Event-triggered Control Law Research Articles

Observer-Based Fault-Tolerant Finite-Time Control of Nonlinear Multiagent Systems.

In this article, an adaptive neural containment control for a class of nonlinear multiagent systems considering actuator faults is introduced. By using the general approximation property of neural networks, a neuro-adaptive observer is designed to estimate unmeasured states. In addition, in order to reduce the computational burden, a novel event-triggered control law is designed. Furthermore, the finite-time performance function is presented to improve the transient and steady-state performance of the synchronization error. Utilizing the Lyapunov stability theory, it will be shown that the closed-loop system is cooperatively semiglobally uniformly ultimately bounded (CSGUUB), and the followers' outputs reach the convex hull constructed by the leaders. Moreover, it is shown that the containment errors are limited to the prescribed level in a finite time. Eventually, a simulation example is presented to corroborate the capability of the proposed scheme.

Distributed adaptive event-triggered finite-time fault-tolerant containment control for multi-UAVs with input constraints and actuator failures

This article investigates the distributed adaptive finite-time containment control problem for multi-UAVs with input constraints, actuator failures, communication limitations, and external disturbances. First, a new smoothing function is used to smooth the asymmetric input constraint signals so that the input constraint and actuator fault control problem can be transformed into a variable gain control problem. Subsequently, a new Nussbaum function is proposed to solve the variable gain control problem. A new adaptive event-triggered strategy is designed to solve the communication limitation problem, and the trigger threshold has the characteristic of adaptive adjustment that can be dynamically decreased. In response to external disturbances, an adaptive law is designed to estimate and compensate for the boundaries of disturbances. It follows from the analysis based on Lyapunov theory that under the proposed controller, the followers will converge to the convex envelope formed by the leaders in a finite time, and Zeno-free is achieved. Simulation results are provided to verify the effectiveness of the developed adaptive event-triggered finite-time fault-tolerant containment control laws.

Dynamic event-triggered neuro-optimal control for uncertain nonlinear systems with unknown dead-zone constraint

In this article, we propose a dynamic event-triggered neuro-optimal control scheme (DETNOC) for uncertain nonlinear systems subject to unknown dead-zone and disturbances through the design of a composite control law. An integral sliding mode-based discontinuous control law is utilized to compensate for the effects of unknown dead-zone, disturbance, and a component of uncertainties. As a result, a system dynamics that evolves free of these effects during the sliding mode is obtained. Then, an adaptive dynamic programming-based dynamic event-triggered optimal control law is designed to stabilize the sliding mode dynamics with the help of critic-only neural network architecture. Finally, stability analysis of the closed-loop system is provided and the effectiveness of the developed DETNOC scheme is verified.

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.

Memory-based event-triggered path-following control for a USV in the presence of DoS attack

This paper proposes a novel memory-based event-triggered path-following controller for unmanned surface vehicles (USVs) in the presence of denial of service (DoS) attacks and limited communication resources. In the guidance part, a double distance logical virtual ship (DDLVS) guidance law is developed to eliminate the accumulation error of the yaw angle velocity in the traditional LVS algorithm, and the USV will keep a smoother tracking performance for the curve reference path. In the control part, the memory-based event-triggered fusion control algorithm proposed in this paper incorporates the signal non-transmission phenomenon caused by DoS attacks into the event-triggered interval through a set of adaptive parameters. Consequently, the adverse affect caused by DoS attacks is solved in the process of designing the event-triggered control law. By combining the neural networks (NNs) and the dynamic surface control (DSC) techniques, the updating weights are significantly compressed. The proposed algorithm is with advantages of concise controller structure, low energy cost, and easy engineering implementation. All the error signals of the closed-loop system are proved to be semi-global uniform ultimate bound (SGUUB) by the Lyapunov analyses. Finally, through two numerical examples, including the comparative simulation and path following maneuver in Shuang island (an island distribute in Liaoning Dalian), are provided to verify the effectiveness of the proposed strategy.

Dynamic event-triggered synchronization for semi-Markovian switching inertial neural networks with generally uncertain transition rates in finite-time interval

This paper investigates the finite-time synchronization for inertial neural networks with stochastic switching parameters based on dynamic event-triggered protocol. Due to the complexity of network environment, semi-Markovian process is introduced into the modeling of inertial neural networks, in which the transition rates vary with the operating time. The dynamic event-triggered protocol is developed to determine whether the signal is transmitted, in which Zeno phenomenon is eliminated under limited bandwidth resources. The objective is to construct an appropriate dynamic event-triggered control law such that the drive-response system maintains finite-time synchronization under generally uncertain transition rates. Based on the Lyapunov functional theory, finite-time synchronization criterion is proposed for the related inertial neural networks. Furthermore, a dynamic event-triggered controller is constructed in a finite-time interval. A numerical example and an image encryption process are given to show the efficiency of the proposed method.

Fixed-time event-triggered sliding mode time-varying formation control for multiple surface vessels with fast convergence

ABSTRACT This paper proposes a new fixed-time sliding mode time-varying formation control strategy for multiple surface vessels (MSVs) to improve control performance and ensure navigation safety. First, a fixed-time extended state observer (FxESO) is designed to provide estimations of velocities and lumped disturbances. Then, the event-triggered fixed-time distributed time-varying formation control law is designed based on prescribed performance function (PPF) and fixed-time nonsingular terminal sliding mode (FxNTSM) manifold. Finally, system stability is analyzed based on Lyapunov theory. The salient features of the proposed scheme are as follows. First, a novel hyperbolic cosecant PPF is incorporated into the control scheme to enhance control performance. Then, the proposed scheme is combined with relative threshold event-triggered strategy to reduce unnecessary updates of actuators. Finally, a uniform exact differentiator is introduced to avoid the formation control law including the acceleration of adjacent vessel. Comparative results are given to demonstrate the superiority of the proposed scheme.

Command filter-based event-triggered control for stochastic MEMS gyroscopes with finite-time prescribed performance

This paper proposes an adaptive neural control strategy for stochastic microelectromechanical system (MEMS) gyroscopes, aiming to achieve a prescribed performance in a finite time. The radial basis function neural network is introduced to address the system’s unknown nonlinear dynamics and stochastic disturbances. Then, the technology of finite-time prescribed performance function, along with the method of command-filtered backstepping design, is utilized to ensure both transient and steady-state performance and simultaneously solve the problem of "explosion of complexity." Moreover, a switching threshold event-triggered control law is proposed to cut down on communication resources and eliminate corresponding parametric inequality restrictions. The proposed adaptive state feedback control strategy is able to guarantee that the output tracking error converges to a prescribed, arbitrarily small residual set. Additionally, the closed-loop system’s signals can be semi-globally ultimately uniformly bounded in probability. Finally, numerical simulations demonstrate the effectiveness and superiority of the proposed strategy.

Event-triggered robust cooperative output regulation for a class of linear multi-agent systems with an unknown exosystem

This paper investigates the robust cooperative output regulation problem for a class of heterogeneous uncertain linear multi-agent systems with an unknown exosystem via event-triggered control (ETC). By utilizing the internal model approach and the adaptive control technique, a distributed adaptive internal model is constructed for each agent. Then, based on this internal model, a fully distributed ETC strategy composed of a distributed event-triggered adaptive output feedback control law and a distributed dynamic event-triggering mechanism is proposed, in which each agent updates its control input at its own triggering time instants. It is shown that under the proposed ETC strategy, the robust cooperative output regulation problem can be solved without requiring either the global information associated with the communication topology or the bounds of the uncertain or unknown parameters in each agent and the exosystem. A numerical example is provided to illustrate the effectiveness of the proposed control strategy.

Time-varying formation for MSVs with input and prescribed performance constraints by fixed-time event-triggered sliding mode control

This work develops a new fixed-time sliding mode time-varying formation control strategy for multiple surface vessels (MSVs) to improve the control performance, ensure the navigation safety and reduce the communication burden. At first, a fixed-time adaptive radial basis function neural network (FxRBFNN) observer is designed to provide the estimations for external environmental disturbances and model parameters uncertainties. Then, observer-based fixed-time distributed time-varying formation event-triggered control law is proposed based on the novel fixed-time auxiliary dynamic system (FxADS), the proposed prescribed performance function, the designed fixed-time nonsingular terminal sliding mode (FxNTSM) manifold and the proposed uniform exact differentiator. Finally, the system stability is proved by Lyapunov theory. The salient features as follows. First, to enhance the convergence performance for time-varying formation control method, a novel hyperbolic cosecant prescribed performance function (PPF) is introduced. Second, a novel FxADS is proposed to confront input constraints. Third, the proposed scheme is combined with a relative threshold event-triggered strategy to reduce the unnecessary updates of actuators under the premise of ensuring the control performance. Fourth, to avoid the proposed formation control law including adjacent vessel accelerations, this work proposed an improved uniform exact differentiator to identify them. Numerical simulations confirm that the proposed scheme has superior control performance.

Fully Event-Triggered Practical Leader-Following Consensus of Multiple Euler-Lagrange Systems Over Switching Networks.

In this article, a fully event-triggered adaptive distributed control law is developed for solving the practical leader-following consensus problem (LFCP) of a group of uncertain Euler-Lagrange (EL) systems. An event-triggered output-based distributed observer (OBDO) is established first to estimate the state variable of the leader system over jointly connected switching networks (JCSNs). Then, the event-triggered adaptive control law is designed to exclude the Zeno phenomenon and to make the steady-state consensus error smaller than any specified value by adjusting some design parameters. Compared with the existing results, the proposed event-triggered control law only depends on the output of the leader system, works over JCSNs, and, moreover, is fully event-triggered so that it can be directly implemented in a digital platform. The overall design is illustrated using a group of two-link manipulators.

Event-Triggered Learning-Based Fault Accommodation for a Class of Nonlinear Interconnected Systems.

In this article, a distributed learning-based fault accommodation scheme is proposed for a class of nonlinear interconnected systems under event-triggered communication of control and measurement signals. Process faults occurring in the local dynamics and/or propagated from interconnected neighboring subsystems are considered. An event-triggered nominal control law is used for each subsystem before detecting any fault occurrence in its dynamics. After fault detection, the corresponding event-triggered fault accommodation law is utilized to reconfigure the nominal control law with a neural-network-based adaptive learning scheme employed to estimate an ideal fault-tolerant control function online. Under the asynchronous controller reconfiguration mechanism for each subsystem, the closed-loop stability of the interconnected systems in different operating modes with the proposed event-triggered learning-based fault accommodation scheme is rigorously analyzed with the explicit stabilization condition and state upper bound derived in terms of event-triggering parameters, and the Zeno behavior is shown to be excluded. An interconnected inverted pendulum system is used to illustrate the proposed fault accommodation scheme.

A Sampling Control Framework and Its Applications to Robust and Adaptive Control

In this paper, we propose a novel sampling control framework based on the emulation technique where the sampling error is regarded as an auxiliary input to the emulated system. Utilizing the supremum norm of sampling error, the design of periodic sampling and event-triggered control law renders the error dynamics bounded-input-bounded-state (BIBS), and when it is coupled with system dynamics, achieves global or semi-global stabilization. The proposed framework is then extended to tackle the stabilization problem for a system where the state of dynamic uncertainty is not available for feedback and the dynamics is subject to parameter uncertainties. The proposed framework is further extended to solve two classes of event-triggered adaptive control problems where the emulated closed-loop system does not admit an input-to-state stability (ISS) Lyapunov function. For the first class of systems with linear parameterized uncertainties, even-triggered global adaptive stabilization is achieved without the global Lipschitz condition on nonlinearities as often required in the literature. For the second class of systems with uncertainties whose bound is unknown, the event-triggered adaptive (dynamic) gain controller is designed for the first time. Finally, theoretical results are verified by three numerical examples.

Aperiodic dynamic event-triggered control for linear systems: A looped-functional approach

The recent literature on event-triggered control has demonstrated the potential of dynamic periodic event-triggered control. Compared to continuous-time event-triggering rules, the benefit of considering periodic event-triggered control is to avoid the Zeno phenomenon, which refers to the situation when there are an infinite number of updates in a bounded interval of time. The idea of periodic event-triggered control is to trigger the control law only at known allowable periodic sampling instants. In this paper, our objective is to relax the constraint on the periodicity of the allowable sampling instants and to adapt this framework to the dynamic event-triggered control, which has not been considered in the literature, as far as we are aware of. Following the successful efforts to assess the stability of aperiodic sampled-data control, here we propose a generic framework to emulate aperiodic dynamic event-triggered control law for linear systems, for which the allowable sampling instants are not necessarily equidistant. Such an analysis is made possible thanks to the looped-functionals framework, which gives the flexibility to consider the periodic/aperiodic static/dynamic event-triggered control in a single formulation. Finally, the efficiency of the proposed results is illustrated through the study of two academic examples.

Robust Output Regulation of Linear Uncertain Systems by Dynamic Event-Triggered Output Feedback Control.

In this article, the robust output regulation problem of the linear uncertain system is investigated by the event-triggered control approach. Recently, the same problem is addressed by an event-triggered control law where the Zeno behavior may happen when time tends to infinity. In comparison, a class of event-triggered control laws is developed to achieve output regulation exactly, and meanwhile, explicitly exclude the Zeno behavior for all time. In particular, a dynamic triggering mechanism is first developed by introducing a dynamic changing variable with specific dynamics. Then, by the internal model principle, a class of dynamic output feedback control laws is designed. Later, a rigorous proof is provided to show that the tracking error of the system converges to zero asymptotically while prohibiting the Zeno behavior for all time. Finally, we give an example to illustrate our control approach.

Event-based performance guaranteed tracking control for constrained nonlinear system via adaptive dynamic programming method

An optimal tracking control problem for a class of nonlinear systems with guaranteed performance and asymmetric input constraints is discussed in this paper. The control policy is implemented by adaptive dynamic programming (ADP) algorithm under two event-based triggering mechanisms. It is often challenging to design an optimal control law due to the system deviation caused by asymmetric input constraints. First, a prescribed performance control technique is employed to guarantee the tracking errors within predetermined boundaries. Subsequently, considering the asymmetric input constraints, a discounted non-quadratic cost function is introduced. Moreover, in order to reduce controller updates, an event-triggered control law is developed for ADP algorithm. After that, to further simplify the complexity of controller design, this work is extended to a self-triggered case for relaxing the need for continuous signal monitoring by hardware devices. By employing the Lyapunov method, the uniform ultimate boundedness of all signals is proved to be guaranteed. Finally, a simulation example on a mass–spring–damper system subject to asymmetric input constraints is provided to validate the effectiveness of the proposed control scheme.

Fuzzy course tracking control of an unmanned surface vehicle with event-triggered mechanism and input quantization

This paper considers the course tracking control of an unmanned surface vehicle with event-triggered mechanism and input quantization, aiming to reduce the influence of external interference and saving communication resources. The fuzzy logic system is used to approximate the model uncertainty and external interference. A linear analytical model is used to describe the input quantization process. The adaptive fuzzy quantization controller does not need the prior information of quantization parameters. Then an event-triggered adaptive fuzzy quantization control law is proposed. In this way, the frequency and amplitude of execution are reduced and the communication burden is relaxed. On the basis of the Lyapunov stability theory, the stability of the proposed control structure is proved, and all the signals in the closed-loop system are ultimately bounded. Finally, the effectiveness and feasibility of the proposed algorithm are verified by simulation experiments.

Periodic Event-Triggered Control for Networked Control Systems With External Disturbance and Input and Output Delays.

This article investigates the problem of periodic event-triggered output-feedback control for networked control systems in the presence of external disturbance and input and output delays. With the aid of the prediction technique, we first develop the predictor-based-extended state observer to reconstruct the system information, including the unknown state and disturbance. The periodic event-triggered output-feedback control law is then designed via the disturbance/uncertainty estimation and attenuation (DUEA) method, such that the communication times can be remarkably reduced and, at the same time, the disturbance rejection ability can be effectively enhanced. Under the predictor-based event-triggered control method, the influence of the time delays is effectively attenuated, and the effect of external disturbance is considerably attenuated due to the prediction technique and the DUEA method. By using the small-gain arguments, this article gives some sufficient stability conditions for the overall control system, and the explicit computations of sampling/updating period and time delays are presented as well. Finally, we employ a practical example and show some comparative simulation results to demonstrate the advantages of the predictor-based event-triggered control method proposed in this article.

Dynamic State Observer-Based Event-Triggered ISM Load Frequency Control of Power Systems With Disturbance Observer

This article proposes a new dynamic observer-based event-triggered integral sliding mode control incorporating disturbance observer (DO) for the load frequency control of a multiarea interconnected power system (IPS) considering load disturbances and uncertainties. To improve the system performance and reduce the cost implementation, first a dynamic observer is designed for the IPS to achieve the state estimation. Then, to reduce the chattering effect and improve the robust performance, a DO is designed to obtain the estimated of the unmatched disturbances. To further reduce the chattering effect and computational burden, based on the obtained state estimation and the estimated disturbances, two integral sliding surfaces are proposed. The periodic-time one that is proposed to ensure the robust performance from the initial-time and the time-triggered one that is proposed to obtain the event-triggered control law. Utilizing the Lyapunov theory, the ultimate boundedness stability of the controlled system is studied and sufficient conditions are derived by solving a set of offline linear matrix inequalities. During the system implementation, it is also verified that the controlled system is Zeno-free. Finally, the proposed control strategy is compared with some existing studies and the simulation results demonstrate the effectiveness and the superiority over the existing approaches.

Event-Triggered Saturation-Tolerant Control for Autonomous Underwater Vehicles With Quantitative Transient Behaviors

This paper proposes an event-triggered saturation-tolerant solution to the problem of trajectory tracking control with application to autonomous underwater vehicles (AUVs) considering quantitative transient behaviors. To prespecify the transient tracking behaviors quantitatively and avoid the possible violation of behavioral functions induced by input saturation, a saturation quantitative prescribed performance control (SQPPC) is proposed, where auxiliary subsystems are devised to monitor the input saturation and modify the behavioral boundaries, such that SQPPC can automatically adapt to the input saturation without transgressing the exact output constraints. Intermittent measurement-based uncertainty and disturbance estimator (IMUDE) is devised to reconstruct the lumped disturbances with reduced communication resource usage. Building upon the estimation results obtained from IMUDE, an event-triggered control law is designed, attaining ultimately uniformly bounded (UUB) results, where a relative threshold-based event-triggered mechanism (RTETM) is constructed to reduce the communication resource dissipation along the controller-to-actuator channel. To validate the effectiveness of the proposed control strategy in controlling an omni directional intelligent navigator, simulation results are provided and analyzed.