Event-triggered Control Scheme Research Articles

Event-triggered Intelligent Control Scheme for Data Integrity Attack Mitigation in Multi-area Power Grids

Event-triggered Intelligent Control Scheme for Data Integrity Attack Mitigation in Multi-area Power Grids

Hybrid Event-Triggered and Impulsive Security Consensus Control Strategy for Fractional-Order Multiagent Systems With Cyber Attacks

Hybrid Event-Triggered and Impulsive Security Consensus Control Strategy for Fractional-Order Multiagent Systems With Cyber Attacks

The dynamics and control of a multi-lingual rumor propagation model with non-smooth inhibition mechanism.

In this paper, the dynamic behaviors and control strategies of a rumor propagation model are studied in multi-lingual environment. First, an S2E2I2R rumor propagation model is proposed, which incorporates a non-smooth inhibition mechanism. Meanwhile, the existence and stability of the equilibrium are analyzed, grounded in the spreader threshold of the government intervention. Finally, the optimal control and the event-triggered impulsive control strategies are proposed to mitigate the spread of rumors, and the comparison of their effectiveness is further presented by the numerical simulation and a practical case.

Event-triggered fixed/preassigned time stabilization of state-dependent switching neural networks with mixed time delays

<abstract><p>This study employed an event-triggered control (ETC) strategy to investigate the problems of fixed-time stabilization (FTS) and preassigned-time stabilization (PTS) for state-dependent switching neural networks (SDSNNs) that involved mixed time delays. To enhance the network's generalization capability and accelerate convergence stabilization, a more intricate weight-switching mechanism was introduced, then to mitigate transmission energy consumption, this paper proposed a tailored event-triggering rule that triggered the ETC solely at predetermined time points. This rule ensured the stability of the system while effectively reducing energy consumption. Using the Lyapunov stability theory and various inequality techniques, this paper presented new results for FTS and PTS of SDSNNs. The validity of these findings was supported by conducting data simulations in two illustrative examples.</p></abstract>

Distributed Event-Triggered Control for Power Sharing in Grid-Connected AC Microgrid

The traditional communication method will bring a heavy burden to the grid system in the distributed collaborative control of microgrids. In grid-connected ac microgrid, this paper adopts an event-triggered control method to resolve the power sharing problem and reduce the communication cost. To begin, the problem is stated as the control problem of a leader-follower multiagent system and uses a distributed hierarchical control strategy to solve it. The primary controller uses the internal model principle to control the voltage output. To optimize the secondary controller, this paper not only adopts an event-triggered control strategy for the microgrid based on the Lyapunov principle to control the output of the compensator state, but also carries out relevant theoretical verification and analysis to ensure that each agent can exclude Zeno behavior. Finally, simulation instances are used to verify the usefulness of the studied control algorithm.

Adaptive event-triggered reachable set control for Markov jump cyber-physical systems with time-varying delays

<p>In this paper, we proposed a reachable set control method for a class of Markov jump cyber-physical systems (MJCPSs) with time-varying delays, which addressed the challenges posed by false data injection (FDI) attacks to system security. The goal was to find the set of regions where all MJCPSs states were reachable from the origin in the presence of bounded disturbances. The adaptive event-triggered control strategy was introduced to save network resources. It also reduced the impact of FDI attacks and external disturbances on system security. The conservatism of the results were reduced by constructing the Lyapunov-Krasovskii (L-K) functional with time-varying delays. Difference terms were estimated by using the discrete Wirtinger inequality and the improved extended reciprocally convex matrix inequality, and the ellipsoid reachable set of the MJCPS was obtained. Then, the reachable set controller was obtained by linear matrix inequalities (LMIs) solving technique. Finally, an example simulation proved the validity of the results.</p>

Dynamics and Event-Triggered Impulsive Control of a Fractional-Order Epidemic Model with Time Delay

Due to the lack of timely protection measures against infectious diseases, or based on the particularity of the transmission of some infectious diseases and the time-varying connections between people, the transmission dynamics of infectious diseases in the information society are becoming more and more complex and changeable. A fractional-order epidemic mathematical model with network weighting and latency is proposed in this paper, and the stability near the disease-free equilibrium point and endemic equilibrium point are discussed separately. Subsequently, an event-triggered impulsive control strategy based on an infection rate threshold is put forward. By selecting the appropriate control gain, the Zeno phenomenon can be eliminated on the premise of ensuring the stability of the control error system. Finally, the theoretical results were validated numerically and some conclusions are presented. These findings contribute to future research on the limited-time event-triggered impulsive control of infectious diseases.

Event-triggered output feedback control design for polynomial fuzzy systems

This paper presents a novel approach to event-triggered output feedback control of polynomial fuzzy systems that exhibit unmeasurable system states. To stabilize the system state, a fuzzy model-based event-triggered control scheme is proposed based on output measurement information. The transmission frequency is modulated using a periodic event-trigger protocol, which is scheduled by resorting to a set of historically released packets to ensure better dynamic performance. The event-triggering condition uses the current samples to determine the next trigger and also takes into account the triggering time-dependent gain parameter, thereby significantly reducing network burden. The proposed approach ensures the existence of both polynomial controller gain and event-triggered parameters by satisfying sufficient conditions based on the sum-of-squares approach and the polynomial fitting approximation algorithm. The addressed system’s stability conditions are solved using MATLAB SOSTOOLS. Three numerical examples, including an invented pendulum model, illustrate the superiority and applicability of the methodology. The main contributions of this work are the introduction of a triggering instant dependent periodic event-triggered output feedback control design, which reduces network traffic and enhances dynamic performance. The approach is validated through numerical examples, which demonstrate its superiority and applicability.

Dynamic memory-event-triggered consensus of uncertain MASs with networked attacks and delays

This paper addresses the problem of achieving consensus in leader-following multi-agent systems under event-triggered control, with considerations of model uncertainties, network time-delay, and random deception attacks. To address the challenges of limited network resources, we propose a novel event-triggered control scheme that employs a dynamic memory-based approach. The proposed scheme utilizes multiple historic event-triggered states that are correlated with the states of neighboring agents and the leader, and the threshold parameter varies dynamically with time. We establish a memory-based closed-loop system that considers both network delays and deception attacks. Using the Lyapunov stability theory, a sufficient condition is derived to ensure consensus performance, and the desired controller gains and triggered matrices are obtained by solving linear matrix inequalities. Besides, we provide a comparative analysis of the memoryless dynamic event-triggered control. Finally, we validate the effectiveness of proposed approach with simulation results.

Event-Triggered Adaptive Bipartite Asymptotic Tracking Control Using Intelligent Technique for Stochastic Nonlinear Multiagent Systems

The problem of event-triggered based adaptive bipartite asymptotic tracking control for multi-agent stochastic pure-feedback nonlinear systems over sign digraph is considered. The agents classification optimization strategy (ACOS) is presented, which transforms a structurally unbalanced multi-agent topology graph into a structurally balanced multi-agent topology graph. Furthermore, how to deal with the nonaffine structure and unknown control gains for multi-agent stochastic pure-feedback nonlinear systems is a challenging issue for designing controllers. As a result, the mean value theorem is used to transfer the nonaffine systems into the affine systems, and the Nussbaum functions are used to eliminate the influence caused by the unknown control gains. In addition, the intelligent control technique is used to approximate the unknown nonlinearities, the event-triggered control strategy following the switching thresholds is introduced to save unnecessary communication resources, and the computation burden is eliminated by means of the dynamic surface technique. It is proved that all signals of the closed-loop systems are bounded in probability and the tracking errors asymptotically converge to zero in probability. Finally, the simulation results illustrate the validity of the proposed scheme. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Impact Statement</i> —In nature, there are a lot of interesting movements of multi-agents. In this paper, the multi-agent stochastic pure-feedback nonlinear systems over sign digraph is considered. How to handle the nonaffine structure and unknown control gains for the studied systems under interest is a difficult issue. Therefore, the mean value theorem and the Nussbaum functions are used to overcome the difficulty of designing controllers. The bipartition problem with the structurally unbalanced sign digraph is another design difficulty, then we propose the ACOS in the controller design process. In addition, the controllers designed in this paper, which are constructed by applying the backstepping method, event-triggered control strategy, and dynamic surface technique, can save the unnecessary communication resources and alleviate computation burden. Finally, the simulation results illustrate the validity of the proposed scheme.

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.

Anti-Attack Event-Triggered Control for Nonlinear Multi-Agent Systems With Input Quantization.

In this article, an anti-attack event-triggered secure control scheme for a class of nonlinear multi-agent systems with input quantization is developed. With the help of neural networks approximating unknown nonlinear functions, unknown states are obtained by designing an adaptive neural state observer. Then, a relative threshold event-triggered control strategy is introduced to save communication resources including network bandwidth and computational capabilities. Furthermore, a quantizer is employed to provide sufficient accuracy under the requirement of a low transmission rate, which is represented by the so-called a hysteresis quantizer. Meanwhile, to resist attacks in the multi-agent network, a predictor is designed to record whether an edge is attacked or not. Through the Lyapunov analysis, the proposed secure control protocol can ensure that all the closed-loop signals remain bounded under attacks. Finally, the effectiveness of the designed scheme is verified by simulation results.

Relaxed Stabilization of Event-Triggered Interval Type-2 T–S Fuzzy Positive Systems With Stochastic Actuator Failure

This paper studies the membership-function-dependent (MFD) stability and positivity analysis of positive Interval Type-2 (IT2) Takagi-Sugeno (T-S) fuzzy fault-tolerant control systems under the event-triggered control framework. Considering the fact that parameter uncertainties may exist in nonlinear plants, the IT2 T-S fuzzy model as an effective mathematical tool is employed to approximately express a complex positive system with nonlinearities and uncertainties. In order to save communication resources and improve the security of system operation, an event-triggered fault-tolerant control strategy is then designed based on the Imperfectly Premise Matching (IPM) concept. In addition, for capturing positivity characteristic and matching with the linear co-positive Lyapunov function, a novel event-triggered condition in the 1-norm rather than in the 2-norm is developed to control the positive IT2 T-S fuzzy systems. Thereinto, the non-convex problem as the first obstacle is required to be cleared since feasible solutions usually cannot be obtained based on the non-convex conditions. For dealing with this problem, a valid approach is presented to conduct convexification with the help of a user-chosen constant vector. Besides, because of event-triggered action, the inherent mismatched issue of the premise variables of the fuzzy model and the ones of the fuzzy controller is encountered. Fortunately, the advanced MFD analysis, which is capable of bringing the information contained in the membership functions (MFs) into the stability and positivity conditions, provides an effective strategy to remove this barrier. Consequently, relaxed results which can accomplish the stability and positivity for wider positive systems are developed and displayed in sum-of-squares (SOS) form. Detailed simulation results are given to clarify the effectiveness of the designed event-triggered fault-tolerant control method.

Dual Event-Triggered Control for Asynchronous Scheduling Parameter Varying Networked Switched Systems Under DoS Attacks

Networked switched quasi-linear parameter varying (LPV) systems can effectively describe the parameter degradation or perturbation of actual systems. For a quasi-LPV networked control system, it may be threatened by various attacks, and the communication barriers caused by attacks, delays, and triggers further make the scheduling between the controller and the system asynchronous. In this article, the event-triggered control for switched quasi-LPV system subject to denial-of-service attacks and scheduling asynchronization is concerned. Two event-triggering mechanisms are utilized to reasonably adjust the communication frequency of state and scheduling variables, which can reduce their transmission risk of being attacked. Under the influence of periodic and aperiodic DoS attacks, complete event-triggered control schemes for networked switched quasi-LPV system are given. Especially, a set of sufficient conditions for analysis and synthesis is presented, which solves the tricky problem of scheduling asynchronization. Finally, a numerical simulation and a switched <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">RLC</i> system example are given to verify the effectiveness of the proposed method.

Event-Triggered Adaptive Robust Control for a Class of Uncertain Nonlinear Systems With Application to Mechatronic System

Adaptive robust control technique is investigated to devise trajectory tracking event-triggered controller for a class of uncertain nonlinear systems. Different from the most existing results, this paper divides the entire control input into two parts, including model compensation input term and the robust term, which are separately dealt with by means of special triggered mechanisms. The advantage of separation is that the system reduces the magnitude of chattering amplitude within triggered inputs. To balance the information updating frequency and the magnitude of chattering amplitude, this paper proposes the dynamic threshold triggered mechanism and provides a new Lyapunov function to guarantee the global boundedness of all the closed-loop signals as well as the convergence of the tracking error to an arbitrary small set around zero. This paper also considers desired compensation adaptive robust event-triggered control strategy, thereby further saving bandwidth resources and smoothing the triggered inputs. The Zeno behavior is excluded for two triggering schemes. Finally, simulation and Hardware-in-the-loop (HIL) experiment are used to verify the effectiveness of the proposed strategies.

Adaptive Event-Triggered Neural Network Fast Finite-Time Control for Uncertain Robotic Systems

A fast convergence adaptive neural network event-triggered control strategy is proposed for the trajectory tracking issue of uncertain robotic systems with output constraints. To cope with the constraints on the system output in the actual industrial field while reducing the burden on communication resources, an adaptive event-triggered mechanism is designed by using logarithm-type barrier Lyapunov functions and an event-triggered mechanism. Meanwhile, the combination of neural networks and fast finite-time stability theory can not only approximate the unknown nonlinear function of the system, but also construct the control law and adaptive law with a fractional exponential power to accelerate the system’s convergence speed. Furthermore, the tracking errors converge quickly to a bounded and adjustable compact set in finite time. Finally, the effectiveness of the strategy is verified by simulation examples.

Data-Driven Control for Discrete-Time Nonlinear Systems With Dual-Channel Dynamic Event-Triggered Mechanism

The problem of data-driven control (DDC) for nonlinear systems with dual-channel event-triggering is investigated in this article. First, the nonlinear system is converted into a dynamic linearized data model using the local dynamic linearization techniques. Then, a new dual-channel triggered control framework is designed based on the transformed linearized data model, and semi-independent dynamic event-triggered strategies are designed for the two channels of control input and measurement output. Different from the traditional single-channel event triggered, the main advantages of the proposed control strategy are that the input and output channels are semi-independently triggered, and it can accomplish the tracking control objectives by relying only on the input and output data of the system, and reducing the communication frequency of the input and output channels while ensuring stability. Finally, the effectiveness of the developed dual-channel event-triggered control strategy is verified by the application experiments.

Distributed adaptive event-triggered asymptotic tracking control of linear uncertain multiagent systems by using output only

For the robust tracking control problem of multiagent systems with linear uncertainties, most of the existing studies require full state feedback. Moreover, the designed control strategy also uses global information, such as the number of agents or the eigenvalues of the Laplacian matrix, which does not satisfy the distributed requirement for multiagent design. To address these problems, first, a Luenburger observer is designed to observe the full state of the system by using the output information. Second, the constant gain in the traditional distributed observer for estimating exosystem state information is replaced with a time-varying gain that comes from the output of a newly designed filter, which avoids the use of the global information mentioned above. Finally, based on the above observer and adaptive dynamic feedback, an event-triggered output feedback control strategy is given. This control strategy is not only piecewise constant, which is easy to implement, but also makes the robust tracking control error converge asymptotically to zero. Furthermore, it is theoretically proven that each agent does not have Zeno behaviour, and the effectiveness of the designed control strategy is verified with a simulation example.

Event-triggered control design with varying gains for polynomial fuzzy systems against DoS attacks

This paper presents an innovative event-triggered control scheme for addressing the stabilization problem of polynomial fuzzy systems under the influence of Denial-of-Service (DoS) attacks. The proposed controller utilizes a sampling-based event-triggered mechanism to reduce communication resources and avoid Zeno behavior. Furthermore, a novel polynomial fuzzy model-based control system is developed to investigate the impact of periodic DoS attacks and the addressed event-triggered mechanism on system stability. To improve system performance, control gains are updated at each triggering instant. The exponential stability criteria are formulated in the form of sum-of-square constraints, supported by a triggering instant dependent piecewise Lyapunov-Krasovskii functional and an online asynchronous premise reconstruction approach. Finally, the efficiency and usefulness of the theoretical findings are validated through simulation examples.

Dynamic event-triggered delay compensation control for networked predictive control systems with random delay

This paper aims to investigate the dynamic event-triggered control problem for networked predictive control systems with random delays and disturbance. First, a discrete-time dynamic event-triggered control scheme, in which sensor information is only updated when it is necessary, is presented. Next, the systems are modeled as a time-delay singular Markovian jump systems with time-varying switching. Then, a dynamic event-triggered delay compensation control strategy is proposed. Sufficient conditions guaranteeing the asymptotically stable are derived based on the Lyapunov–Krasovskii functional method together with the linear matrix inequality (LMI) technique. Finally, simulation results verify the effectiveness of the proposed strategy.