Event-triggered Mechanism Research Articles

Distributed event-triggered algorithm for convex optimization with coupled constraints

This paper develops a distributed primal–dual algorithm via an event-triggered mechanism to solve a class of convex optimization problems subject to local set constraints, coupled equality and inequality constraints. Different from some existing distributed algorithms with the diminishing step-sizes, our algorithm uses the constant step-sizes, and is shown to achieve an exact convergence to an optimal solution with an ergodic convergence rate of O(1/k) for general convex objective functions, where k>0 is the iteration number. Based on the event-triggered communication mechanism, the proposed algorithm can effectively reduce the communication cost without sacrificing the convergence rate. Finally, a numerical example is presented to verify the effectiveness of the proposed algorithm.

Observer-based reinforcement learning for optimal fault-tolerant consensus control of nonlinear multi-agent systems via a dynamic event-triggered mechanism

In this article, an adaptive optimized consensus tracking control problem is studied for nonlinear strict-feedback dynamic multi-agent systems (MASs), considering both unmeasurable system states and time-varying bias faults. By utilizing the backstepping technique, we develop an adaptive reinforcement learning (RL) algorithm within the observer-critic-actor architecture, specially designed to compensate for the lack of state information and derive control inputs, thereby achieving approximate optimal control. Moreover, an event-triggered mechanism is introduced in the sensor-to-controller channel, which dynamically adjusts the triggering threshold online and employs event-sampled states to initiate control actions. To address discontinuities caused by state triggering, we construct virtual controllers that continuously sample state signals and reconfigure the actual controller based on previously triggered states. The outputs of the MASs are shown to accurately track the desired reference signals while ensuring the boundedness of all closed-loop signals. Additionally, the proposed controller is verified to be devoid of Zeno behavior. Finally, the effectiveness of our control methodology is demonstrated through numerical simulation.

Adaptive event-triggered robust output regulation for uncertain switched T-S fuzzy systems

This paper focuses on the adaptive event-triggered robust output regulation issue for uncertain switched Takagi–Sugeno fuzzy systems with unknown exosystems and model uncertainty. The purpose is to pursue the output regulationcharacteristic and the disturbances suppression characteristic. First, an event-triggered mechanism is introduced to ease the communication burden. Then, on the basis of the event-triggered program, an adaptive event-triggered robust output regulation control law is developed to battle the uncertainty and disturbances. Further, constraints are arrived to force the solvability of the adaptive event-triggered robust output regulation issue for uncertain switched Takagi–Sugeno fuzzy systems by virtue of the average dwell-time way. In the end, a simulation instance with a mass–spring-damping model employed is arranged to showcase the practicality and effectiveness of the developed control methodology.

Robust adaptive control for dynamic positioning vessels with actuator faults via the multiport event-triggered mechanism

This paper presents a robust adaptive neural control algorithm for dynamic positioning tasks of full-actuated vessels using a novel multiport event-triggered mechanism. The proposed mechanism only updates control signals and feedback error signals upon violation of the multiport event-triggered rule. Therefore, the communication loads between the sensors and controller, as well as between the controller and actuators, are significantly mitigated. Furthermore, a fault-adaptive method is adopted by estimating the uncertain actuator faults, based on the proposed event-triggered mechanism, the fault-adaptive parameters will be updated at the triggering instant, eliminating the destabilizing effects of the closed-loop system. Besides, the dynamic surface control (DSC) and the robust neural damping technique are employed to address the problems of “complexity explosion” and system uncertainties. Based on Lyapunov stability theory, the closed-loop system is proved to satisfy semi-globally uniform ultimate bounded (SGUUB). Finally, a simulation experiment validates the effectiveness of the proposed algorithm.

Adaptive event-triggered sliding mode control for platooning of heterogeneous vehicular systems and its [formula omitted] input-to-output string stability

Platooning of vehicular systems is an effective technique for enhancing transportation efficiency. As the scale of the vehicular platoon systems increases, disturbances on individual vehicles can affect the whole platoon through their connections. Besides, excessive vehicles impose a significant burden on communication devices. Towards this end, this work investigates the distributed platoon control problem of connected vehicular systems subject to disturbances by employing a resource-efficient communication mechanism. The proposed adaptive event-triggered mechanism (AETM) avoids periodic data transmission and reduces communication burden among vehicles. Besides, the AETM regulates the triggered threshold dynamically via the perception of spacing errors and avoids continuous inter-vehicle communication. Next, an AETM-based finite-time extended state observer (AFESO) is designed to alleviate the impact of the external disturbances. Then, an adaptive event-triggered distributed sliding mode control (DSMC) framework is developed to guarantee platoon stability. It is approved that, under the proposed control method, the closed-loop system subject to the disturbances satisfies the L2 input-to-output string stability (L2-IOSS). The salient feature of the AETM-based DSMC is that the AETM can effectively reduce communication consumption, while DSMC mitigates the performance degradation caused by triggering errors and disturbances. Finally, numerical simulations demonstrate the effectiveness of the proposed algorithm.

Finite-Time Convergence Guidance Law for Hypersonic Morphing Vehicle

Aiming at the interception constraint posed by defensive aircrafts against hypersonic morphing vehicles (HMVs) during the terminal guidance phase, this paper designed a guidance law with the finite-time convergence theory and control allocation methods based on the event-triggered theory, achieving evasion of the defensive aircraft and targeting objectives for a morphing vehicle in the terminal guidance phase. Firstly, this paper established the aircraft motion model; the relative motion relationships between HMV, defensive aircraft, and target; and the control equations for the guidance system. Secondly, a guidance law with finite-time convergence was designed, establishing a controller with the angle between the aircraft–target–defense aircraft triplet as the state variable and lift as the control variable. By ensuring the angle was non-zero, the aircraft maintained a certain relative distance from the defense aircraft, achieving evasion of interception. The delay characteristic of the aircraft’s flight controller was considered, analyzing its delay stability and applying control compensation. Thirdly, a multi-model switching control allocation method based on an event-triggered mechanism was designed. Optimal attack and bank angles were determined based on acceleration control variables, considering different sweep angles. Finally, simulations were conducted to validate the effectiveness and robustness of the designed guidance laws.

Mode-dependent observer-based secured hybrid-driven reliable control for semi-Markov jump cyber–physical systems with DoS attacks

This study focuses on the issue of secured observer based hybrid-triggered quantized reliable control design for semi-Markov jump cyber–physical systems (S-MJCPSs) with general uncertain transition rates, quantization, actuator faults, denial-of-service (DoS) attacks and disturbances. In particular, a mode-dependent observer is developed to estimate the unmeasurable states of the considered system. Further, to minimize unnecessary data transmissions within the network channel, we have implemented a hybrid-triggered mechanism in which, the swap process between the time-triggered and the event-triggered mechanisms is governed by a Bernoulli distribution. Moreover, an observer-based hybrid-triggered quantized reliable controller is devised by utilizing the state estimation information obtained from the observer, which assists the S-MJCPSs to achieve primary intent of this study. Subsequently, the control input signals are quantized before being transmitted into the actuator due to the limited capacity of the data transmission medium. Concurrently, periodic DoS jamming attacks are taken into consideration, which have a significant impact on control performances. A set of adequate criteria is established in the context of linear matrix inequalities by employing the mode-dependent time-varying piecewise Lyapunov-Krasovskii functional and the integral inequalities, guaranteeing that the undertaken system accomplishes the global exponential stability and meet a specific H∞ performance level. Following that, by means of the deduced adequate criteria, the values of the controller and observer gain are computed. At last, the efficacy and applicability of the derived theoretical findings are demonstrated through two practical examples.

Distributed predefined-time economic dispatch based on event-triggered strategy for microgrids under directed graphs

The proliferation of new energy solutions and the enhanced accessibility of distributed power sources have significantly increased the complexity and variability of microgrid structures. Due to inherent limitations in its operational mode, the traditional centralized optimization method falls short in effectively addressing the economic dispatch problem in contemporary microgrids. In this paper, a distributed optimization method is devised to address the economic dispatch problem of microgrids under directed graphs by leveraging the consensus algorithm of multi-agent systems. We define the incremental cost of each microgrid unit as the consensus variable, achieving convergence through a pioneering combination of event-triggered strategy and predefined-time control theory. This approach not only optimizes resource use but also ensures timely and efficient solution convergence, addressing critical gaps in existing methodologies. This approach enables control over the upper bound of convergence time for optimal solutions by directly adjusting time parameters in the controller under the directed graph. Consequently, it achieves coordinated control of the power output in microgrids within a predefined time while reducing the communication resources between nodes with event-triggered mechanism. The effectiveness of the proposed algorithm is verified through simulation and analysis.

Disturbance observer-based event-triggered impulsive control for nonlinear systems with unknown external disturbances

Input-to-state practical stability (ISpS) of a kind of nonlinear systems suffering from unknown exogenous disturbances is explored in this article, where a disturbance observer is established to estimate the information of the exogenous disturbances. To achieve ISpS of the system, the impulsive controller as well as state-feedback controller are both considered to regulate the discrete and continuous dynamics of the system, respectively. Especially, a novel disturbance observer-based event-triggered mechanism is devised to decide the release of impulsive control signal. Furthermore, several adequate conditions are given for excluding the occurrence of Zeno phenomenon. To confirm the feasibility of the proposed results, two numerical instances and their corresponding simulation results are presented.

Cooperative output regulation problem with faults based on adaptive event-triggered mechanism

This paper focuses on the cooperative output regulation issue of heterogeneous linear multiagent systems (MAS)s under directed graph, where there are communication link faults and actuator faults. Firstly, a novel adaptive event-triggered compensator is constructed to estimate the state of the exosystem in the presence of communication link faults. Then, dynamic event-triggered controller is constructed to compensate for the impact of actuator faults on system stability. Note that the novel dual event-triggered mechanisms are designed to reduce communication frequency, and the MAS does not show the Zeno behavior. Finally, simulation experiment is introduced to verify the validity of the proposed method.

Dynamic event-driven optimal consensus control for state-constrained multiagent zero-sum differential graphical games

In this paper, a dynamic event-driven optimal control scheme is proposed for the zero-sum differential graphical games in nonlinear multiagent systems with full-state constraints. Initially, to address the dual demands of optimality and state constraints, a set of system transformation functions are introduced to satisfy the state constraints of the agents. Then, by applying the principle of differential game theory, the distributed optimal control problem affected by external disturbances is formulated as a zero-sum differential graphical game, and the performance index function related to neighbor informations and disturbances is designed for each follower. Afterwards, to enhance the utilization of communication resource, a novel dynamic event-triggered mechanism characterized by a dynamic threshold parameter and an auxiliary dynamic variable is developed, which not only exhibits greater flexibility but also diminishes the frequency of triggers. Furthermore, the approximate optimal control strategies are obtained by employing an event-driven adaptive dynamic programming algorithm. Ultimately, a simulation example is presented to verify the applicability of the proposed control approach.

Distributed zero-gradient-sum optimisation algorithm with an edge-based adaptive event-triggered mechanism

This paper addresses the continuous-time distributed optimisation problem over networks, where the global objective function is formed by a sum of convex local objective functions. To avoid continuous communication among agents, a distributed adaptive Zero-Gradient-Sum (ZGS) optimisation algorithm under a dynamic event-triggered scheme is proposed. This is achieved by dynamically adjusting the coupling strengths of adjacent agents within the network. Our analysis confirms that the proposed algorithm will exponentially converge to the optimal solution provided that the underlying communication graph is undirected and connected. Additionally, we demonstrate that our event-triggered scheme is not subject to Zeno behaviour, which is a theoretical concern in systems with frequent event triggers.

Attitude tracking control of a foldable wave energy powered AUV based on linear time-varying MPC under position constraints

In this paper, we investigate the tracking control of attitude and specific spatial position for a foldable wave energy powered autonomous underwater vehicle (FWEPAUV) under space constraints. In order to reduce energy consumption and maximizing the efficiency of the charging, a cascaded dual closed-loop control strategy is proposed. Firstly, we utilize model predictive control (MPC) with multi-variable constraint capabilities to design the kinematics controller. The output constraints are set as space constraints for MPC, and an objective function is used to strike a balance between control accuracy and energy consumption. The improved chaotic firefly algorithm (ICFA) is chosen due to its rapid convergence and strong robustness. It is employed in the rolling optimization process of MPC to secure a viable solution. Secondly, the kinematics controller is combined with the dynamics controller based on adaptive sliding mode control (ASMC) to achieve robust tracking control. In order to eliminate the interference from time-varying ocean currents and complex external forces meanwhile utilizing these interferes to reduce energy consumption, An ocean current observer based on ICFA-PI control and a prescribed time disturbance observer (PTDO) are devised to estimate them. Finally, an event-triggered (ET) mechanism is applied to integrate with the trajectory tracking controller to decrease the computational load. The stability of the ICFA-MPC controller and the ASMC controller is proven using the Lyapunov stability theory. Simulation results demonstrate that in complex external interference environments, the controller can achieve stable tracking control of the attitude and a specific spatial position, thereby meeting predefined goals such as space constraints, energy savings, and maximizing the efficiency of the charging.

On robustification of digital event-based controllers for control-affine nonlinear systems

In this paper, the robust digital stabilization problem of nonlinear systems is investigated. In particular, a methodology for the design of robust quantized sampled-data stabilizers updated via an event-triggered mechanism is provided for time-varying control-affine nonlinear systems affected by actuation disturbances and measurement noises. The notion of time-varying steepest descent feedback (TSDF), continuous or not, and the Input-to-State Stability (ISS) redesign methodology are used for the development of the proposed robust event-based digital controller. Under the assumption that the actuation disturbances and measurement noises are bounded with a-priori known bounds and that the amplitude of the measurement noises satisfies a certain condition related to the new added robustification term, the following result is proved: there exist a suitably fast sampling and an accurate quantization of the input/output channels such that the digital implementation of robustified TSDF controllers, updated through a proposed event-triggered mechanism, ensures semi-global practical stability of the related closed-loop system regardless of the above uncertainties. In the methodology here proposed, time-varying sampling periods and the non-uniform quantization of the input/output channels are allowed. Moreover, the theory here developed includes the analysis of the intersampling system behaviour. Possible discontinuities in the function describing the TSDF at hand are also managed. The provided results are validated through a numerical example.

Event-triggered quasi-time-varying [formula omitted] filtering for switched systems via multiple trigger-dependent Lyapunov functionals

This paper investigates the event-triggered filtering problem for switched systems from the perspective of switched impulsive systems (SISs) by exploiting the characteristics of trigger and impulse behavior. By constructing multiple trigger-dependent Lyapunov functionals (MTDLFs), an event-triggered quasi-time-varying (ET-QTV) H∞ filtering scheme is developed. Since the MTDLFs properly capture the event-induced impulse property of SISs, they release less conservatism. The filtering stability in the scenario of frequent plant switching during an interevent interval is also guaranteed by incorporating the average dwell time switching. The filtering synthesis condition is formulated as a set of QTV matrix inequalities defined over a finite time horizon, based on which a sum of squares-based filter design algorithm is presented further. Additionally, the derivation of a lower bound on interevent intervals is clarified for the event-triggering mechanism (ETM) to exclude the Zeno phenomenon. Finally, the effectiveness and potential practicability of the proposed filtering scheme are validated using a morphing aircraft model.

Discrete-Time Event-Triggered Type-2 fuzzy wavelet neural network control for Multi-Motor servo system

This paper investigates a discrete-time event-triggered adaptive neural network control scheme for a multi-motor servo system (MMSS) to realize a desired position tracking performance. First, a discrete-time model of the MMSS, including a dead-zone and a nonlinear friction, is established based on the Euler’s discretization. Then, a discrete-time adaptive neural network controller is designed by integrating a type-2 fuzzy wavelet neural network (T2FWNN) and the backstepping technology. The neural network is not only used to estimate uncertain nonlinearities, but also can handle the non-causal problem caused by the conventional backstepping method. Meanwhile, a fixed threshold event-triggered mechanism along with the incorporation of a dead-zone operator is superimposed into the actual controller thus saving communicational resources. Besides, stability analysis proves that all the signals in the closed MMSS are bounded, and the position tracking error converges to a small neighborhood of the origin. Finally, abundant simulation experience results demonstrate the effectiveness and robustness of the proposed scheme.

State estimation of singularly perturbed Semi-Markov jump coupled neural networks: A two-time-scale event-triggered approach

The state estimation problem of singularly perturbed semi-Markov jump coupled neural networks is investigated in this paper, in which a two-time-scale event-triggered mechanism is proposed to adapt the two-time-scale character of the singularly perturbed coupled neural networks such that mitigate the burden of data communication. A dual-rate sampling strategy is used for slow and fast states such that the proposed two-time-scale event-triggered mechanism contains two independent event-triggered conditions corresponding to the slow and fast states respectively. Based on the two-time-scale event-triggered mechanism, sufficient conditions for the H∞ performance analysis of singularly perturbed semi-Markov jump coupled neural networks are established. Furthermore, under the circumstances that the singularly perturbed parameter does not exceed the presupposed upper bound, a design algorithm is developed for the two-time-scale state estimator to ensure that the error dynamics are stochastically stable. Finally, an example is used to illustrate the effectiveness of this method used in singularly perturbed semi-Markov jump coupled neural networks.

RBF-based event-triggered fixed-time stable and chattering-free controller for load following of the pressurized water reactor

Preventing chattering phenomenon in load following control of the Pressurized Water Reactor (PWR) is still a main challenge. This article focuses on the chattering-free and rapid load following control strategy for PWR. Firstly, by proposing a newly adaptive updated law, the Radial Basis Function Neural Networks (RBFNNs) is employed to compensate the unmeasured states of relative delayed neutron precursor density, average fuel temperature and uncertain disturbances simultaneously online. On this basis, a fixed-time stable controller including saturation function is constructed to guarantee that the actual output power (OP) of nuclear reactor can follow the desired power (DP) within a fixed-time and the chattering phenomenon is alleviated. Additionally, to save the limited network resource and reduce the cost of computation, an event-triggered mechanism is constructed. Based on it, a chattering-free fixed-time stable controller is structured to ensure that the actual reactivity of control rod (ARCR) converges to the desired control rod reactivity (DCRR) within a fixed-time, meanwhile, it can eliminate Zeno phenomenon during the control process. Finally, the effectiveness and feasibility of the theoretical results are demonstrated through simulation examples.

Event-triggered synchronization for delayed reaction–diffusion neural networks under hybrid deception attacks

This paper focuses on designing some event-triggered mechanisms to synchronize the delayed reaction–diffusion neural networks (DRDNNs) under hybrid stochastic deception attacks. Firstly, two different kinds of stochastic deception attacks are considered for DRDNNs system. Based on the distinct characteristics of the attack signals, a novel adaptive event-triggered control mechanism is proposed to synchronize DRDNNs under hybrid deception attacks subject to Lipschitz form, and a novel aperiodic event-triggered control mechanism is proposed to synchronize DRDNNs under hybrid deception attacks subject to bounded form. Using the Lyapunov theory and some inequality techniques, several event-triggered synchronization criteria for DRDNNs under hybrid deception attacks are established. Furthermore, Zeno behavior is strictly excluded by proving the existence of lower bounds for the intervals between any adjacent events in the two designed event-triggered mechanisms. Finally, two numerical examples are provided to illustrate the effectively and superiority of the two proposed event-triggered mechanisms.

Event-triggered adaptive neural prescribed performance admittance control for constrained robotic systems without velocity measurements

In this paper, an event-triggered adaptive neural prescribed performance admittance control (ETANPPAC) scheme is proposed to control the constrained robotic systems without velocity sensors. To ensure compliance during human–robot interaction, the reference trajectory is obtained by reshaping the desired trajectory for the robotic systems based on the admittance relationship, where a saturation function is used to constrain the reference trajectory, avoiding excessive contact forces that could render the trajectory inexecutable. Moreover, a barrier Lyapunov function is used to constrain the tracking errors for prescribed performance, where a velocity observer and a radial basis function neural network are designed to estimate the velocity and the uncertainty of the robotic systems, respectively, to enhance control performance. To reduce the communication burden, an event-triggered mechanism is introduced and the Zeno behavior is avoided with the event-triggered condition. The stability of the whole control scheme is analyzed by the Lyapunov function. Simulation and experimental tests demonstrate that the proposed ETANPPAC scheme can track the desired trajectory well under constraints and reduce the communication burden, thereby achieving better efficiency for controlling the robotic systems compared with similar control schemes.