Event-Triggering State and Fault Estimation for a Class of Nonlinear Systems Subject to Sensor Saturations.

This paper is concerned with the state and fault estimation issue for nonlinear systems with sensor saturations and fault signals. For the sake of avoiding the communication burden, an event-triggering protocol is utilized to govern the transmission frequency of the measurements from the sensor to its corresponding recursive estimator. Under the event-triggering mechanism (ETM), the current transmission is released only when the relative error of measurements is bigger than a prescribed threshold. The objective of this paper is to design an event-triggering recursive state and fault estimator such that the estimation error covariances for the state and fault are both guaranteed with upper bounds and subsequently derive the gain matrices minimizing such upper bounds, relying on the solutions to a set of difference equations. Finally, two experimental examples are given to validate the effectiveness of the designed algorithm.

Resilient distributed optimization for cyber-physical systems under adversarial environments: An event-based method.

This work presents a resilient distributed optimization algorithm based on the event-triggering mechanism for cyber-physical systems (CPSs) to optimize an average of convex cost functions corresponding to multiple agents under adversarial environments. Two attack scenarios, including the f-total (each agent is affected by at most f malicious agents in the whole network) and the f-local (each agent is affected by at most f malicious agents in its in-neighbor set) attacks are considered. Subsequently, the convergence conditions under these two attack scenarios are provided, respectively, both of which guarantee that the state values of benign agents converge to a bounded error range. The optimality conditions are also presented by theoretical analysis, which guarantee that the state values of benign agents converge to a safety interval constructed by local optimal values under certain graph conditions, despite the misbehavior of malicious agents. In addition, four numerical examples are presented to show the effectiveness and superiority of the event-triggering resilient distributed optimization (RDO-E) algorithm. Compared to existing resilient algorithms, the proposed method achieves resilient distributed optimization with higher accuracy and less demanding communication overheads. Finally, by applying the proposed method to the multi-microgrid system, a resilient economic dispatch problem (REDP) is successfully solved, which validates the practical viability of the RDO-E algorithm.

Sampled-based adaptive event-triggered resilient control for multiagent systems with hybrid cyber-attacks.

The multiagent systems have shared broad application in many practical systems including unmanned aircraft clusters, intelligent robots, and intelligent transportation. However, many unexpected cyber-attacks may disturb or disrupt the normal communication of the agents, thus reducing the interacting efficiency of multiagent systems. Ever since the cyber-attacks have been proposed, the resilient control problem for multiagent systems has been intensively explored in light of the communication network growth. However, most of the consequences only focused on denial-of-service (DoS) attacks or deception attacks independently. Distinguished from the existing resilient control mechanisms, the current investigation represents the first attempt at designing an adaptive resilient controller for multiagent systems according to the sampled-based adaptive event-triggered manner, where denial-of-service (DoS) attacks and deception attacks are both considered. First, the hybrid cyber-attacks model and its impact on the closed-loop system are addressed. And then, an adaptive event-triggered strategy is proposed to reduce network resource consumption and ease the communication burden, where the designed adaptive law can automatically adjust the triggering threshold. Finally, the consensus state of multiagent systems is capable of achieving via a series of reasonable control rules formulated through Lyapunov functional approach despite suffering hybrid cyber-attacks. And a simulation example is given to substantiate the feasibility of the proposed method.

Event-triggered aperiodic intermittent control for linear time-varying systems.

This paper considers the aperiodic intermittent control (AIC) for linear time-varying systems (LTVSs), where the occurrence instants are determined by an event triggering mechanism based on Lyapunov functions. For LTVSs, most of the existing results are demanded that the feedback controls are exerted all the time. In fact, in many practical applications, the applied controls are unnecessary/impossible to be imposed all the time. Therefore, the event-triggered AIC is introduced in this paper for LTVSs, and the uniformly stability, globally asymptotic stability and finite-time stability are proposed for LTVSs with event-triggered AIC, respectively. In addition, by using the piecewise constant feedback control method, effective intermittent controllers are designed for LTVSs. Finally, we present two numerical examples to illustrate the efficacy of the derived results.

Dynamic event-triggered distributed observer for linear systems.

This paper investigates a dynamic event-triggered distributed observer for linear systems including two groups of local observers: one can access the local outputs and another cannot. By the detectability decomposition, the proposed observer contains a detectable sub-state (DSS) observer and a distributed undetectable sub-state (USS) observer. The dynamic event-triggered mechanism (DETM) for the outputs only requires a copy of the DSS observer with low dimension. Besides, only the USS estimate is transmitted to the neighbors which can reduce the communication burden. Positive minimum inter-event times are prescribed previously in the DETMs, and piece-wise dynamics for internal dynamic variables are employed. By modeling the error systems as hybrid systems, the exponential stability of the proposed observer is assured with the utilization of time-triggering method and event-triggering method.

Aperiodically intermittent event-triggered pinning control on cluster synchronization of directed complex networks.

This paper is dedicated to investigating the exponential cluster synchronization in a class of nonlinearly coupled complex networks with non-identical nodes and an asymmetrical coupling matrix. A novel aperiodically intermittent pinning control (APIPC) protocol is presented, which takes full account of the cluster-tree topology structure of the networks and pins only the nodes in the current cluster that have directional links to neighboring clusters. Since it is difficult to precisely determine the intermittent control instants and rest instants of APIPC in advance, the event-triggered mechanism (ETM) is thus proposed. Based on the concept of the minimal control ratio and the segmentation analysis method, sufficient requirements for realizing the exponential cluster synchronization are derived. Moreover, the Zeno behavior of ETM is excluded by rigorous analysis. Eventually, the effectiveness and advantages of the established theorems and control strategies are demonstrated by two numerical simulations.

Dynamic event-based state estimation for delayed artificial neural networks with multiplicative noises: A gain-scheduled approach.

This study is concerned with the state estimation issue for a kind of delayed artificial neural networks with multiplicative noises. The occurrence of the time delay is in a random way that is modeled by a Bernoulli distributed stochastic variable whose occurrence probability is time-varying and confined within a given interval. A gain-scheduled approach is proposed for the estimator design to accommodate the time-varying nature of the occurrence probability. For the sake of utilizing the communication resource as efficiently as possible, a dynamic event triggering mechanism is put forward to orchestrate the data delivery from the sensor to the estimator. Sufficient conditions are established to ensure that, in the simultaneous presence of the external noises, the randomly occurring time delays with time-varying occurrence probability as well as the dynamic event triggering communication protocol, the estimation error is exponentially ultimately bounded in the mean square. Moreover, the estimator gain matrices are explicitly calculated in terms of the solution to certain easy-to-solve matrix inequalities. Simulation examples are provided to show the validity of the proposed state estimation method.

Bipartite output consensus of heterogeneous linear multi-agent systems by dynamic triggering observer.

In this paper, the bipartite output consensus problem of heterogeneous linear multi-agent systems (HL-MASs) is investigated. Compared with related works, both the cooperative interactions and antagonistic interactions between agents exist. First, a novel distributed dynamic triggering observer is designed to recover the leader's state. In order to avoid verifying the dynamic triggering condition continuously, a monitoring scheme is proposed. Then, the communication cost can be reduced. Next, we solve the bipartite output consensus problem of HL-MASs by both state feedback and output feedback control laws. Moreover, detailed analysis on the dynamic event triggering condition is conducted. It is shown that the lower bound of inter-execution time is larger than zero, so Zeno behaviour is excluded. The choice of parameters involved in the dynamic event triggering condition is also analysed. Finally, examples are given for demonstration.