Event-triggered Control Scheme Research Articles

Event-triggered delayed impulsive control for functional differential systems on networks

This article investigates the stability of functional differential systems on networks adopting event-triggered multi-delayed impulsive control. We construct a new event-triggered mechanism (ETM) type using the Lyapunov function and the network topology. Combined with the ETM and multi-delayed impulsive control, in which the impulsive jumps are related to the current and past states, we propose a multi-delayed event-triggered impulsive control strategy. This controller will execute the control tasks only when the systems violate the preset ETM. In the view of the Razumikhin method and the graph theory, some criteria are given to avoiding Zeno behavior under this ETM and achieving exponential stability, which is related to the event-triggered parameters, network topology, and impulsive intensity. As an application of our theoretical results, a class of coupled oscillation systems is considered and numerical simulations are presented to verify the practicability and effectiveness.

Event-triggered control strategy based on absolute velocity and relative position measurements for second-order nonlinear multi-agent systems under DoS attacks

Consensus control in multi-agent systems (MASs) has wide-ranging applications in various domains. As the scope of applications continues to expand, ensuring the secure control of these systems to counter a diverse range of network attacks becomes highly necessary. Among these threats, Denial of Service (DoS) attacks have emerged as a major network security threat, capable of rendering targeted agents or networks unable to provide normal services. This paper presents an effective control protocol aiming to achieve leader-following consensus in a second-order MAS with event-triggered control mechanism and nonlinear dynamics under DoS attacks. The protocol utilizes absolute velocity and relative position measurements. The maximum attack frequency and duration of the DoS attacks have been obtained. Furthermore, the possibility of Zeno behavior is eliminated. Theoretical analysis demonstrates that, even under DoS attacks, consensus can be maintained among the leaders and followers within the MASs. In conclusion, the effectiveness of the proposed distributed security control protocol is validated through a case study involving a multi-vehicle cooperative system with unmanned cars.

Event-triggered finite-time neural control for uncertain nonlinear systems with unknown disturbances and its application in SVC

In this article, an event-triggered finite-time neural control strategy is proposed for nonlinear power systems with unknown disturbances and static var compensator (SVC). We first transform the power system with SVC into a three-dimensional uncertain nonlinear system and then extend it to an [Formula: see text]-dimensional uncertain nonlinear system. The disturbance observer is established to estimate external disturbances and the unknown nonlinear terms are approximated by the radial basis function neural networks. Moreover, to avoid the complexity explosion problem in the traditional backstepping method, the command filtering technique is adopted, and the error caused by the command filters is compensated. The adaptive event-triggered finite-time controller ensures that all signals are bounded in finite time and excludes Zeno phenomena. In the end, the simulation for the two-area interconnected power system with SVC is presented to verify the availability and feasibility of the proposed approach.

Robust model predictive control for perturbed nonlinear systems via an error differential-integral based event-triggered approach

This paper aims to establish a new event-triggered model predictive control (MPC) strategy for perturbed nonlinear systems working in a networked environment, which can effectively save the computation and communication resources while ensuring control performance. The core of the framework is a new event-triggered mechanism, which is built by combining the differential and integral (D-I) information of the error between the optimal state and the actual state in a pre-specified time horizon. Based on such a triggering mechanism, a D-I based event-triggered robust MPC algorithm is proposed to stabilise the system and reduce the computation/communication resource consumption. In addition, sufficient conditions to ensure the feasibility and stability of the proposed MPC algorithm and avoid Zeno behaviour are obtained through rigorous theoretical analysis. Finally, the proposed control framework is implemented in the nonlinear cart-damper-spring and autonomous underwater vehicle systems to verify its effectiveness.

A Modular Event-Triggered Containment Control Scheme for Nonlinear Heterogeneous Multiagent Systems With Unknown Leaders.

This article addresses the containment control problem in multiagent systems with nonlinear heterogeneous followers and multiple unknown leaders whose dynamics are exclusively known to their neighbors. The primary goal is to ensure the convergence of each follower to the dynamic convex hull spanned by the leaders under the constraints of limited communication resources. To achieve this, this article introduces a modular event-triggered containment control scheme with three modules. The first module, Module I-signal generator, is designed for each follower to generate a reference signal asymptotically entering the dynamic convex hull without relying on follower dynamics. The second module, Module II-event-triggered mechanism, is tailored to save communication resources effectively by determining when to broadcast information based on perturbed system stability and input-to-state stability theories. The third module, Module III-tracking controller, treats each follower as an independent agent and is crafted to track the reference signal generated by Module I using an output regulation approach. It is established that the system achieves containment control without Zeno behavior under the influence of these modules, and the theoretical results are validated through simulation examples, demonstrating the practical validity of the proposed approach.

Secure Event-Triggered Sliding Control for Path Following of Autonomous Vehicles Under Sensor and Actuator Attacks

Sensor and actuator attacks, which propagate the injected false data through CAN bus, make real threats to the security of the control of autonomous vehicles. This paper attempts to solve the secure event-triggered path following control problem of autonomous vehicles subject to both sensor and actuator attacks. Firstly, a more general physical security model is established to characterize the impacts of sensor and actuator attacks on measurements and control actions. Then, feedback linearization method based on Lie derivative is exploited to linearize the established nonlinear attacked model. Thus, the nonlinear path following control is transformed into a linear case which will facilitate to the following networked-based analysis and design. Based on the established secure model, the event-triggered control scheme is utilized to alleviate communication burden under sampled-data control framework. Furthermore, the sliding control design is augmented to mitigate the malicious impacts caused by such false data injection attacks due to its inherent robustness to abnormal attack signal. Remarkably, the proposed secure sliding control can be plugged into the original control scheme rather than changing its existing control structure. Criteria for formal stability analysis and controller design are also provided and expressed in terms of some numerically tractable linear matrix inequalities. Finally, both simulation and experimental verification cases are shown to verify the composite control scheme.

Fault-Tolerant Synchronization for Memristive Neural Networks With Multiple Actuator Failures.

By using the fault-tolerant control method, the synchronization of memristive neural networks (MNNs) subjected to multiple actuator failures is investigated in this article. The considered actuator failures include the effectiveness failure and the lock-in-place failure, which are different from previous results. First of all, the mathematical expression of the control inputs in the considered system is given by introducing the models of the above two types of actuator failures. Following, two classes of synchronization strategies, which are state feedback control strategies and event-triggered control strategies, are proposed by using some inequality techniques and Lyapunov stability theories. The designed controllers can, respectively, guarantee the realization of synchronizations of the global exponential, the finite-time and the fixed-time for the MNNs by selecting different parameter conditions. Then the estimations of settling times of provided synchronization schemes are computed and the Zeno phenomenon of proposed event-triggered strategies is explicitly excluded. Finally, two experiments are conducted to confirm the availability of given synchronization strategies.

Event-Triggered Adaptive Neural Impedance Control of Robotic Systems.

This article presents an event-triggered adaptive neural impedance control (ETANIC) scheme for robotic systems, where the combination of impedance control (IC) and event-triggered mechanism can significantly reduce the computational burden and the communication cost under the premise of ensuring the stability and tracking performances of the robotic systems. The IC is used to achieve the compliant behavior of the robotic systems in response to the environment. The uncertainties of the robotic systems are estimated by the radial basis function neural network (RBFNN), and the update laws for RBFNN are derived from the designed Lyapunov function. The stability of the whole closed-loop control system is analyzed by the Lyapunov theory, and the event-triggered conditions are designed to avoid the Zeno behavior. The numerical simulation and experimental tests demonstrate that the proposed ETANIC scheme can achieve better efficiency for controlling the robotic systems to perform the interaction tasks with the environment in comparison to the adaptive neural IC (ANIC).

Event-Triggered Boundary Control of Semilinear Hyperbolic Systems

We present an event-triggered boundary control scheme for hyperbolic systems. The trigger condition is based on predictions of the state on determinate sets, and the control input is updated only when the predictions deviate from the reference by a given margin. Nominal closed-loop stability, the absence of Zeno behaviour, and robustness to uncertainty and disturbances, are all established analytically. For the special case of linear systems, the trigger condition can be expressed in closed-form as an <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$L_{2}$</tex-math></inline-formula> -scalar product of kernels with the distributed state. The presented controller can also be combined with existing observers to solve the event-triggered output-feedback control problem. A numerical simulation demonstrates the effectiveness of the proposed approach.

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

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

&lt;abstract&gt;&lt;p&gt;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.&lt;/p&gt;&lt;/abstract&gt;

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.

Event-Based Nonsingular Fixed-Time Tracking Control of an Uncertain Manipulator System Subject to Full-State Static Constraints.

This article centers around investigating the event-triggered nonsingular fixed-time tracking issue for an n -link rigid robot manipulator with full-state constraints, external disturbances, and model uncertainties. We propose the definition of the constrainedly practically fixed-time stability (CPFTS) and provide a sufficient condition for CPFTS. A novel auxiliary function is developed to address the singularity issue caused by repeated differentiation in achieving the fixed-time tracking control. The uncertain parameters are approximated using the radial basis function neural network (RBFNN). This study proposes the model-based and the neutral network-based tracking control approaches, designed using the scaling function technique and the barrier Lyapunov function, respectively, to ensure that the tracking error systems are CPFTS and the full-state constraints comply. Moreover, the communication transmission load is reduced using the relative threshold event-triggered control strategy. Simulation results demonstrate the effectiveness of the proposed tracking control algorithms.

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.