Global exponential stabilization of 2 × 2 linear hyperbolic PDEs via dynamic event-triggered backstepping control

Integral reinforcement learning-based dynamic event-triggered safety control for multiplayer Stackelberg–Nash games with time-varying state constraints

Output-based dynamic event-triggered consensus control for linear multiagent systems

In this article, we consider the load frequency control problem for a class of power systems based on the dynamic event-triggered control (ETC) approach. The transmission networks are unreliable in the sense that malicious denial-of-service (DoS) attacks may arise in the power system. First, a model-based feedback controller is designed, which utilizes estimated states, and thus can compensate the error between plant states and the feedback data. Then, a dynamic event-triggered mechanism (DETM) is proposed by introducing an internal dynamic variable and a timer variable with jump dynamics. The proposed (DETM) can exclude Zeno behavior by regularizing a prescribed strictly positive triggering interval. Incorporated in the ETC scheme, a novel hybrid model is established to describe the flow and jump dynamics of the power system in the presence of DoS attacks. Based on the hybrid dynamic ETC scheme, the power system stability can be preserved if the attacks frequency and duration sustain within an explicit range. In addition, the explicit range is further maximized based on the measurement trigger-resetting property. Finally, a numerical example is presented to show the effectiveness of our results.

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.

In this paper, the dynamic event-triggered tracking control issue is studied for a class of unknown stochastic nonlinear systems with strict-feedback form. At first, neural networks (NNs) are used to approximate the unknown nonlinear functions. Then, a dynamic event-triggered controller (DETC) is designed through the adaptive backstepping method. Especially, the triggered threshold is dynamically adjusted. Compared with its corresponding static event-triggered mechanism (SETM), the dynamic event-triggered mechanism (DETM) can generate a larger execution interval and further save resources. Moreover, it is verified by two simulation examples that show that the closed-loop stochastic system signals are ultimately fourth moment semi-globally uniformly bounded (SGUUB).

This paper presents an in-depth investigation into the quasi-synchronisation of nonlinear time-delay drive-response systems with mismatched parameters by dynamic event-triggered impulsive control (ETIC), which integrates impulsive control and dynamic event-triggered control (ETC). The impulsive moments are determined by a certain dynamic state-dependent event mechanism. Compared to static ETIC, the proposed dynamic ETIC scheme effectively minimises the amount of controller updates and saves a significant amount of energy without reducing the system decay rate. Utilizing Lyapunov stability theory and the variation of the parameter formula, some appropriate conditions are set to keep the synchronisation error within a non-zero bound. It shows that the dynamic ETIC strategy effectively achieves quasi-synchronisation of considered systems, and the Zeno phenomenon is excluded by contradiction. Finally, the validity of the methods is demonstrated through a numerical example.

This paper investigates the event-based consensus tracking control of nonlinear multi-agent systems (MASs) with disturbances and unknown time-varying parameters (TVPs). Firstly, to circumvent the challenge brought by unknown TVPs, the bound estimation method is adopted, focusing solely on estimating the bounds of these parameters without any effort to address their derivatives. Secondly, by constructing some smooth functions as compensation terms and incorporating a set of positive integrable functions into the process of virtual controller design, the effect of unknown TVPs is fully compensated and a backstepping based distributed asymptotic consensus tracking control algorithm is developed. Additionally, to achieve more efficient transmission and further save resources, a dynamic event-triggered control (ETC) strategy is presented, where a dynamic variable is embedded in the trigger condition to adjust the threshold flexibly. In particular, reasonable proofs are given to confirm that our presented control method can not only guarantee the boundedness of all closed-loop signals but also realize the asymptotic output tracking and avoids Zeno behavior, irrespective of the parameters variation speed and any information about the desired trajectory’s high order derivatives. Ultimately, simulation results attest the validity of our strategy.

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 composite anti-disturbance fault-tolerant tracking control for ships with disturbances and actuator faults

In this article, the dynamic event-triggered control problem of memristive neural networks (MNNs) under multiple cyber-attacks is considered. A novel dynamic event-triggering scheme (DETS) and the corresponding event-triggered controller are proposed by taking into consideration both denial-of-service and deception attacks (DoS-DAs). Then, a key lemma is established to show that the dynamic event-triggered controller can be used to solve the globally stochastically exponential stability (GSES) issue of concerned MNN under multiple cyber-attacks. Meanwhile, a novel Lyapunov functional is proposed based on the actual sampling pattern. It is shown that under our proposed dynamic event-triggered controller and Lyapunov functional, the concerned MNN can achieve GSES in the presence of DoS-DAs. In addition, our results include relevant results on event-triggered control of MNN with static event-triggering scheme (SETS) or without cyber-attacks as special cases. The effectiveness of the proposed event-triggered controller under multiple cyber-attacks is illustrated by a simulation example.

Dynamic event-triggered adaptive neural control for MIMO nonlinear systems via a single parameter learning method

Aperiodic intermittent dynamic event-triggered synchronization control for stochastic delayed multi-links complex networks

Data-driven-based sliding-mode dynamic event-triggered control of unknown nonlinear systems via reinforcement learning

Event-triggered integral sliding mode formation control for multiple quadrotor UAVs with unknown disturbances

This article designs the static and dynamic event-triggered control (ETC) and self-triggered control (STC) algorithms to achieve the semiglobal stabilization of discrete-time systems with input constraints. First, a novel static ETC algorithm based on the discrete-time parametric Lyapunov equation (DPLE) is designed. In order to further increase the interevent times (IETs), the corresponding dynamic ETC is designed. Next, both static and dynamic STC, where the next control law updates depend on the previous triggered states, are proposed to avoid monitoring the measurement errors. The proposed algorithms are not only capable of reducing the number of transmissions significantly but also build a very simple and clear relationship between the only design parameter and the nontrivial IET (NIET). This allows us to change regularly IETs by adjusting the design parameter so that the nontriviality of static and dynamic ETC and STC is guaranteed and a tradeoff between the IETs and the control performance can be easily found. Specifically, by exploring the properties of DPLE, the designed algorithms avoid the complex relationship between the nontrivial condition and the system matrices. Finally, the designed static and dynamic ETC and STC algorithms are applied to the design of the spacecraft rendezvous control system and their effectiveness is verified by simulation results.