensus protocol for mitigating external disturbances (ED_ETCP) is raised. Finally, several numerical examples are provided to illustrate the validity of the theoretical results.

Distributed bipartite leader-following consensus of linear multi-agent systems with input time delay based on event-triggered transmission mechanism

This study focuses on the distributed bipartite consensus tracking for linear multi-agent systems with input time delay based upon event-triggered transmission mechanism. Both cooperative interaction and antagonistic interaction between neighbor agents are considered. A novel distributed bipartite control technique with event-triggered mechanism is raised to address this consensus issue. Different from the existing methods, our control technique does not need continuous communication among agents, is capable of addressing the case of input delay, and is applicable for the signed communication topology. Moreover, to avoid continuous monitoring of one’s own state, a self-triggered control strategy is further proposed. And when the system states cannot be measured, the observer-based bipartite control technique with event-triggered mechanism is thus put forward. Furthermore, the results in leader-following consensus are extended to containment control. It is proven that the proposed controllers fulfill the exclusion of Zeno behavior in two consensus problems. Finally, simulation experiments are used to test the practicability of the theoretical analysis.

Event-triggered dissipative control for networked stochastic systems under non-uniform sampling

This paper is concerned with dissipative control for networked stochastic systems with an event-triggered transmission mechanism. Different from some existing results, signals from a physical plant are sampled non-uniformly. In order to save precious communication resources, a data-packet processor is introduced to choose necessary data-packets based on a flexible event-triggered condition. By establishing an integral inequality in stochastic setting, which is regarded as a counterpart of Wirtinger-based inequality, a criterion is derived such that the resultant closed-loop system is mean-square stable and dissipative. This criterion is then used to calculate suitable event-triggered controllers in terms of solutions to linear matrix inequalities. An air vehicle system is finally taken to substantiate the validity of the proposed method.

Event-based filtering for time-varying nonlinear systems subject to multiple missing measurements with uncertain missing probabilities

This paper is concerned with the recursive filtering problem for a class of time-varying nonlinear stochastic systems in the presence of event-triggered transmissions and multiple missing measurements with uncertain missing probabilities. The measurements from different sensors may undergo the missing phenomena, which are characterized by a set of mutually independent Bernoulli random variables and the missing probabilities could be uncertain. In addition, the event-triggered transmission mechanism is introduced to reduce the network communication burden, where the current measurement is transmitted to the remote filter only when it changes greatly compared with the previous one. The aim of this paper is to design a time-varying filter such that, in the presence of the multiple missing measurements, event-triggered transmission mechanism and stochastic nonlinearities, an upper bound of the filtering error covariance is obtained and then minimized by properly designing the filter gain. The explicit form of the filter gain is given in terms of the solutions to two recursive matrix equations. It is shown that the developed filtering scheme is of a recursive form applicable for the online computations. Finally, we provide two illustrative examples to demonstrate the feasibility and applicability of the developed event-triggered filtering scheme.

Finite-Time State Estimation for Recurrent Delayed Neural Networks With Component-Based Event-Triggering Protocol

This paper deals with the event-based finite-time state estimation problem for a class of discrete-time stochastic neural networks with mixed discrete and distributed time delays. In order to mitigate the burden of data communication, a general component-based event-triggered transmission mechanism is proposed to determine whether the measurement output should be released to the estimator at certain time-point according to a specific triggering condition. A new concept of finite-time boundedness in the mean square is put forward to quantify the estimation performance by introducing a settling-like time function. The objective of the addressed problem is to construct an event-based state estimator to estimate the neuron states such that, in the presence of both mixed time delays and external noise disturbances, the dynamics of the estimation error is finite-time bounded in the mean square with a prescribed error upper bound. Sufficient conditions are established, via stochastic analysis techniques, to guarantee the desired estimation performance. By solving an optimization problem with some inequality constraints, the explicit expression of the estimator gain matrix is characterized to minimize the settling-like time. Finally, a numerical simulation example is exploited to demonstrate the effectiveness of the proposed estimator design scheme.

Event-triggered H∞ filtering for discrete-time systems over unreliable networks with package dropouts

This paper considers the event-triggered H∞ filtering problem for discrete-time linear systems subject to package dropouts. The system measurement output is quantized before transmitted through the communication network with limited capacity. The event-triggered transmission mechanism is proposed and Bernoulli process is utilized to describe the package dropouts. A new sufficient condition is obtained such that the resulted error system is mean square exponentially stable and the filtering error satisfies the prescribed H∞ performance. Then, co-design algorithm for the parameters of filter and event-triggered condition is presented by means of linear matrix inequality approach. Moreover, the minimum attenuation level bound can be obtained by solving a convex optimization problem. Numerical simulations show the effectiveness and less conservativeness of the proposed approach.

Event-triggered networked predictive control of system with data loss

This paper investigates the problem of event-triggered networked predictive control for systems with data loss. An event-triggered networked predictive control system is proposed. Based on predictive control model, a data loss compensation strategy is presented and an extended event-triggered transmission mechanism is developed. The closed-loop event-triggered predictive control system is described as a switched system and sufficient closed-loop stability conditions related to event-triggered mechanism are established. Under the event-triggered networked predictive control scheme, the consumption of the communication resources is reduced. Finally, an example is provided to illustrate the effectiveness of the proposed method.