Adaptive Event-triggered Strategy Research Articles

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.

Adaptive protocol-based control for reaction-diffusion memristive neural networks with semi-Markov switching parameters

This study explores the asynchronous control of reaction-diffusion memristive neural networks (RDMNNs) using an innovative adaptive event-triggered protocol. The unique characteristic of RDMNNs is captured through a semi-Markov process model, wherein the probability density function of the duration time is contingent on two consecutive modes. A novel adaptive event-triggered strategy, specifically designed for the semi-Markov switching signal, is introduced to effectively reduce the network's bandwidth usage. The determination of thresholds in the adaptive triggering criterion is intricately associated with the system state residuals. Due to the mismatch between the controller and the RDMNNs, the protocol-based controller operates asynchronously. This asynchronous operation is characterized by a hidden semi-Markovian model. Utilizing stochastic Lyapunov functions that correlate with the detected and system modes, several sufficient criteria for designing an effective asynchronous controller are provided, thereby ensuring the stochastic stability of the system. Finally, the feasibility of the proposed scheme is validated through a simulated example.

Non-fragile [formula omitted] filtering for delayed discrete-time Markov jump systems: An adaptive event-triggered strategy

This paper deals with the problem of non-fragile filter for delayed discrete-time Markov jump systems. The purpose is to design a mode-dependent filter such that the result filtering error system is stochastically stable and satisfies a prescribed H∞ performance lever γ. In order to improve the data transmission efficient, an adaptive event-triggered strategy is introduced. Then, with the help of the Lyapunov stability theory and matrix inequality, the sufficient conditions for the solution of the filter design problem proposed are established. Finally, two numerical examples are presented to verification the effectiveness of the proposed method.

Sampled-based adaptive event-triggered resilient control for multiagent systems with hybrid cyber-attacks

The multiagent systems have shared broad application in many practical systems including unmanned aircraft clusters, intelligent robots, and intelligent transportation. However, many unexpected cyber-attacks may disturb or disrupt the normal communication of the agents, thus reducing the interacting efficiency of multiagent systems. Ever since the cyber-attacks have been proposed, the resilient control problem for multiagent systems has been intensively explored in light of the communication network growth. However, most of the consequences only focused on denial-of-service (DoS) attacks or deception attacks independently. Distinguished from the existing resilient control mechanisms, the current investigation represents the first attempt at designing an adaptive resilient controller for multiagent systems according to the sampled-based adaptive event-triggered manner, where denial-of-service (DoS) attacks and deception attacks are both considered. First, the hybrid cyber-attacks model and its impact on the closed-loop system are addressed. And then, an adaptive event-triggered strategy is proposed to reduce network resource consumption and ease the communication burden, where the designed adaptive law can automatically adjust the triggering threshold. Finally, the consensus state of multiagent systems is capable of achieving via a series of reasonable control rules formulated through Lyapunov functional approach despite suffering hybrid cyber-attacks. And a simulation example is given to substantiate the feasibility of the proposed method.

Dynamic event-triggered synchronisation control for complex dynamical networks with stochastic attacks and actuator faults

ABSTRACT This paper studies the problem of dynamic adaptive event-triggered synchronisation control for a class of complex dynamical networks (CDNs) with parameter uncertainty and stochastic network attacks. In order to save communication resources, reduce the driving burden and overcome the conservatism of fixed parameters, an improved event-triggered control strategy is designed. Firstly, a new synchronisation error model with uncertain of internal coupling weights between nodes and stochastic network attacks on actuator is modelled. Secondly, the Bernoulli stochastic distribution process is used to describe the probability of stochastic attacks, and the actuator failure model is adopted to depict actuator failure. By establishing an appropriate Lyapunov function, some sufficient conditions to ensure the asymptotic stability of the synchronisation error system is derived, which reduces conservatism. In addition, the cooperative design of controller and adaptive event-triggered strategy is solved by solving linear matrix inequality. Eventually, two examples are given to illustrate the effectiveness and potential of the proposed design method.

Adaptive Event-Triggered Interval Type-2 T-S Fuzzy Control for Lateral Dynamic Stabilization of AEVs With Intermittent Measurements and Actuator Failure

This article presents an adaptive event-triggered dynamic output feedback interval type-2 (IT-2) T-S fuzzy control method for autonomous electric vehicle systems. First, the IT-2 T-S fuzzy model is introduced to describe the nonlinear lateral dynamics of the vehicle systems. Second, a novel adaptive event-triggered strategy is designed to reduce the computational burden. Third, the phenomenon of intermittent measurements is depicted by the Bernoulli random distributed process, and the failure of the actuator is also included. According to the Lyapunov stability theory, an IT-2 T-S fuzzy dynamic output feedback controller (DOFC) is presented to ensure the random stability and performance of the vehicle systems. Finally, the effectiveness and real-time performance of the proposed control strategy are verified via numerical simulation and a hardware-in-the-loop (HIL) test platform.

Adaptive event-triggered control for almost sure stability for vehicle platooning under interference and stochastic attacks

This paper considers the adaptive event-triggered strategy and controller design of vehicle platooning for stochastic attacks and interferences in communication channels. Bernoulli distribution and Markovian distribution Denial of Service models are introduced in this paper. In designing the controller, Aiming at the stochastic jamming attacks, the stability criterion is presented to guide the controller for almost sure string stability, and meanwhile aiming at the special safety requirements and the reduction of system interferences, the asymmetry event-triggered strategy framework is presented to adapt the transmission environment and the different safety requirements, which is designed to balance the principal concern in different situations. Finally, an example is introduced to demonstrate the controller performances of the vehicle platooning with the Bernoulli distribution and Markovian distribution DoS models, which implies that the presented methods are effective.

Cloud-Based Event-Triggered Predictive Control for Heterogeneous NMASs Under Both DoS Attacks and Transmission Delays

A novel compensation control method for heterogeneous multiagent systems under Denial-of-Service (DoS) attacks and transmission delays is investigated in this article. This control method has all the advantages of the cloud-based computation strategy, the adaptive event-triggered strategy, and the predictive control scheme. The adaptive event-triggering mechanism can adjust the event numbers adaptively, the predictive control can reduce or eliminate the negative effects brought out by both DoS attacks and transmission delays actively, while the cloud-based computation strategy can eliminate the negative effects completely as the same as there are no DoS attacks and transmission delays. Through the interval decomposition skill and the augmented system modeling method, the compensated geschlossenes system model is established. Moreover, the joint design for the feedback gain matrices and the event-triggered parameters is implemented. In the simulation part, five VTOL aircraft are used to demonstrate the theoretical results.

Event-Based Asynchronous Output Feedback Control for Nonlinear Markov Jump Systems With Partially Unknown Transition Probabilities

This brief investigates the event-triggered output feedback control problem for nonlinear Markov jump systems (NMJSs) with unknown probabilities. The NMJSs with parameter uncertainties are characterized by using the interval type-2 (IT2) fuzzy model. An adaptive event-triggered strategy is utilized to decide when to transmit, which can reduce communication costs. The output feedback controller is designed, where the modes can be allowed different with the system, and the conditional probability matrix does not require completely known. Then, an event-based asynchronous output feedback control approach is proposed for obtaining the required result. With the aid of an example, we demonstrate the effectiveness of the event-based asynchronous output control algorithms.

Dynamic Event-Triggered Consensus of General Linear Multi-Agent Systems With Adaptive Strategy

Consensus of multi-agent systems (MASs) with general linear dynamics is investigated. In order to reduce communication frequency and avoid dependence on global information, adaptive event-triggered strategy is applied. A kind of dynamic event-triggered strategy, which is more flexible in giving event-trigger threshold, is proposed. An event-trigger condition based on continuous relative measurement is first designed. Then, the event-trigger condition is improved to only utilize event-triggered information. Sufficient criteria are derived to guarantee consensus. Moreover, it is proved that the designed event-trigger conditions can exclude Zeno behavior. Numerical simulation is performed to verify the effectiveness of the obtained results.

Event-triggered fuzzy control for vehicle nonlinear suspension systems with time delay

This article investigates the adaptive event-triggered fuzzy control problem for vehicle suspension systems with nonlinear characteristics and time delay. First, to describe the vehicle suspension system with nonlinear components, a Takagi-Sugeno fuzzy suspension model is established via local linear regression method. Second, considering that the communication bandwidth is limited, an adaptive event-triggered strategy is employed to acquire a higher efficiency in terms of saving the transmission resource. Third, based on a time-delay principle and Lyapunov stability theory, a co-design method is proposed to obtain the event generator and the fuzzy controller simultaneously. The proposed event-triggered fuzzy control method is effective to improve performances of the suspension system and guarantee the limits of suspension travel and tire deflection. Finally, compared with the existing method, the effectiveness of the proposed method is verified by simulation tests with various road disturbances.

Attitude tracking control for fractionated spacecraft with actuator failures under adaptive event-triggered strategy

In this paper, two event-triggered algorithms are investigated to reduce the consumption and occupation of system resources for attitude tracking control of spacecraft system under external disturbances, model uncertainties, actuator failures, and limited communication. The first robust controller is designed with the triggering condition based on a time-based exponential function that has a dynamically decreasing trigger threshold. To improve the first controller, the second controller with the adaptive triggering condition based on a time-based exponential function is established to facilitate the realization of a comprehensive combination of feedback compensation mechanism and event-triggered control theory. It follows from the theoretical analysis that asymptotic convergence and Zeno-free are achieved under the proposed controller. Simulation results are provided to verify the effectiveness of the developed adaptive event-triggered fault-tolerant control laws.

Event-Triggered Control for Networked Systems Under Denial of Service Attacks and Applications

In this paper, the resilient controller design and synthesis issues of networked control systems under denial-of-service (DoS) attacks are investigated via an adaptive event-triggered strategy. In a networked control system, DoS attacks have serious impacts on the security of communication, possibly causing degraded stability performance of the system. To remove threats from DoS attacks, an adaptive event-triggered communication mechanism is proposed, which also provides benefits in reducing the consumption of communication resources and relieving the pressure on network bandwidth. Since the system state information is usually not fully known, an observer-based controller is developed to stabilize the system and maintain a desired performance index despite the occurrence of stochastic attacks. Furthermore, a joint design method is proposed to obtain the controller gain, observer gain and event-triggered weight matrix. Finally, practical examples based on a four-tank system, an Internet-based three-tank system and an Internet-based test rig system are presented to illustrate the effectiveness of the proposed techniques to respond and eliminate the impact of DoS attacks.

Adaptive event-triggered fault detection for Markovian jump systems with network time-delays

In this paper, the adaptive event-triggered fault detection (FD) problem is investigated for Markovian jump systems (MJSs) with network time-delays and data package dropouts. First, a novel event-triggered communication strategy with an adaptive threshold is introduced to screen the sampled signals and reduce the data releasing frequency. Consequently, the limited communication resources and network bandwidth are saved as much as possible. Second, since communication links between the plant and filter are considered to be unreliable, the effects of intermittent package dropouts phenomenon and network time-delays are taken into account simultaneously. By using time-delay system method, the network-induced phenomena, adaptive event-triggered strategy and MJSs are unified into a networked MJSs time-delay system. Then, sufficient conditions are developed such that the resulting residual system is mean-square exponentially stable with the desired [Formula: see text] performance. Finally, a numerical example of the F-404 aircraft engine system is provided to illustrate the effectiveness and potential of the proposed design method.

Resilient Annular Finite-Time Bounded and Adaptive Event-Triggered Control for Networked Switched Systems With Deception Attacks

This paper investigates the annular finite-time bounded and adaptive event-triggered control problems of networked switched systems (SSs) with deception attacks. Initially, we consider an adaptive event-triggered strategy (AETS) for switched systems that, compared to traditional strategies, can reduce inessential transmission in a communication network. Concurrently, an event-triggered threshold is obtained from the proposed adaptive law so that the control system can dynamically maintain satisfactory system performance. Subsequently, we consider the security issue of the networked switched systems subjected to deception attacks occurring in a random way where the bounded malicious signals are introduced by adversaries. On the strength of the delay-dependent Lyapunov functional candidate and Wirtinger-based integral inequality, several criteria, which calculated in the form of linear matrix inequalities (LMIs), are obtained to ensure the annular finite-time bounded for switched systems with prescribed mixed <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> and passivity. Ultimately, the feasibility of the proposed method is verified by simulations.

Event-Triggered Sliding Mode Control of Power Systems With Communication Delay and Sensor Faults

As large-scale power systems are more and more closely integrated with remote transmission technologies, they are also affected by malicious factors in the cyber and physical layers when bringing convenience. In this article, we propose a novel adaptive event-triggered strategy and apply to the multi-area power system to deal with the load frequency control (LFC) problem with network-induced delay and stochastic sensor faults based on the discrete-time sliding mode control (DSMC) technique. Compared with existing event-triggered strategies, the proposed event-triggered strategy dynamically adjusts the threshold according to system state fluctuations, which can improve the system's tolerance for sensor faults and reduce the number of transmitted packets. Firstly, a dynamic LFC model combining network-induced delay, sensor faults, adaptive event-triggered strategy and DSMC is proposed by using the analysis method of time-delay system. Then we devise an appropriate discrete-time sliding surface for each subsystem in the networked power systems. The Lyapunov stability theory is used to analyze the asymptotic stability and robustness of each subsystem, and the decentralized controller design method is derived. Finally, some simulation examples are introduced to confirm the effectiveness of the proposed adaptive event-triggered DSMC approach.

Event-triggered adaptive neural control of fractional-order nonlinear systems with full-state constraints

In this article, we present an event-triggered scheme for fractional order nonlinear systems under full-state constraints. The neural networks are employed to approximate the continuous nonlinear unknown functions in the system. Moreover, a new adaptive event-triggered strategy is designed under the unified framework of backstepping control method, which can largely reduce the amount of communications. Since the state constraints are frequently emerged in the control procedure, the barrier Lyapunov functions is used to avoid the violation of state constraints. The stability of the closed-loop system is ensured through fractional Lyapunov stability analysis. Finally, the effectiveness of the proposed scheme is verified by simulation examples.

Adaptive Event-Triggered Fault Detection for Fuzzy Stochastic Systems With Missing Measurements

This paper discusses adaptive event-triggered fault detection filter design for fuzzy stochastic models with missing measurements. First, a novel event-triggered strategy is introduced, while an adaptive law is provided to adjust communication threshold dynamically. Compared with traditional event-triggered methods with fixed threshold, the proposed strategy is more effective on saving network communication resources. Second, a Bernoulli stochastic process is proposed to describe the measurement missing phenomenon, which always appears in real network environment. Then, an integrated fault detection model for fuzzy stochastic systems is constructed by taking network-induced delays, adaptive event-triggered strategy and missing measurements into account. A new method is provided to achieve mean-square asymptotical stability of residual model with one desired fault detection objective. Finally, simulation cases are introduced to verify the validity of the designed strategy.