Dynamic Event-triggered Mechanism Research Articles

An Observer-Based Topology Identification and Synchronization in Finite Time for Fractional Singularly Perturbed Complex Networks via Dynamic Event-Triggered Control

This paper investigates the topology identification and synchronization in finite time for fractional singularly perturbed complex networks (FSPCNs). Firstly, a convergence principle is developed for continuously differential functions. Secondly, a dynamic event-triggered mechanism (DETM) is designed to achieve the network synchronization, and a topology observer is developed to identify the network topology. Thirdly, under the designed DETM, by constructing a Lyapunov functional and applying the inequality analysis technique, the topology identification and synchronization condition in finite time is established in the forms of the matrix inequality. In addition, it is proved that the Zeno behavior can be effectively excluded. Finally, the effectiveness of the main results is verified by an application example.

Formation tracking control with disturbance rejection in leader-follower multi-agent systems under dynamic event-triggered mechanism

This paper investigates the problem of time-varying formation tracking (TVFT) in a linear multi-agent systems (MASs) with external disturbance in a type of directed network topology, and proposes a formation control strategy in a leader-follower control framework with disturbance rejection under a dynamic event-triggering mechanism (DETM). Firstly, in order to improve the overall performance of the system, an extended state observer is used to estimate the status of the follower and the presence of external disturbance in real time. Secondly, a DETM is designed to avoid continuous communication between adjacent intelligent agents. And the formation tracking error between adjacent multi-agents is used to formulate a distributed TVFT control strategy. Furthermore, a compensation function is designed to actively offset the impact of time-varying disturbances on system. A Lyapunov function is then established for stability analysis, verifying the absence of Zeno behavior with this DETM. Finally, a comparative numerical simulation was conducted using an unmanned aerial vehicle (UAV) cluster case to verify that the proposed method can save communication resources to a greater extent and has good control performance.

Dynamics Event-Triggered-Based Time-Varying Bearing Formation Control for UAVs

This article addresses the leader-follower formation maneuver control problem of multiple unmanned aerial vehicles (UAVs), taking into account the time-varying velocity and time-varying relative bearing. An event-triggered bearing-based distributed velocity observer was designed, using only the desired position and velocity of the leaders. Furthermore, a dynamic event-triggered mechanism was introduced to reduce continuous communication between UAVs, thus effectively saving communication bandwidth and resources. Building on this, a bearing-only formation maneuver control strategy was proposed, integrating the event-triggered velocity observer with the backstepping control approach. To conclude, numerical simulations have been conducted to confirm the effectiveness of the proposed scheme in accomplishing formation maneuver control objectives, including translation, scaling, and rotation control. Furthermore, the advantages of the dynamic event-triggering strategy have been demonstrated through comparative simulations with traditional event-triggering strategies. Additionally, the effectiveness of the proposed observer and controller has been demonstrated by a comprehensive hardware-in-the-loop (HITL) simulation example.

Dynamic event-triggered fixed-time consensus control for coupled multi-permanent magnet synchronous motors stochastic system with prescribed performance

With the ubiquity of permanent magnet synchronous motors (PMSM), multi-PMSMs are increasingly being elastically coupled to achieve greater output power. The authors establish a general mathematical model of coupled multi-PMSMs system. The dynamic analysis of the coupled multi-PMSMs system is analyzed using coupled three-PMSMs system as an example, and a dynamic event-triggered fixed-time consensus controller with prescribed performance is constructed for coupled PMSMs stochastic system. In addition, Interval Type-3 Fuzzy System (IT3FS) is employed to estimate unknown functions, the computing cost is reduced by updating the weights based on only one adaptive law per PMSM. To enhance the efficiency of communication resource utilization, a dynamic event-triggered mechanism is introduced, where the triggering condition depends on a dynamic auxiliary variable. Moreover, fixed-time control technical is applied to guarantee the setting time and a novel fixed-time prescribed performance function (PPF) is constructed to constrain consensus errors. To cope with the “complexity explosion”, a fixed-time tracking differentiator (TD) is introduced and the corresponding filtered error compensation signals is designed. Finally, the authors demonstrate the system is fixed-time stabilized in probability by applying fixed-time stabilization Lyapunov stability criterion, and the simulation results of a coupled three-PMSMs stochastic system show that the presented algorithm is feasible.

Safety-critical control of planar quadcopters under dynamic event-triggered mechanism

This paper aims to solve the problem of safety-critical control, i.e., safe flight of planar quadcopters in complex environments. By introducing input-to-state safe barrier function (ISSf-BF), the attitude, position, speed, and other key parameters of planar quadcopters are fully considered to realize safety-critical control. Also, a dynamic event-triggered mechanism (DETM) is constructed, and Zeno phenomenon is ruled out based on the constructed DETM. By monitoring the relative distance and speed changes between planar quadcopters and the boundary of the safe zone, the control action is triggered only when the control strategy needs to be adjusted, which effectively reduces the unnecessary waste of computation and communication resources. Finally, a series of simulation experiments are demonstrated to verify the feasibility of the proposed method.

Adaptive sampling artificial-actual control for non-zero-sum games of constrained systems

Considering physical constraints encountered by actuators, this paper addresses the non-zero-sum game of continuous nonlinear systems with symmetric and asymmetric input constraints through aperiodic sampling artificial-actual control. Initially, the artificial system built by the improved Elman dynamic neural networks (EDNNs) has artificial-actual interaction with the physical system, which provides a new perspective for predicting the system state. By constantly learning and adjusting parameters, EDNNs can gradually approximate the dynamic behavior of the real system to achieve more effective control. Aiming at accommodating diverse input constraints, the non-quadratic value function constructed from a smoothly bounded function is devised. Then, the polynomial parameterized adaptive dynamic programming (ADP) is employed to approximate the solution of the coupled Hamilton–Jacobi equation (HJE), deriving optimal control laws for two players. To improve the efficiency of data communication, three adaptive sampling mechanisms including event-triggered mechanism (ETM) with relative threshold, dynamic ETM (DETM) and self-triggered mechanism (STM) are introduced in turn during the iterative learning process of control sequences. DETM further extends sampling intervals by incorporating internal dynamic variables, while STM determines the next trigger time through soft calculation without hardware monitoring. All three trigger modes can ensure the system stability while avoiding the Zeno phenomenon, and relevant proofs are given. Finally, the simulation validates the effectiveness of the designed algorithm and highlights the unique characteristics of each trigger mode.

Dynamic event-triggered control for CPSs under QoS-based variable sampling approach

In this article, a quality of service (QoS) dependent variable sampling dynamic event-triggered control method is designed for a cyber–physical system (CPS) with delays and packets dropout to cope with non-ideal network environments, maintain the desired control performance and improve the communication efficiency. To achieve the variable period sampling, a sampler is designed based on the QoS of the wireless network by using the delta operator discretization method. Then, a variable period sampling scheme for the delta operator system converted from the CPS is designed. Furthermore, a dynamic event-triggered mechanism (DETM) is proposed using the variable period sampling signal, which can reduce event triggered data calculations and increase event triggered intervals. By utilizing the average dwell time (ADT) approach, sufficient conditions contains the explicit variable sampling period are derived for the derived switched CPS. Finally, the effectiveness of the designed method is verified by numerical examples.

Event-Based Adaptive NN Fixed-Time Cooperative Formation for Multiagent Systems.

This article focuses on the fixed-time formation control problem for nonlinear multiagent systems (MASs) with dynamic uncertainties and limited communication resources. Under the framework of the backstepping method, a time-varying formation function is introduced in the controller design. To attain the prescribed transient and steady-state performance of MASs, a fixed-time prescribed performance function (FTPPF) is designed and the further coordinate transformation addressing the zero equilibrium point problem is removed. To achieve better approximating performance, a neural network (NN)-based composite dynamic surface control (CDSC) strategy is proposed, where the CDSC scheme is consisted of prediction errors and serial-parallel estimation models. According to the signals generated by the estimation models, disturbance observers are established to overcome the difficulty from approximating errors and mismatched disturbances. Moreover, an improved dynamic event-triggered mechanism and varying threshold parameters are constructed to reduce the signal transmission frequency. Via the Lyapunov stability theory, all the signals in the closed-loop system are semi-globally uniformly ultimately bounded. Finally, the simulation results verify the effectiveness of the developed CDSC strategy.

Observer-based resilient dynamic event-triggering control for switched systems under DoS and FDI attacks

This article investigates the problem of the observer-based dynamic event-triggering control for networked switched systems under denial-of-service (DoS) attacks and false data injection (FDI) attacks. The considered DoS attack model is limited by duration and frequency, and the FDI attack signal is generated by an exogenous system. First, an extended observer is constructed to estimate both the system state and the FDI attack signal. Subsequently, an observer-based resilient dynamic event-triggered mechanism is designed to reduce communication resource costs and eliminate the limitation of maximum asynchronous times less than dwell time such that the system can frequently switch in the event interval. Furthermore, by employing Lyapunov stability theory and average dwell time method to guarantee the switched system is exponentially stable. Meanwhile, a co-design scheme of controller gains, observer gains, and triggering parameters is developed. Finally, a switched circuit system is used to substantiate the feasibility of the proposed approach.

Observer-based bipartite consensus tracking of nonlinear fractional-order multi-agent systems with pull-based dynamic event-triggered mechanism

This paper investigates the observer-based bipartite consensus tracking of nonlinear fractional-order multi-agent systems (FOMASs) by employing a pull-based dynamic event-triggered mechanism (DETM). First, considering that the relevant state information of each agent is not always measurable, a class of distributed observers is considered for each agent to estimate its state information. Then, a pull-based DETM for FOMASs is proposed to avoid continuous controller updates, in which the dynamic threshold is modulated according to the preset conditions. The pull-based DETM constructed in this paper enables each agent to update the controller only based on its own trigger instants. Furthermore, an observer-based dynamic event-triggered control protocol is designed to guarantee the bipartite consensus tracking of FOMASs. Correspondingly, sufficient conditions are obtained by using graph theory and choosing suitable Lyapunov candidate functions. Moreover, the Zeno behavior is precluded. Finally, two simulation examples are presented to illustrate the theoretical results efficiently.

Event-based nonfragile state estimation for memristive recurrent neural networks with stochastic cyber-attacks and sensor saturations

Abstract This paper addresses the issue of nonfragile state estimation (SE) for memristive recurrent neural networks (MRNNs) with proportional delay and sensor saturations. In practical engineering, numerous unnecessary signals are transmitted to the estimator through the networks, which increases the burden of communication bandwidth. In this paper, a dynamic event-triggered mechanism (DETM) is employed to select useful data instead of a static event-triggered mechanism. By constructing a meaningful LyapunovKrasovskii functional (LKF), a delay-dependent criterion is derived in terms of linear matrix inequalities (LMIs) for ensuring the global asymptotic stability of the augmented system. In the end, two numerical simulations are employed to illustrate the feasibility and validity of the proposed theoretical results.

Dynamic event-triggered control of large-scale nonlinear systems with sensor uncertainty

In this article, the dynamic event-triggered control problem is investigated for a class of large-scale nonlinear systems subject to sensor uncertainty. The unknown nonlinearities involved in each subsystem are assumed to be bounded by time-varying continuous functions multiplied by unknown constants and unmeasured states. To estimate the unmeasured states of each subsystem, a time-varying high-gain observer is constructed. Then, a dynamic event-triggered mechanism is proposed by introducing an internal dynamic variable in triggering function. Combined with the dual-domination approach, a dynamic event-triggered output feedback controller is developed for each subsystem. Subsequently, it is proved the convergence of all states is ensured based on the Lyapunov theory and the Zeno behavior can be avoided. Eventually, an example is presented to demonstrate the effectiveness of the proposed dynamic event-triggered control scheme.

Dynamic event-triggered state estimation for discrete-time delayed switched neural networks with constrained bit rate

In this paper, a class of discrete-time delayed switched neural networks with dynamic event-triggered mechanism (DETM) and constrained bit rate is considered. In order to reduce the transmission frequency and alleviate the unnecessary resource loss between sensor and estimator, a DETM is proposed. The data transmission from sensor to estimator is realized through constrained bit rate channel. Therefore, in order to reflect the bandwidth allocation rules of accessible neurone nodes, a bit rate constraint model is introduced and an encoding-decoding mechanism is developed. This paper is concerned with the strategy of average dwell time (ADT) and linear matrix inequality, then sufficient conditions for the exponential ultimate boundedness of switched neural networks with DETM and constrained bit rate are proposed. Finally, an example is given to prove the effectiveness of the results.

Dynamic event-triggered adaptive tracking control for stochastic nonlinear systems with deferred time-varying constraints

This article investigates the problem of adaptive tracking control for stochastic nonlinear systems with deferred state constraints. The novel unified universal barrier function (UUBF) and deferred funnel error transformation are developed to handle various state constraints and adjust the tracking error more precisely, respectively. Multi-dimensional Taylor network (MTN) and dynamic event-triggered mechanism (DETM) are employed to design controller in the backstepping process, which can achieve full-state constraints and the tracking control after the preassigned time while conserving more communication resources. Finally, two simulation examples are presented to verify the effectiveness of the proposed control scheme.

Dynamic Event-Triggered Control for Delayed Nonlinear Markov Jump Systems under Randomly Occurring DoS Attack and Packet Loss

This paper aims to address the exponential stability and stabilization problems for a class of delayed nonlinear Markov jump systems under randomly occurring Denial-of-Service (DoS) attacks and packet loss. Firstly, the stochastic characteristics of DoS attacks and packet loss are depicted by the attack success rate and packet loss rate. Secondly, a Period Observation Window (POW) method and a hybrid-input strategy are proposed to compensate for the impact of DoS attack and packet loss on the system. Thirdly, A Dynamic Event-triggered Mechanism (DETM) is introduced to save more network resources and ensure the security and reliability of the systems. Then, by constructing a general common Lyapunov functional and combining it with the DETM and other inequality analysis techniques, the less conservative stability and stabilization criteria for the underlying systems are derived. In the end, the effectiveness of our result is verified through two examples.

Networked Output-Feedback MPC: A Bounded Dynamic Variable and Time-Varying Threshold-Dependent Event-Based Approach.

The event-triggered model predictive control (MPC) problem is addressed for polytopic uncertain systems. A new dynamic event-triggered mechanism (DETM) with a bounded dynamic variable and a time-varying threshold is proposed to manage measurement data packet releases. The dynamic output-feedback MPC issue is detailed as a "min-max" optimization problem (OP) with an objective function over an infinite horizon, where the hard constraint on the predictive control is required. By applying a Lyapunov-like function containing the bounded dynamic variable, an auxiliary OP constrained by several matrix inequalities is proposed, and the design methods of the output-feedback gains are provided if this auxiliary OP is feasible. The designed MPC controller ensures that the closed-loop system is input-to-state practically stable. Two examples including an event-triggered DC motor are given to illustrate the validity of the developed MPC algorithm. Simulation results verify that the proposed DETM has advantages over some existing triggering mechanisms in decreasing the consumption of resources while meeting the required performance.

Distributed Event-Triggered Quantized Fault-Tolerant Control of Linear Multiagent Systems With External Disturbances and Parameter Uncertainties.

In this article, the issue of fault-tolerant leader-following consensus under a distributed dynamic event-triggered mechanism is addressed for linear multiagent systems (MASs) in the presence of unknown parameter uncertainties, external disturbances, and actuator faults, including loss of effectiveness and bias, in which the mechanism is with quantized state measurements. Due to the fact that information is transmitted via a bandwidth-limited communication network, a quantized control scheme with a uniform quantizer is introduced for leader-following consensus. In order to decrease the communication load and save the limited communication network resources, a distributed event-triggered mechanism is studied for leader-following consensus problem of linear MASs with quantized state measurements. In the presence of actuator faults, external disturbances, and unknown parameter uncertainties, an adaptive coupling gain for the controller is presented. Based on the Lyapunov function approach, the stability of the closed-loop system and the convergence of consensus errors are proved. Furthermore, the Zeno behavior is excluded for the triggering time sequences. Finally, simulation studies are given to verify the effectiveness of the proposed event-triggered fault-tolerant control scheme.

Event-based adaptive active disturbance rejection control for nonlinear large-scale systems with input delay

This paper explores the active disturbance rejection control (ADRC) issue for interconnected large-scale systems with unmeasured states, input delay and a total disturbance composed of unknown constrained external disturbances. An enhanced extended state observer is built via fuzzy logic systems to identify system uncertainties and states and keep restraining the total disturbance of uncertain systems. When the triggering condition is met, the tracking differentiator component of the ADRC is able to reduce the communication overhead by successfully applying the backstepping control approach and the dynamic event-triggered mechanism. Meanwhile, the integral compensation method eliminates the negative effect cased by the input delay. Furthermore, the developed decentralised fuzzy control scheme can ensure that all signals in the closed-loop systems are semiglobally uniformly ultimately bounded and Zeno behaviour does not occurs. Finally, an example is used to depict the validity of the control approach.

Event-driven prescribed performance distributed formation control for MSVs based on second-order step response with actuator input saturation

This study aims to investigate the problem of event-driven distributed formation control for marine surface vehicles (MSVs) system, including prescribed performance constraint and actuator input saturation. By constructing a novel prescribed performance function based on second-order step response, the intuitive physical significance is realized, and the performance specifications imposed on the output tracking errors are achieved. Subsequently, a double-layer adaptive sliding-mode disturbance observer (ASMDO) is presented to compensate for lumped uncertain terms. Within the controller design stage, an auxiliary dynamic system is built to introduce the auxiliary intermediate control law, thus avoiding the need to directly design anti-saturation generalized control input for the MSVs system. Then, by incorporating external dynamic variable, a dynamic event-triggered mechanism (DETM) is proposed to regulate the transmission between the controller and actuators. Under the DETM, the designed controller only transmits the control force and torque into actuators at the specified triggering moment, and the trigger moment is independent of the internal state of the system. After that, the minimum inter-event time (MIET) of the proposed DETM is proved to be strictly positive, which ensures Zeno-free behavior. Lastly, the efficiency and advantages of the proposed event-driven distributed formation controller are verified through numerical simulations.

Secure Change Control for Supply Chain Systems via Dynamic Event Triggered Using Reinforcement Learning under DoS Attacks

In this paper, a distributed secure change control scheme for supply chain systems is presented under denial-of-service (DoS) attacks. To eliminate the effect of DoS attacks on supply chain systems, a secure change compensation is designed. A distributed policy iteration method is established to approximate the coupled Hamilton–Jacobi–Isaacs (HJI) equations. Based on the established reinforce–critic–actor (RCA) structure using reinforcement learning (RL), the reinforced signals, performance indicators, and disturbance input are proposed to update the traditional time-triggered mechanism, and the control input is proposed to update the dynamic event-triggered mechanism (DETM). Stability is guaranteed based on the Lyapunov method under secure change control. The simulation results for supply chain systems show the effectiveness of the secure change control scheme and verify the results.