Event-triggered Control Problem Research Articles

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.

Fuzzy Dynamic Event-Triggered Tracking Control for Semilinear Time-Delay Parabolic Systems

In this article, the fuzzy dynamic event-triggered tracking control problem of semilinear parabolic systems (SLPSs) with time-varying delay is investigated. Firstly, T-S fuzzy partial differential equation (PDE) models are introduced to describe the SLPSs. Secondly, a less conservative and more general fuzzy dynamic event-triggered strategy (DETS) is proposed to reduce communication consumption and avoid unnecessary continuous signal monitoring. Since the dynamic threshold is closely related to the currently sampled signal and the latest successfully transmitted signal, it can be promptly dynamically adjusted. In addition, on the basis of a reasonable assumption, a novel linear matrix inequality (LMI) relax technique is introduced to deal with the mismatched premise variables between the fuzzy systems and the fuzzy controller. By constructing the appropriate Lyapunov-Krasovskii candidate functional (LKCF), the criteria that the SLPSs can asymptotically track the target systems is derived, and the desired dynamic event-triggered control (DETC) gains can be obtained by solving a set of LMIs. The DETS reduces effectively communication resource consumption. Finally, the control problem of temperature distribution of catalytic rod in practical engineering application is given to verify the effectiveness and superiority of the proposed control scheme.

Event-triggered adaptive control for stochastic nonlinear systems with time-varying full state constraints and output dead-zone

This paper studies the problem of event-triggered adaptive control for a class of strictly-feedback stochastic nonlinear systems. The output dead zone and time-varying full-state constraints are considered in the controller. Firstly, time-varying barrier Lyapunov functions (TVBLFs) are introduced to address problems of time-varying state constraints. Then, the Nussbaum-type function is used to compensate for the influence of the output dead zone. The problem of state constraint is well solved when the system’s output signal is affected by the dead zone, which satisfies a wider application space. In addition, a new relative threshold of the event-triggered control (ETC) scheme is considered to save communication resources, and the Zeno behavior is excluded. This scheme makes the trigger interval more flexible and optimizes the performance of the controller. Moreover, the error compensation signals reduce the filtered errors and simplify the algorithm. Simultaneously, an adaptive tracking controller with the backstepping technique is designed so that all signals in the closed-loop system are bounded and the tracking error converges to a small residual set of the origin. Finally, two simulation examples are given to demonstrate the effectiveness of the control method.

Adaptive event-triggered coordination control of unknown autonomous underwater vehicles under communication link faults

Coordination control of autonomous underwater vehicles finds broad applications in various marine activities. This paper focuses on the adaptive event-triggered coordination control problem of autonomous underwater vehicles under the effects of nonlinear and coupled dynamics, unknown system parameters, and communication link faults. The limited communication capacity is considered and an event-triggered distributed observer is designed under the effects of communication link faults. Reinforcement learning method is implemented to learn optimal coordination controller to achieve the position formation and the attitude synchronization for autonomous underwater vehicles with unknown dynamics. Simulation results verify the effectiveness of the proposed approach.

Dynamic event-triggered adaptive control for a class of uncertain nonlinear systems

This paper investigates the event-triggered control problem for a class of strict-feedback uncertain nonlinear systems. For such kind of systems, many backstepping-based event-triggered control methods have been proposed. However, these currently available methods all adopt static event conditions which cannot simultaneously guarantee zero tracking/stabilization error and non-existence of the Zeno behavior. In this paper, a dynamic event-triggered control scheme for strict-feedback uncertain nonlinear systems is proposed. In this scheme, an auxiliary dynamic variable is included in the event condition. By properly designing the dynamics of this auxiliary variable, the proposed dynamic event-triggered control scheme can ensure zero tracking/stabilization error in the absence of Zeno behavior. Simulation results are provided to illustrate the effectiveness of the proposed scheme and verify the established theoretical results.

Adaptive critic learning for approximate optimal event-triggered tracking control of nonlinear systems with prescribed performances

This paper studies the approximate optimal event-triggered tracking control problem for a class of second-order nonlinear systems with prescribed performances. By employing a prescribed performances function, restrictions on tracking errors are removed for prescribed performances including convergence rate, maximum overshoot, and steady-state error. On this basis, a complex optimal tracking control problem can be transformed into an optimal stabilising control problem by constructing an augmented system. Subsequently, an event-triggered mechanism is introduced to reduce communication resources. Then, an adaptive critic learning algorithm is used to solve the Hamilton-Jacobi-Bellman equation, where the weights in the critic networks are tuned through the gradient descent approach and experience replay technology. Through the developed learning method, the persistence of excitation condition is released, and the data efficiency is improved. The Lyapunov stability theory is introduced to verify the stability of the close-loop system and the tracking errors are uniformly ultimately bounded. Finally, simulation results are presented to show the effectiveness of the proposed approach.

Event-triggered H ∞ control of complex dynamical networks subject to stochastic cyber-attacks by new two-sided delay-dependent LKF functional

ABSTRACT This article deals with the problem of event-triggered control of complex dynamical networks (CDNs) subject to stochastic cyber-attacks and random coupling delays. Hackers may abuse the system by injecting malicious activity into communication networks. Two types of non-linear functions for cyber-attacks, time-varying coupling delays, respectively, and a set of random variables satisfying the Bernoulli distribution are proposed. Against complex dynamical networks, an attacker has the capacity to change the transmitted data. So the traditional control method can be a major challenge, and it is essential to respond to cyber-attacks of this kind. An event-triggered mechanism (ETM) was implemented to avoid losing limited bandwidth. The input delay method is then used to build a time-varying sample under stochastic cyber-attacks. The stability criterion of the developed system model and the display expression of control parameters are then established using a new two-sided delay-dependent Lyapunov–Krasovskii functional (LKF) and linear matrix inequality (LMI) techniques. Lastly, two numerical examples, including the inverted pendulum application, are presented to illustrate the effectiveness and feasibility of the derived theoretical results.

Observer-Based Global Event-Triggered Control for a Class of Switched Nonlinear Systems

This note studies the global event-triggered control problem for a class of switched nonlinear systems in the sense of output feedback by the average dwell-time method. Since system switching has no regularity and the controller is only updated at triggering instants, the mismatch behavior may happen between the controller and its corresponding subsystem, which may lead to a destroyed system performance. To derive a flexible triggering condition and solve this problem, two dynamic gains are introduced and a mode-dependent dynamic triggering mechanism is designed. Then, by using the weighted homogeneity and inequality techniques, a mode-dependent dynamic event-triggered controller is developed by output feedback, under which the system states can globally enter an adjusted neighborhood of the origin. Meantime, by the skillful use of the right-continuous property of the switching signal, the Zeno behavior is proved to be avoided. Finally, as the effectiveness verification, two practical examples are provided.

Event-triggered neural network control using quadratic constraints for perturbed systems

This paper investigates the event-triggered control problem for perturbed systems under neural network controllers. We propose a novel event-triggering mechanism, based on local sector conditions related to the activation functions, to reduce the computational cost associated with the neural network evaluation. It avoids redundant computations by updating only a portion of the layers instead of evaluating periodically the whole neural network. Sufficient conditions in terms of matrix inequalities are established to design the parameters of the event-triggering mechanism and compute an inner-approximation of the region of attraction for the perturbed feedback system. The theoretical conditions are obtained by using a quadratic Lyapunov function and an abstraction of the activation functions via quadratic constraints to decide whether the outputs of the layers should be transmitted through the network or not. Such conditions allow us to reduce the computational activity on the neural network while preserving the stability and performance level of the perturbed feedback system. To illustrate the efficacy of our approach, we consider the nonlinear inverted pendulum system stabilized by a trained neural network.

Event-triggered fuzzy adaptive output feedback containment fault-tolerant control for nonlinear multi-agent systems against actuator faults

In this paper, the observer-based fully distributed event-triggered adaptive fuzzy containment fault-tolerant control (FTC) problem is studied for nonlinear multi-agent systems (MASs) subject to unknown actuator faults. Fuzzy logic systems (FLSs) are utilized to model unknown agents, and a new state observer is constructed to estimate unmeasurable states. Since the global communication information of topology is not available, the local reference signals are also not available, a distributed estimator is introduced to obtain the information of the local reference signals. To deal with the unknown actuator faults, a novel actuator fault compensation mechanism with a projection operator is adopted. Then based on backstepping technique and by designing a two-channel event-triggered mechanism, a fully distributed event-triggered containment FTC approach is developed. It is testified that all parameters and variables of the controlled system are bounded, and the followers can converge to the dynamic convex hull spanned by the leaders. Finally, we apply the developed control algorithm to the multiple unmanned surface vehicle (USV) systems, and the corresponding simulation results further confirm the feasibility of the developed control algorithm.

Event-Triggered Containment Control for Nonlinear Multiagent Systems via Reinforcement Learning

Herein, we concern with the problem of event-triggered containment optimal control for a class of nonlinear multiagent discrete-time systems (MADSs). Compared to the previous containment control strategy for linear MADSs, a novel containment control strategy for nonlinear MADSs via the backstepping technique is introduced. Utilizing the classic reinforcement learning (RL) approach, we implement the n-step backstepping structure with actor-critic neural networks (NNs). In addition, instead of a time-triggered scheme, the event-triggered scheme (ETS) is employed for saving computations. Finally, some simulation results are presented to verify the performance of our control strategy.

Security-Based Control for Networked Interval Type-2 Fuzzy Systems With Multiple Cyber-Attacks: An Improved Dynamic Event-Triggered Scheme

This paper addresses the problem of dynamic event-triggered (DET) control for networked interval type-2 (IT2) fuzzy systems subject to multiple cyber-attacks, which contain deception attacks and aperiodic denial-of-service attacks simultaneously. Firstly, a multiple cyber-attack model is established for the IT2 fuzzy systems with multiple cyber-attacks and external disturbances. Secondly, an improved DET mechanism (DETM) is proposed to reduce the communication burden. Contrasting with existing works, by introducing two adjustable parameters into the novel DETM, the presented DETM has more flexibility in adjusting the data packet transmissions to conserve more limited network resources. Meanwhile, the Zeno behavior is probably excluded under the proposed DETM while preserving the system performance. Thirdly, imperfectly matched membership functions (MFs) are considered between fuzzy controller and IT2 fuzzy model, and the global boundary information of MFs and slack matrices are introduced to relax stability conditions. Besides, by constructing a proper Lyapunov-Krasovskii functional, sufficient conditions are obtained to ensure the exponentially mean-square stable of the IT2 fuzzy systems with <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$H_{\infty }$</tex-math></inline-formula> performance. Finally, two simulation examples are provided to illustrate the effectiveness of the theoretical results.

Robust Adaptive Dynamic Event-Triggered Control of Switched Nonlinear Systems

This paper investigates the robust adaptive event-triggered control (ETC) problem for switched nonlinear systems. A novel robust adaptive controller for nonlinear systems under dwell-time switching is first constructed based on a common virtual control Lyapunov function method using backstepping. Then, a dynamic ETC (DETC) strategy is integrated into the switching controller to constitute an overall solution to the problem under investigation while the challenges caused by interaction of switching and triggering instants are well addressed. In particular, global asymptotic stability and exclusion of Zeno behavior are guaranteed for the resulting closed-loop system. Furthermore, the solution is extended to a larger class of switched nonlinear systems whose subsystems are not assumed to be asymptotically stable. In this case, a state-dependent switching strategy with guaranteed dwell-time must be constructed such that the proposed switching DETC can be applied. Finally, three examples are presented to demonstrate the effectiveness of the proposed design strategies.

Collision-Risk-Based Event-Triggered Optimal Formation Control for Mobile Multiagent Systems Under Incomplete Information Conditions

This article deals with collision-risk-based event-triggered optimal formation control problems for mobile multiagent systems. First, several collision-risk-related definitions, such as collision-free margin, moving direction angle, collision risk angle, and collision risk level, are proposed for the moving agents. Then, a collision-risk dependent, time-varying, event-triggered heterogeneous communication network topology is developed, where the agent starts obtaining information of the neighboring agents only when collision risks occur among them. Third, an anti-collision control law, which is composed of a switch function, a control force direction function, and a control strength function, is designed to guarantee the collision avoidance formation of multiagents. Fourth, to ensure the formation quality and save control cost of the multiagent system, an optimal formation control scheme with feedforward compensation is designed. Simulation results illustrate that: 1) by using the collision risk information of mobile agents, the proposed control scheme is effective to realize the collision avoidance optimal formation task and 2) the anti-collision formation controller can be implemented with incomplete information of the agents.

Adaptive Dynamic Programming-Based Event-Triggered Robust Control for Multiplayer Nonzero-Sum Games With Unknown Dynamics1-4mmPlease verify and confirm the term "Multi-Player" has been changed to "Multiplayer" in the title of this article.

In this article, the event-triggered robust control of unknown multiplayer nonlinear systems with constrained inputs and uncertainties is investigated by using adaptive dynamic programming. To relax the requirement of system dynamics, a neural network-based identifier is constructed by using the system input-output data. Subsequently, by designing a nonquadratic value function, which contains the bounded functions, the system states, and the control inputs of all players, the event-triggered robust stabilization problem is converted into an event-triggered constrained optimal control problem. To obtain the approximate solution of the event-triggered Hamilton-Jacobi (HJ) equation, a critic network for each player is established with a novel weight updating law to relax the persistence of excitation condition based on the experience replay technique. Furthermore, according to the Lyapunov stability theorem, the present event-triggered robust optimal control ensures the multiplayer system to be uniformly ultimately bounded. Finally, two simulation examples are employed to show the effectiveness of the present method.

Periodic Event-Triggered Dynamic Feedback Synchronization Control of Discrete-Time Neural Networks.

This article investigates the event-triggered synchronization control problem of discrete-time neural networks (DNNs) in the case of periodic sampled-data. A discrete-time periodic event-triggered mechanism is adopted to evaluate the measurements, which avoids formulating the triggering function in a continuous manner and saves energy consumption. Under this framework, an event-triggered dynamic output-feedback controller is designed to achieve the goal of synchronization. A piecewise Lyapunov functional is constructed to analyze the sawtooth-like pattern of sampled-error signals. Thereafter, the synchronization criteria are formulated for the considered DNNs. The co-designed issue is further discussed for the control gains and triggering parameter. Finally, a simulation example is presented to show the effectiveness of the proposed method.

Distributed Dynamic Event-Triggered Secon- dary Control for Islanded Microgrids With Disturbances and Communication De- lays: A Hybrid Systems Approach

This paper investigates the secondary control problem of islanded microgrid (MG) under hybrid systems framework. Owing to network-induced imperfections caused by communication and limited communication resources, we consider the event-triggered secondary control problem under time-varying delays. A hybrid dynamic event-triggering mechanism (DETM) is proposed to reduce the usage of communication resources, the event triggering interval is enforced to be lower bounded by strictly positive value. A hybrid closed-loop model is constructed, that incorporates the communication delays and event-triggering mechanism (ETM), and it facilitates stability and performance analysis. Lyapunov-based stability conditions are proposed, while the maximum allowable delay can be determined. Moreover, the proposed hybrid DETM ensures that the minimum event-triggering interval (METI) can be designable and pre-specified, even in the presence of disturbances, which is important for control implementation. Finally, the effectiveness of the proposed control strategy is illustrated by simulation examples.

Hybrid Event-Triggered Intermittent Control for Nonlinear Multi-Agent Systems

A novel hybrid event-triggered intermittent control problem for a class of nonlinear multi-agent systems is addressed. The system dynamics are reconstructed to describe the nonlinear multi-agent systems via the intermittent control method and a hybrid event-triggered mechanism. This study represents the first attempt at designing a control time sequence determined by the hybrid event-triggered mechanism to replace the predesigned control time sequence. Moreover, a consensus tracking control strategy is presented for the reconstructed multi-agent systems, and the results are extended to the case involving multiple leaders. The control strategy can solve the problems associated with the artificial selection of the control time sequence and unnecessary control input consumption. There are two simulation examples presented to demonstrate the effectiveness and feasibility of the control strategies.

Adaptive backstepping control of high-order fully actuated nonlinear systems with event-triggered strategy

This paper investigates the problem of adaptive event-triggered fuzzy control for nonlinear high-order fully actuated systems. In this paper, a completely unknown nonlinear function is considered, and its prior knowledge is unknown. To solve this problem, the fuzzy logic system technology is applied to approximate the unknown nonlinear function. In order to save communication resources, a novel high-order event-triggered controller is proposed under backstepping control. With the help of Lyapunov stability theory, it is proved that all signals of the closed-loop system are bounded. Finally, the theoretical results are applied to the robot system to verify their validity.

Observer-based dynamic event-triggered secure control for nonlinear networked control systems with false data injection attacks

This paper considers the problem of observer-based dynamic event-triggered secure control for nonlinear networked control systems (NCSs) under false data injection attacks (FDIA). A novel output feedback controller is developed by utilizing a sampled-data observer to relieve the impacts of unmeasured states and FDIA. To make full use of the limited network resources, a dynamic periodic event-triggered mechanism (PETM) with an auxiliary variable is designed, which can reduce the transmission of information. Then, an approximation of injected false data is implemented based on an adaptive neural network (NN) in the presence of FDIA. Based on the proposed control strategy, the boundness of the nonlinear NCSs can be ensured by selecting some proper parameters. Finally, the effectiveness and validity of the proposed control strategy are illustrated via simulation of a single link robot arm system.