Event-triggered Mechanism Research Articles

Distributed MPC with composite event-triggering mechanisms for nonlinear multi-agent systems

Distributed MPC with composite event-triggering mechanisms for nonlinear multi-agent systems

Anti-bump control for switched systems with decentralised adaptive event-triggering

A control system may exist bumps during mode transitions and discontinuous control input would seriously affect performance. In this paper, the anti-bump (or bumpless) switching control for triggered and switched systems under injection attacks is studied. This control method combines decentralised adaptive event-triggering mechanism (DAETM) and anti-bump condition. The main work is as follows: First, a DAETM is considered to allocate network resources properly and resist data injection attacks. Then, a data buffer is adopted to consolidate data from decentralised channels for ensuring timely utilisation. In addition, a controller gain condition is presented to achieve the bumpless switching between subsystems. Moreover, by adopting piecewise Lyapunov function method, sufficient conditions are obtained for triggered switched systems to be exponentially stable and anti-bump under injection attacks. Finally, the design method effectiveness is verified by actual circuit simulation.

Adaptive connectivity-preserving formation control with a dynamic event-triggering mechanism

Connectivity preservation for real-life multi-agent systems is key to designing an effective and reliable control algorithm to achieve desired objectives. But the coexistence of system nonlinearities and uncertainties makes it difficult for the algorithm design, and especially in the event-triggered setting, such difficulty becomes severer due to the demand for integration of multiple ingredients. This paper focuses on proposing an adaptive connectivity-preserving formation control strategy that incorporates a dynamic event-triggering mechanism, in the scenario of the unknown control directions and intrinsic nonlinearities coupling with parameter uncertainties. First, a cluster of potential functions serving as control barrier functions are given to maintain the prescribed connectivity of communication graph. Second, two types of dynamic gains (one type is based on a Nussbaum function) are introduced for agents to cope with the system nonlinearities, uncertainties and the adverse effect of the execution error. As such, an adaptive event-triggered formation protocol is constructed such that the desired formation with connectivity preservation is achieved while a positive minimum inter-execution interval is ensured for each agent to avoid Zeno behavior. Particularly, the threshold in the developed event-triggering mechanism is dynamically adjusted online, rather than static, which better improves resource efficiency. A simulation example is given to demonstrate the effectiveness and advantage of the proposed strategy.

Adaptive Neural Network Synchronous Tracking Control for Teleoperation Robots Under Event-Triggered Mechanism

Adaptive Neural Network Synchronous Tracking Control for Teleoperation Robots Under Event-Triggered Mechanism

Adaptive neural network event-triggered secure formation control of nonholonomic mobile robots subject to deception attacks

This paper investigates the adaptive neural network (NN) event-triggered secure formation control problem for nonholonomic mobile robots (NMRs) subject to deception attacks. The NNs are employed to approximate unknown nonlinear functions in robotic dynamics. Since the transmission channel from sensor-to-controller is vulnerable to deception attacks, a NN estimation technique is introduced to estimate the unknown deception attacks. In order to alleviate the amount of communication between controller-and-actuator, an event-triggered mechanism with relative threshold strategy is established. Then, an adaptive NN event-triggered secure formation control method is proposed. It is proved that all closed-loop signals of controlled systems are bounded and the formation tracking errors converge a neighborhood of the origin in the presence of deception attacks. The comparative simulations illustrate the effectiveness of the proposed secure formation control scheme.

Double adaptive event-triggered strategy for leader-following group consensus of heterogeneous multi-agent systems in cooperative–competitive networks

This article investigates adaptive event-triggered control for leader-following group consensus of hybrid heterogeneous multi-agent systems (HMASs) composed of single- and double-integrator agents under cooperative–competitive relationships. By using fully distributed control protocol and adaptive protocol, the leader-following group consensus can be achieved without global information. Additionally, a novel adaptive double dynamic event-triggered mechanism (DDETM) is proposed, in which two independent adaptive event-triggered conditions are devised to maintain events of communication and updates of controllers. Furthermore, adaptive parameters and internal dynamic variables are employed to reduce the number of triggers and exclude Zeno phenomenon. By utilizing Lyapunov stability theory, some sufficient conditions are acquired for the leader-following group consensus of the HMASs. Finally, simulation examples are given to prove the effectiveness of the proposed method.

Periodic event-triggered data-driven control for networked control systems with time-varying delays

This article focuses on data-driven analysis and controller design for networked control systems (NCSs) with network-induced delays. The study considers a linear time-invariant (LTI) system controlled through a periodic event-triggering mechanism. First, by leveraging data-based representations, we establish data-based stability conditions for NCSs with time-varying delays. Furthermore, we propose the data-based method for co-designing the controller and the periodic event-triggering scheme. In addition, we present novel data-based conditions for verifying dissipativity properties of NCSs. The effectiveness of our proposed methods is validated through a simulation and a turbofan engine hardware-in-the-loop (HIL) experiment.

Event-triggered iterative learning containment control for one-sided Lipschitz nonlinear singular switched multi-agent systems with multiple leaders and error quantization

In this work, under a repeatable control environment, the iterative learning containment control issue for multi-agent systems with one-sided Lipschitz nonlinear singular switched dynamics is addressed. It focuses on the output containment for one-sided Lipschitz nonlinear singular switched multi-agent systems (SWMASs) with multiple leaders, for which an event-triggered containment quantized iterative learning control (QILC) scheme is designed. Due to the limited system resources, SWMASs are often restricted in their communication capabilities and the frequency of controller updates in many applications, the proposed event-triggering mechanism can greatly restrict the use of communication and computation resources throughout the iterative learning process. When the triggering requirement is met, the event-triggered QILC input is repeatedly updated; alternatively, the control input stays unchanged. To conquer the nonidentical initial states of followers, an initial state learning with error quantization and event-triggering is also utilized. By virtue of some appropriate learning gains and sufficient convergent conditions, the containment error convergence can be accomplished for one-sided Lipschitz SWMASs. Finally, some numerical examples via Matlab are provided to demonstrate the efficacy of the event-triggered QILC scheme, it indicates that followers’ outputs can converge to the convex hull formed by leaders and the containment errors gradually converge to the 10−3 - vicinity of the origin along the iteration axis, where the communication resources can save more than 50% in the transmission channel.

A Double Sensitive Fault Detection Filter for Positive Markovian Jump Systems with A Hybrid Event-Triggered Mechanism

A Double Sensitive Fault Detection Filter for Positive Markovian Jump Systems with A Hybrid Event-Triggered Mechanism

Attack-Dependent Adaptive Event-Triggered Security Fuzzy Control for Nonlinear Networked Cascade Control Systems Under Deception Attacks

This article investigates the issue of H∞ security output feedback control for a nonlinear networked cascade control system with deception attacks. First, to further reduce the amount of communication data, reasonably schedule network resources, and alleviate the impact of multi-channel deception attacks, an attack-dependent adaptive event-triggered mechanism is introduced into the primary network channel, and its adaptive triggered threshold can be adjusted according to the random attack probability. Secondly, the output dynamic quantization of the secondary network channel is considered. Then, a novel security cascade output feedback controller design framework based on the Takagi–Sugeno (T-S) fuzzy networked cascade control system under deception attacks is established. In addition, by introducing the Lyapunov–Krasovskii stability theory, the design conditions of the controller are given. Finally, the effectiveness and superiority of the proposed design strategies are verified by two simulation examples of power plant boiler–turbine system and power plant boiler power generation control system.

Relaxed Event Triggered Control of Discrete‐Time Takagi‐Sugeno Systems Based on Homogeneous Polynomial Method

ABSTRACTThis article presents an event triggered control approach for discrete‐time T‐S fuzzy systems, which integrates the homogeneous polynomial method and a dynamic event triggering mechanism. First, a kind of discrete‐time loop functions and the dynamic event triggering scheme based on periodic sampling are constructed to establish stability conditions with known system information. Second, by introducing the homogeneous polynomial method to relax the stability criteria, the sampling interval is increased and the system conservatism is reduced. Additionally, an integrated framework for collaboratively designing the event triggering functions and controllers is proposed, which reduces the complexity of sampling transmission while ensuring system stability. Finally, numerical and practical examples are used to illustrate the feasibility and effectiveness of the proposed method.

Finite-time bounded security control of networked switched stochastic systems

This paper is dedicated to discussing stochastic finite-time bounded control of switched stochastic systems with feedback information sampled by an event-triggered mechanism and transmitted by network. Besides the incomplete information because of sample, data loss and corruption during information transmission caused by hybrid cyber attacks including DoS attacks and false-data injection attacks, are also taken into consideration. Based on the above incomplete information, state feedback controllers are established to fulfil stochastic finite-time boundedness of switched stochastic systems and make it have a certain disturbance attenuation capability, i.e. have a finite-time L 2 -gain, successfully. Sufficient conditions are cast into a convex optimisation problem which can be easily solved by fixing some parameters. Simulation results are employed to verify the validity of results.

Fault-tolerant control of nonlinear networked control systems based on a dynamic event-triggered mechanism under dual cyber attacks

In this paper, the fault-tolerant control problem of actuator failures for nonlinear networked control systems under deception attacks and denial-of-service attacks is investigated based on a dynamic event-triggered mechanism. Firstly, denial-of-service attacks and deception attacks are modeled as two independent variables that conform to the Bernoulli probability distribution, and the fault-tolerant control problem of nonlinear networked control systems is considered under the possible actuator failures. Then, an appropriate Lyapunov-Krasovskii functional is constructed, and sufficient conditions for ensuring asymptotic stability of the closed-loop systems are obtained using Jensen inequality. Meanwhile, the designed fault-tolerant controller can handle possible actuator failures. Finally, the effectiveness of the proposed method is proved through an example.

Asynchronous Sliding Mode Control of Networked Markov Jump Systems via an Asynchronous Observer Approach Based on a Dynamic Event Trigger

This paper explores the utilization of sliding mode control, which relies on an asynchronous observer, for Markov jump systems subject to external disturbances. Firstly, given that the system’s mode is not directly measurable and could potentially differ from the observer’s and controller’s mode, the paper constructs an asynchronous observer employing a hidden Markov model. Secondly, a sliding surface is designed to correspond with the asynchronous observer. Moreover, a multi-parameter event-triggered mechanism is incorporated into the observer design to alleviate bandwidth strain. Thirdly, by applying the integrated sliding mode control law, we ensure that the system state trajectories will reach the sliding surface within a finite time. Fourthly, the achievement of H∞ stability is realized by making use of the Lyapunov function. Lastly, a practical-oriented example is presented to illustrate the efficiency of the established method.

Tobit Kalman fusion filtering under dynamic event-triggering protocol with token bucket specification

This paper addresses the multi-sensor fusion filtering problem for a class of linear discrete time-varying systems with censored measurement, described by the Tobit model, and scheduled by dynamic event-triggering protocols with token bucket specification. A dynamic event-triggering mechanism is first used to determine whether to transmit measurements under the token bucket specification, allowing transmission only if there are sufficient tokens and if the event-triggering condition is satisfied. Next, two indicator variables are denoted to represent the combined impact of the dynamic event-triggering protocol and the token bucket specification. A local Tobit Kalman filtering algorithm is then designed for each node by minimizing the trace of the filtering error covariance matrix under censoring and information transmission protocols' influence. Subsequently, all local estimates from each node are transmitted to the fusion center, where global estimates are generated using a federal fusion rule. The global estimates with suitable weights are sent back to every node for predictions at subsequent time instants. Finally, an illustrative simulation example is used to evaluate performance of this fused filtering scheme proposed in this paper.

Active disturbance rejection control for systems with uncertainties and noise using super twisting differentiator-based event-triggered ESO

This paper focuses on the noise suppression issue of high-gain extended state observer (ESO) for systems with uncertainty and unknown measurement noise in the active disturbance rejection control (ADRC) paradigm. A special combination of super twisting differentiator (STD) and event-triggered ESO is devised with a designed event-triggered mechanism. Specially, the proposed STD-ETESO incorporates a finite-time switching strategy, which improves the poor transient performance of the STD acting as a pre-filter to prepare relatively smooth signals for the ESO. The locally asymptotic boundedness of the estimation error can be guaranteed by the Input-state-stability (ISS) and existing research for the STD. The effectiveness of the proposed method is validated by a general example and a practical motion control system. Different integral criteria is introduced to evaluate the overall tracking accuracy and energy usage for comparison with existing different ESOs, which reveals the superiority of the proposed STD-ETESO in noise attenuation and disturbance rejection.

Distributed leader-following bipartite consensus for one-sided Lipschitz multi-agent systems via dual-terminal event-triggered mechanism

This article analyses leader-following bipartite consensus for one-sided Lipschitz multi-agent systems by dual-terminal event-triggered output feedback control approach. A distributed observer is designed to estimate unknown system states by employing relative output information at triggering time instants, and then an event-triggered output feedback controller is proposed. Dual-terminal dynamic event-triggered mechanisms are proposed in sensor–observer channel and controller–actuator channel, which can save communication resources to a great extent, and the Zeno behavior is ruled out. A new generalized one-sided Lipschitz condition is proposed to handle the nonlinear term and achieve bipartite consensus. Some stability conditions are presented to guarantee leader-following bipartite consensus. Finally, one-link robot manipulator systems are introduced to demonstrate the availability of the designed scheme. The results demonstrate that the agents of the robot manipulators can track the reference trajectories bi-directionally, and effectively reduce communication resources by 61.22% and 68.04% at the sensor–observer and controller–actuator channels, respectively.

A distributed algorithm with dynamic event-triggered mechanism for resource allocation problems

A distributed algorithm with dynamic event-triggered mechanism for resource allocation problems

An energy trade-off management strategy for hybrid ships based on event-triggered model predictive control

This paper addresses the energy management problem of hybrid ships by proposing an event-triggered model predictive control (ET-MPC) method. The novelty in this work lies in the establishment of an event-triggered mechanism and a state prediction model for energy management of hybrid ships. First, torque models of the internal combustion engine (ICE) and electric machine (EM) are developed using a data-driven approach, followed by the construction of fuel consumption and carbon emission models. Second, an event-triggered mechanism, dependent on state prediction error, is introduced and updated at each time step based on the system’s current state. Additionally, a cubature Kalman filter (CKF) is employed to estimate and correct the state prediction error, minimizing inaccuracies. A trade-off coefficient is incorporated to optimize the balance between fuel consumption and carbon emissions. The ET-MPC method results in a 0.68% difference in fuel consumption and 3.43% increase emissions compared to the traditional MPC method. However, ET-MPC significantly reduces computational overhead by 56.66. The ET-MPC method effectively allocates the ship’s energy according to the varying trade-off coefficient, achieving optimal energy management under different constraints.

Fuzzy Adaptive Event-Triggered Consensus Control for Nonlinear Multiagent Systems Under Jointly Connected Switching Networks.

This article studies the fuzzy adaptive event-triggered (ET) consensus control issue of nonlinear multiagent systems (NMASs) under jointly connected switching networks. Since the leader and its high-order derivatives are unknown under jointly connected switching networks, a novel distributed ET reference generator equipped with an ET mechanism is constructed to estimate them. Meanwhile, the continuous information transmission among agents is avoided and the network channel utilization is optimized. Subsequently, fuzzy logic systems (FLSs) are employed to approximate unknown dynamics, and a fuzzy adaptive ET consensus control algorithm only using intermittent communication is designed by backstepping control methodology. It is demonstrated that all the closed-loop signals are semi-globally uniformly ultimately bounded (SGUUB), with the tracking errors converging to a small neighborhood around zero. Finally, we apply the developed fuzzy adaptive ET consensus control algorithm to unmanned surface vehicles (USVs), and the simulation results verify the effectiveness of the proposed ET consensus control algorithm.