Event-triggered Control Scheme Research Articles

Event-Triggered Composite Learning Finite-Time Trajectory Tracking Control for Underactuated MSVs Subject to Uncertainties

In this paper, a novel event-triggered composite learning finite-time control scheme is presented for underactuated marine surface vehicles (MSVs) trajectory tracking under unknown dynamics and unknown time-varying disturbances. Line-of-sight (LOS) tracking control method is employed to address the underactuation problem of MSVs. The neural networks (NNs) are untilized to approximate unknown dynamics. The serial-parallel estimation model is employed to construct the prediction error, and the prediction errors and tracking errors are fused with construct the NN weights updating. Combining the result of approximation information, the disturbances observers can be created to achieve disturbance estimation. Fractional power technology is artistically introduced to realize the finite-time trajectory tracking control of MSVs based on composite learning. The proposed control scheme ensures the simultaneous realization of high precision tracking performance and unknown information approximation. Moreover, an event-triggered mechanism is introduced to reduce the transmission load and the execution rate of actuators. It is proved that the proposed control scheme ensures all error signals of the MSVs trajectory tracking control system can converge to the neighborhood of zero within a finite time. Finally, the simulation results on an MSV verify the effectiveness and superiority of the proposed control scheme.

Time-Varying Transformation-Based Adaptive Tracking Control of Uncertain Robotic Systems With Event-Triggered Mechanism

In this paper, an adaptive global event-triggered control scheme for uncertain robotic systems, capable of guaranteeing adjustable tracking performance, is developed. Compared with most of the existing results, the proposed control exhibits some features. Firstly, to improve the transient tracking performance without the requirement of large control effort, a time-varying scaling diagonal matrix and an error transformation are constructed so that each element of the tracking error has its own convergence rate, which can be adjusted by selecting the design parameters. Secondly, due to the consideration of decreasing the consumption of network resources, some additional diagonal matrices embedded in the new closed-loop system make the new control gain matrix not symmetric and positive definite, then the stability analysis becomes rather more challenging. To solve this issue, an alternative Lyapunov function is constructed to circumvent the above obstacles. The effectiveness of the proposed approach is verified by a 2-DOF robotic manipulator.

An Event-Triggered Watermarking Strategy for Detection of Replay Attacks

The problem of detecting replay attacks in linear time-invariant discrete-time systems is considered in this paper. In the same spirit of watermarking techniques that apply a distinctive signature to the plant\’s signals, we propose an event-triggered control scheme, that is purposely designed to generate a unique sequence of switching intervals, by computing an appropriate input value to be held constant while the communication is not triggered. We provide a detailed undetectability characterization in the time domain and design a controller that achieves the desired behavior. Our proposed method results in a control scheme that makes it hard for an attacker to satisfy undetectability conditions, and, as a result, a standard observer-based residual generator can be employed to reveal replay attacks. We finally validate the method using a numerical example.

Finite-Time Control for Connected Vehicles Under Denial-of-Service Attacks: A Dynamic Event-Triggered Control Strategy

Finite-Time Control for Connected Vehicles Under Denial-of-Service Attacks: A Dynamic Event-Triggered Control Strategy

Event-triggered [formula omitted]/passive synchronization for Markov jumping reaction–diffusion neural networks under deception attacks

The issue of H∞/passive master–slave synchronization for Markov jumping neural networks with reaction–diffusion terms is investigated in this paper via an event-triggered control scheme under deception attacks. To lighten the burden of limited communication bandwidth as well as ensure the control performance, an event-triggered transmission scheme is developed. Meanwhile, the randomly occurring deception attacks, which received from the event generator are assumed to modify the sign of the control signal, are taken into account. Furthermore, sufficient conditions ensuring the prescribed H∞/passive performance level of the neural networks, are deduced beyond Lyapunov stability theory, and the controller gains are derived dealing with the matrix convex optimization problem. At last, the availability of the approach proposed is demonstrated via a numerical example.

Event-triggered-based fixed-time adaptive prescribed tracking control for stochastic nonlinear systems with actuator faults

In this paper, the event-triggered fixed-time control scheme is developed for a class of stochastic nonlinear systems with prescribed boundary constraints and actuator faults. For the controlled systems with unknown nonlinear functions, the neural networks are employed to reestablish the system model. Based on the event-triggered control technology, an original adaptive fixed-time control strategy with prescribed performance and the fault state is proposed by using the backstepping technique. Under the developed adaptive controller, the tracking error satisfies the predefined boundary functions and all the closed-loop system signals are bounded in probability in a fixed time, and the convergence time is irrelevant to the initial states of the system. The practicability of the designed controller is illustrated by a simulation example.

Event-triggered adaptive fuzzy fault-tolerant control for autonomous underwater vehicles with prescribed tracking performance

In this paper, we solve the trajectory tracking control problem of autonomous underwater vehicles (AUVs) with actuator faults and model uncertainties. In order to achieve the output tracking with prescribed transient performance, we develop a novel tan-type barrier Lyapunov function (BLF)-based funnel tracking control strategy to constrain the position tracking error of the AUV within prespecified performance funnels. Furthermore, to maintain the predesigned transient tracking performance of the AUV when the actuator fault occurs, an event-triggered adaptive fuzzy fault-tolerant control (FTC) scheme is proposed to compensate actuator faults. Meanwhile, to handle the uncertainties caused by the system model, fuzzy logic systems (FLSs) are adopted to approximate the unknown hydrodynamic parameters and actuator fault parameters. Different from the existing results, the communication burden and the excess wear from the controller to the actuators are reduced utilising the event-triggered mechanism. By using the theory of Lyapunov stability, it is proven that all the signals of the closed-loop system are semiglobally uniformly bounded. Finally, the effectiveness of the proposed method is demonstrated by a simulation example.

Event-triggered intelligent critic control with input constraints applied to a nonlinear aeroelastic system

In this paper, we establish an event-triggered intelligent control scheme with a single critic network, to cope with the optimal stabilization problem of nonlinear aeroelastic systems. The main contribution lies in the design of a novel triggering condition with input constraints, avoiding the Lipschitz assumption on the inverse hyperbolic tangent function. Based on an improved weight updating criterion that eliminates the requirement of initial admissible control, the control law is obtained approximately by online training of a single critic network. The Lyapunov stability and the Zeno phenomenon of the closed-loop system are analysed. The feasibility of the established algorithm is verified by applying it to an optimal stabilization task of a nonlinear aeroelastic system. The results reveal that the developed approach can handle input-constrained optimal control problems, with performance comparable to the time-based method that updates control inputs at each instant, while reducing the computational and communication's load.

Event-triggered data-driven control of discrete-time nonlinear systems with unknown disturbance

This paper is dedicated to event-triggered data-driven control of nonlinear systems with unknown disturbance via model free iterative learning approach. An extended state observer is employed to reconstruct the disturbance in system output. An event-triggered model free iterative learning control strategy is constructed by system input, system output and the reconstructed disturbance. Sufficient conditions are proposed to make the resultant tracking error system be uniform ultimate bounded. Simulation examples are provided to validate the effectiveness of the proposed scheme.

Dynamic event-triggered output consensus of nonstrict-feedback MASs with predefined performance

This paper deals with the output consensus problem for uncertain nonstrict-feedback leader-follower multi-agent systems with predefined performance. A distributed event-triggered control strategy with dynamic threshold is proposed to update the actual control input and alleviate the computation burden of the communication procedure effectively. The unknown nonstrict-feedback structures are addressed by using the property of radial basis function neural networks. It is worth noting that in practical applications, the predefined performance often alternates between constrained and unconstrained cases in some extreme situations. To overcome this challenge, a novel coordinate transformation technique is incorporated to tackle both the two cases with and without performance constraint in a unified manner. As a result, the proposed event-triggered control approach ensures that the output consensus errors converge to zero asymptotically, and all the signals in the closed-loop system are bounded. Finally, the effectiveness of the proposed protocol is demonstrated by the simulation results.

Sliding mode differentiator-based event-triggered control for state-constrained nonlinear systems with unknown virtual control coefficients

In this paper, a novel event-triggered (ET) adaptive neural tracking control scheme is proposed for uncertain strict-feedback systems subject to asymmetric time-varying full-state constraints, unknown virtual control coefficients and external disturbances. The lower-order time-varying asymmetric barrier Lyapunov function (TVABLF) is constructed to ensure that state constraints are not transgressed. Then, a simple backstepping-based control procedure is developed to remove the existing restrictions on a high power of piecewise TVABLF and high-order differentiability of virtual laws. In particular, a novel adaptive updating law is constructed to co-design the controller and the ET scheme by the combination of the Nussbaum gain technique, thereby solving the ET controller design difficulty resulting from unknown control coefficients. The proposed scheme can guarantee all states within the time-varying constrained areas, achieve the satisfactory tracking ability and save the communication resource. Finally, the effectiveness of the proposed scheme is testified by numerical and practical examples.

Event-triggered synchronization control of complex networks with adaptive coupling strength

The event-triggered synchronization control problem is concerned for a class of complex networks with nonlinearly coupling function and adaptive coupling strength. Given a state-based event-trigger mechanism and the threshold, an event-triggered control method is introduced to make complex networks achieve exponential synchronization. By combining the Lyapunov stability theory and the knowledge of graph theory, a sufficient condition is established such that complex networks can achieve exponential synchronization. Then, the feasibility of the event-triggered control is analyzed. Moreover, the second-order Kuramoto oscillators is taken into account. And the event-triggered control strategy is used to make the oscillators achieve exponential synchronization. Meanwhile, two simulation results about the second-order Kuramoto oscillators are given to show the effectiveness of results.

Adaptive critic designs for decentralised robust control of nonlinear interconnected systems via event-triggering mechanism

In this article, a novel decentralised event-triggered control scheme is developed for the robust stabilisation of interconnected systems. The existing literature about decentralised robust control by adaptive critic designs (ACDs) is mostly time-triggered which requires extensive communication and computation. To extend the ACD framework and save the communication and computational resources, the event-triggered decentralised control using ACDs is investigated. First, we utilise a series of auxiliary subsystems to restructure the interconnected systems in a distributed way, so as to transform the robust control problem into the corresponding optimal control problem. Then, adaptive critic designs with event-triggering mechanism are investigated to solve the Hamilton–Jacobi–Bellman equations via local policy iteration algorithm. The single-critic networks are employed to achieve the approximation of optimal value functions. The Lyapunov stability proof ensures the asymptotic stability of the whole system and the uniform ultimate boundedness of the critic neural networks. Finally, an interconnected plant is employed to demonstrate the effectiveness of proposed method.

Sampled-data and event-triggered boundary control of a class of reaction–diffusion PDEs with collocated sensing and actuation

This paper provides observer-based sampled-data and event-triggered boundary control strategies for a class of reaction–diffusion PDEs with collocated sensing and Robin actuation. Infinite-dimensional backstepping design is used as the underlying control approach. It is shown that the continuous-time output feedback boundary control applied in a sample-and-hold fashion ensures global closed-loop exponential stability, provided that the sampling period is sufficiently small. Further, robustness to perturbations of the sampling schedule is guaranteed. For the event-triggered implementation of the continuous-time controller, a dynamic triggering condition is utilized. The triggering condition determines the time instants at which the control input needs to be updated. Under the observer-based event-triggered boundary control, it is shown that there is a minimal dwell-time between two triggering instants independent of initial conditions. Further, the global exponential convergence of the closed-loop system to the equilibrium point is established. A simulation example is provided to validate the theoretical results.

Observer-based event-triggered control for linear MASs under a directed graph and DoS attacks

In this paper, we investigate the observer-based event-triggered consensus problem for linear multi-agent systems (MASs) under a directed graph and denial-of-service (DoS) attacks. A type of DoS attacks launched by malicious attackers at irregular intervals is considered, which can cause communication channel disruption. A novel event-triggered secure control scheme based on a closed-loop observer is proposed to determine the scheduling of the controller update, and a separation method with less conservativeness is employed to design the controller and observer gains. Then, the frequency and duration of DoS attacks that can be tolerated are analysed for the observer-based secure consensus problem. In addition, a strictly positive minimal event-triggered time interval for each agent is designed with the help of the proposed event-triggered condition to eliminate the Zeno behaviour. Finally, a numerical simulation is given to verify the theoretical analysis.

Exponential Synchronization of Complex Networks: An Intermittent Adaptive Event-Triggered Control Strategy

This paper investigates the exponential synchronization (ES) problem for complex networks (CNs) under an intermittent adaptive event-triggered control (IAE-TC) strategy which is based on dynamic IAE-TC during the control activation intervals. In this control mechanism, the intermittent controller has adaptability to the evolution results of the controlled networks and is activated only when the event-triggered condition is violated. Then, by employing this kind of control strategy and the Lyapunov method, some sufficient conditions are proposed to achieve the ES of CNs and it is proven that the Zeno behavior can be eliminated. Besides, an application about the islanded microgrid system and some numerical simulations are provided to verify the effectiveness of the derived theoretical results. Moreover, in the numerical simulations, it is shown that the dynamic IAE-TC strategy can decrease the number of event-triggered instants more availably than the static one.

Input-to-State Stability of Nonlinear Systems Using Observer-Based Event-Triggered Impulsive Control

In this article, we are concerned with the input-to-state stability (ISS) problem for a class of nonlinear systems with exogenous disturbances, where the states of the system are not fully available. Based on the idea of event-triggered control (ETC) strategy, a novel event-triggered mechanism (ETM) is designed to reduce the burden of the communication and controller updating with guaranteed performance requirement. Correspondingly, an observer-based impulsive controller coupled with sample control is proposed such that the controlled system is ISS under the designed ETM. Moreover, the possible accumulations of triggered instants (i.e., Zeno behavior) in the proposed control strategy are excluded. The controller gains and ETM parameters are co-designed by solving linear matrix inequalities (LMIs). Finally, a numerical example is provided to illustrate the effectiveness of the obtained theoretical results.

Event-Triggered Mechanism Based Control Method of SMES to Improve Microgrids Stability Under Extreme Conditions

Microgrids with multiple converters suffer from excessive pressure on the communication network during traditional voltage and frequency regulation control. This paper proposed an event-triggered control strategy based superconducting magnetic energy storage (SMES) scheme to improve AC microgrids stability under successive disconnection of sources or step change of loads. By designing an event-triggered mechanism, each converter in the microgrid sends its own information to SMES in a non-periodic way. Then the control method of bus voltage and frequency regulation using SMES is constructed, which enables SMES to update its control mode. The microgrid model is established in PSCAD/EMTDC, and two cases of extreme conditions are conducted to demonstrate the effectiveness of the proposed method.

Event triggered control for fault tolerant control system with actuator failure and randomly occurring parameter uncertainty

The paper investigates the event-triggered control problem for fault tolerant control system with actuator failure and randomly occurring parameter uncertainty. Firstly, the hybrid event-triggered control scheme is adopted for the fault tolerant control system subjected to actuator failure and randomly occurring parameter uncertainty in this paper, which could greatly decrease the amount of network data transmission and save communication resources. By utilizing the delay system method, the event-triggered fault tolerant control system is modeled as a delay system. Then, by constructing appropriate Lyapunov–Krasovskii functional, sufficient conditions are derived in form of linear matrix inequalities (LMIs) to guarantee the system is stochastically passive with prescribed passive performance γ. The impact of the actuator fault and the random occurring parameter uncertainty are studied under the unified framework. Eventually, two simulation examples including F-404 aircraft engine system are presented to illustrate the effectiveness of the proposed theoretical findings.

Event triggered control of connected vehicles under multiple cyber attacks

This paper investigates an event triggered control problem of connected vehicles under multiple cyber-attacks, including denial-of-service attacks (DoS) and deception attacks, which occur aperiodically. Then, a novel event triggered control scheme is proposed in the presence of multiple cyber-attacks. The upper bound of attack frequency, attack length rate of DoS attacks and the conditions of controller gains are obtained. It is proven that the proposed distributed control scheme can realize the asymptotic tracking performance of connected vehicles while avoiding Zeno behavior. A string stability analysis under multiple cyber-attacks is also provided. Simulation results confirm the effectiveness of the event triggered control schemes under multiple cyber-attacks.