Adaptive Event-triggered Scheme Research Articles

Consensus for nonlinear multi-agent systems with actuator saturation by adaptive event-triggered scheme

This paper studies the leader–follower consensus problem for nonlinear multi-agent systems with actuator saturation by adaptive event-triggered scheme. This adaptive mechanism allows parameters in triggered mechanism to change automatically compared to fixed ones in traditional strategies. The paper introduces two distinct approaches to address the issue of saturation. The first approach is sector bounded condition, while the second relies on convex hull representation. Compared with the former one, the latter one can reduce the conservatism of controller design effectively. Furthermore, it is assumed that the nonlinear function adheres to incremental quadratic limitations, thereby offering a more comprehensive depiction of its nonlinear properties. Finally, the validity of the proposed approaches is demonstrated by one numerical example in the background of mechanical systems.

Secure adaptive event-triggered anti-synchronization for BAM neural networks with energy-limited DoS attacks

This article focuses on the problem of adaptive event-triggered anti-synchronization control for bidirectional associative memory neural networks subject to energy-limited denial of service attacks. First, a novel adaptive event-triggered scheme is developed by resorting to the acknowledgment character technique, which can help conserve valuable communication resources and has better performance in resisting malicious cyber attacks compared to traditional schemes. Second, a more general attack strategy for denial of service attacks is proposed with the consideration of energy constraints, and an anti-synchronization error system is established to analyze the anti-synchronization behavior. Then, sufficient conditions are provided to guarantee the anti-synchronization of drive and response bidirectional associative memory neural networks in H∞ sense. Next, a design approach is obtained based on the above conditions for the controller gains. Finally, a numerical example is employed to demonstrate the effectiveness of the proposed method and its superiority over the traditional event-triggered scheme.

Event-triggered [formula omitted] PI state estimation for delayed switched neural networks

On state estimation problems of switched neural networks, most existing results with an event-triggered scheme (ETS) not only ignore the estimator information, but also just employ a fixed triggering threshold, and the estimation error cannot be guaranteed to converge to zero. In addition, the state estimator of non-switched neural networks with integral and exponentially convergent terms cannot be used to improve the estimation performance of switched neural networks due to the difficulties caused by the nonsmoothness of the considered Lyapunov function at the switching instants. In this paper, we aim at overcoming such difficulties and filling in the gaps, by proposing a novel adaptive ETS (AETS) to design an event-based H∞ switched proportional–integral (PI) state estimator. A triggering-dependent exponential convergence term and an integral term are introduced into the switched PI state estimator. The relationship among the average dwell time, the AETS and the PI state estimator are established by the triggering-dependent exponential convergence term such that estimation error asymptotically converges to zero with H∞ performance level. It is shown that the convergence rate of the resultant error system can be adaptively adjusted according to triggering signals. Finally, the validity of the proposed theoretical results is verified through two illustrative examples.

Adaptive event‐triggered efficient output feedback model predictive control for networked interval type‐2 T–S fuzzy system with data loss

AbstractThis article is concerned with the adaptive event‐triggered (AET) efficient output feedback model predictive control (EOFMPC) for a set of nonlinear networked interval type‐2 Takagi–Sugeno (IT2 T–S) fuzzy control systems with data loss. For the reduction in data transmission burden and required control performance, the AET scheme and a novel dynamic triggering law are introduced jointly in this article. The efficient model predictive control approach as in Kouvaritakis et al. (IEEE Trans Automat Contr. 2000;45(8):1545–1549.), which transfers the main part of online optimization work to offline stage by incorporating an extra perturbation into the fixed feedback law, is extended to networked control systems to reduce their online computational burden. This article proposes the following: (1) an unified closed‐loop model representation of data loss process and IT2 T–S fuzzy nonlinearities is established under the framework of EOFMPC, which explicitly considers the AET condition; (2) a new design method for the state observer gain is presented based on an ellipsoidal invariant set, so that a smaller bound of estimation error can be obtained through an extra optimization problem; (3) some optimization problems are formulated to design the proposed controllers, and the corresponding EOFMPC algorithm is summarized; (4) by means of the extra matrix partition technique, the online optimization problem subject to one ellipsoidal constraint is transformed into a novel convex form to solve the uncertainty of the estimation error. A simulation example and a few comparative simulation experiments illustrate the effectiveness and availability of the aforementioned approach.

Adaptive event-triggered based fault detection filtering for unmanned surface vehicles under DoS attacks

This paper proposes an adaptive event-triggered fault detection filter to warn of faults happening to unmanned surface vehicles in a network. First, the unmanned surface vehicle is modeled as a Markovian jump system which makes allowances for the influences of waves or other disturbances. Then, a fault detection filter is developed to produce crucial residual signals, despite disturbances and denial-of-service attacks. Inputs of the designed filter originate from a resilient adaptive event-triggered mechanism, of which the threshold can be adjusted for saving valuable network resources and mitigating adverse influences of denial-of-service attacks. By using the residual signals, detection evaluation functions are established and a detection logic algorithm is devised. Under this framework, the co-design of both the fault detection filter and the adaptive event-triggered scheme is given. Finally, the proposed method is proved to be applicable by simulating a real unmanned surface vehicle model.

Byzantine Agents’ Detection in Distributed Nash Equilibrium Seeking Algorithms Using an Adaptive Event-Triggered Scheme

In this article, a resilient, adaptive event-triggered distributed Nash equilibrium seeking algorithm is investigated for a class of multimicrogrid systems. Since the strategy of each microgrid affects others’ costs through total generation cost, such interaction among microgrids is modeled as a noncooperative game. To reduce unnecessary signal transmission among the aggregators, an adaptive algorithm, where threshold parameters are adjusted based on the behavior of the estimated signals in the last two iterations, is proposed. It is observed that when false data is injected into an agent, its estimated signals begin to diverge from the equilibrium point, and as a result, the threshold value becomes increasing. Furthermore, the misbehaving agents are identified and isolated using a reputation-based mechanism and are allowed to rejoin the network when they behave normally again. To deal with arbitrary behavior of users, receding horizon scheme of predictive control is used. Finally, a set of simulation results is presented to evaluate the effectiveness of the suggested algorithm.

Generalized efficient robust predictive control for networked interval type-2 T–S fuzzy system with adaptive event-triggered scheme

This article studies the generalized efficient robust predictive control (GERPC) for the nonlinear systems denoted by interval type-2 (IT2) Takagi–Sugeno (T–S) fuzzy model over networks with consideration of packet dropout, bounded disturbance and adaptive event-triggered scheme (AETS). First, the packet dropout is described by a Bernoulli process, and an AETS with thresholds that can be self-regulated according to different situations is employed to decide whether to release data into the communication network, so as to reduce the data transmission frequency and the network burden; Second, taking into consideration of packet dropout and AETS, a unified IT2 T–S fuzzy control model based on GERPC is established, and the stability of closed-loop system with bounded disturbance is guaranteed by quadratic boundedness (QB) technique; Third, generalized efficient robust predictive controller is designed to obtain a larger initial feasible region, smaller online computation and acceptable control performance. The unconstrained control law is designed offline, and a new variable will be optimized online to achieve good control performance, which is introduced to adjust the control quantity. Then, the convex LMI reformulation method is utilized to handle non-convex terms in optimization problem, and the effectiveness of the GERPC algorithm is proved by the matrix partition technique. At last, the effectiveness of the designed controller based on the GERPC strategy is verified by a given simulation experiment.

Adaptive event‐triggered dynamic output feedback control for networked control systems under hybrid attacks

AbstractThis paper concentrates on network congestion and security issue for networked control systems under the hybrid attacks. The hybrid attacks including deception attack, replay attack and denial‐of‐service attack are modelled as Bernoulli random process. A general form adaptive event‐triggered scheme (AETS) under the hybrid attacks is designed to alleviate the network congestion and save communication resources utilizing adaptive threshold and the weighted average of data packets. Meanwhile, the security issue under the hybrid attacks is addressed by a dynamic output feedback controller (DOFC) based on the AETS. Moreover, the sufficient conditions are obtained by a piecewise Lyapunov function to guarantee that the closed‐loop system is exponentially mean‐square stable. A practical experiment on networked motor control system verifies the effectiveness of the proposed scheme. The proposed scheme can not only save communication resources to further alleviate network congestion, but also defense the hybrid attacks in the network.

Observer-Based Event-Triggered Control for Interval Type-2 Fuzzy Networked System With Network Attacks

This paper is focused on the adaptive event-based interval type-2 (IT-2) fuzzy security control problem for networked control systems with multiple network attacks. In order to save the network resources, this paper proposes an improved adaptive event-triggered model, which can adjust the threshold dynamically according to the change of current signal and previous triggering signal. In addition, due to the change of external environment, the impact of randomly occurring output bias is considered in the systems. Taking the effects of multiple network attacks and adaptive event-triggered scheme into consideration, a class of augmented error fuzzy system model is established. Next, some sufficient conditions are obtained for the stochastic stability with an <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 index via the Lyapunov stability analysis method. The corresponding paraments of the IT-2 fuzzy controllers and observers are also derived by some linear matrix inequalities (LMIs). Finally, two simulation examples are given to support the advantages of the proposed method.

Adaptive event-triggered finite-time sliding mode control for singular T–S fuzzy Markov jump systems with asynchronous modes

This paper investigates the finite-time asynchronous control issue for singular fuzzy Markov jump systems (SFMJSs) with mode-dependent derivative-term coefficient through the sliding mode control (SMC) approach. Firstly, a new adaptive event-triggered scheme (ETS), which satisfies the system conditions by changing the threshold, is adopted to further reduce the communication consumption. Meanwhile, a mode-dependent fuzzy sliding surface (SS) function on account of hidden Markov model is designed. Based on a state observer, an asynchronous SMC law is proposed in accordance with the adaptive ETS to ensure that SFMJSs are finite-time boundedness (FTB). Finally, two examples are provided to certify the availability and practicability of the presented results.

Adaptive event-triggered synchronization of neural networks under stochastic cyber-attacks with application to Chua’s circuit

This paper focuses on the synchronization control problem for neural networks (NNs) subject to stochastic cyber-attacks. Firstly, an adaptive event-triggered scheme (AETS) is adopted to improve the utilization rate of network resources, and an output feedback controller is constructed for improving the performance of the system subject to the conventional deception attack and accumulated dynamic cyber-attack. Secondly, the synchronization problem of master–slave NNs is transformed into the stability analysis problem of the synchronization error system. Thirdly, by constructing a customized Lyapunov–Krasovskii functional (LKF), the adaptive event-triggered output feedback controller is designed to ensure the synchronization error system is asymptotically stable with a given H∞ performance index. Lastly, in the simulation part, two examples, including Chua’s circuit, illustrate the feasibility and universality of the related technologies in this paper.

Adaptive Event-Triggered Asynchronous Fault Detection for Nonlinear Markov Jump Systems With Its Application: A Zonotopic Residual Evaluation Approach

In this paper, the event-triggered asynchronous fault detection (FD) problem is investigated for a class of nonlinear Markov jump systems (NMJSs) via zonotopic residual evaluation (ZRE). An adaptive event-triggered scheme (AETS) is utilized to reduce the redundant data releasing frequency such that both communication resources and network bandwidth can be saved as much as possible. The hidden Markov model (HMM) with generally hybrid probabilities is introduced into the FD framework to fully characterize the asynchronization phenomenon between the plant and the FD filter as well as the event generator and to address both the general transition and mode detection information issues simultaneously. By applying the double variables-based decoupling principle and variable substitution principle, a co-design criterion of optimal <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$l_{1}/H_{\infty }$</tex-math></inline-formula> asynchronous reduced-order FD filter and AETS is derived fully considering accessible mode information, such that the generated residual signal is sensitive to system faults while being robust to amplitude-bounded exogenous disturbances and measurement noise. Furthermore, a novel ZRE strategy with dynamic thresholds is developed for FD in NMJSs. Finally, an application of automotive electronic throttle body is used to illustrate the effectiveness of the proposed approach.

Mixed H[formula omitted] and passivity finite-time state estimation for neural networks under hybrid cyber-attacks

This paper focuses on finite-time state estimation for neural networks under hybrid cyber-attacks based on adaptive event-triggered scheme (AETS). Firstly, Markov jump process is used to describe different types of cyber-attacks. In order to save communication resources, the system output state model under mixed cyber-attacks is established based on AETS. At the same time, a state estimator based on the output signal is designed to avoid the state unavailability of the system. By constructing a Lyapunov-Krasovskii functional and affine Bessel-Legendre inequality method, sufficient conditions are obtained to guarantee the estimation error system is finite-time boundedness and mixed passive and H∞ performance level. The expected gain of the state estimator is obtained by solving a set of feasible linear matrix inequalities. Finally, the robustness and effectiveness of the proposed method are verified by three numerical examples such as Chua’s circuit.

Adaptive event‐triggered pinning synchronization control for complex networks with random saturation subject to hybrid cyber‐attacks

SummaryThis research is concerned with the problem of pinning based output synchronization control for a complex networks with random saturation vulnerable to hybrid cyber‐attacks via an adaptive event‐triggered scheme (AETS). The output synchronization error systems are subject to suffer from deception attacks, replay attacks, and denial‐of‐service attacks. A novel hybrid cyber‐attack model is first constructed to integrate the three kinds of attacks into a synchronization of complex network. AETSs based on output synchronization errors with the consideration of hybrid cyber‐attacks, have been proposed to reduce the burden of communication. A pinning control strategy is used to decrease the control signal's input. By constructing a Lyapunov functional and using the linear matrix inequality technique, sufficient conditions are provided to ensure the output synchronization error system. Finally, a numerical simulation results are developed to illustrate the efficacy of the proposed theoretical methodology.

Fuzzy-Model-Based Asynchronous Fault Detection for Markov Jump Systems With Partially Unknown Transition Probabilities: An Adaptive Event-Triggered Approach

This article addresses the event-triggered asynchronous fault detection (FD) problem of fuzzy-model-based nonlinear Markov jump systems (MJSs) with partially unknown transition probabilities. For this objective, the nonlinear plant is modeled as an interval type-2 (IT2) fuzzy MJS with the aid of the IT2 fuzzy sets capturing the uncertainties of the membership functions. An adaptive event-triggered scheme is introduced to bring down the costs of the communication network from the system to the fuzzy fault detection filter (FDF), in which the triggering parameter can be adaptively tuned with the system dynamics. A hidden Markov model (HMM) is employed to characterize the asynchronous phenomenon between the system and the FDF. Unlike the existing results, the transition probabilities of the plant and the FDF are allowed to be partially known. By using the Lyapunov and the membership-function-dependent methods, the existence conditions of the FDF are derived. Finally, the proposed FD methods are verified by a numerical simulation.

Design of delay-dependent state estimation algorithm for nonlinear coupling complex networks with dynamical bias: An adaptive event-triggered scheme

This paper is concerned with the delay-dependent variance-constrained state estimation (DDVCSE) problem for nonlinear coupling complex networks (NCCNs) subject to dynamical bias and adaptive event-triggered mechanism (AETM). First of all, the nonlinear coupling is modeled to embody the form of data exchange between different network units, which is linearized by resorting to the Taylor expansion. Then, an augmented technique is adopted to deal with the random bias described by a dynamical equation. Furthermore, an AETM is arranged in the sensor-to-estimator channel to adjust communication rate thereby averting resource waste. A novel augmented delay-dependent state estimator is constructed such that the upper bound of state estimation error covariance (UBSEEC) can be derived in the presence of state delay, nonlinear coupling, dynamical bias and AETM. Moreover, the estimator gain is designed appropriately in the sense of locally minimized variance-constrained index. Besides, the algorithm performance issue is discussed with rigourous mathematical proof, where the monotonicity of the trace of UBSEEC with respect to coupling strength is clarified in detail. Finally, an illustrative example is carried out to demonstrate the validity of the developed DDVCSE strategy.

Adaptive Event-Triggered [formula omitted] Control of Semi-Markov Jump Distributed Parameter Systems

This paper focuses on the problem of adaptive event-triggered L2−L∞ control for distributed parameter systems with semi-Markov jump parameters. An event-triggered transmission scheme, whose threshold function can be adaptively adjusted, is established to decrease the frequency of data transmission. Simultaneously, the utilization of limited network bandwidth resources is also improved due to plenty of “unnecessary” packets being discarded before accessing networks. Furthermore, the jumping of stochastic parameters in networks can be described by a semi-Markov jump model, in which the sojourn-time is considered subject to the Weibull distribution instead of the exponential distribution. Based on the aforesaid discussions, a mode-dependent controller is designed under an adaptive event-triggered scheme. Whereafter, some criteria are constructed based on the Lyapunov stability theory to guarantee stochastic stability and a prescribed L2−L∞ performance of the system. Ultimately, two simulation examples are provided to verify the feasibility of the proposed design approach.

Adaptive Event-Triggered Finite-Frequency Fault Detection With Zonotopic Threshold Analysis for LPV Systems.

This article investigates a class of multiobjective optimization fault detection observer design problems for linear parameter varying (LPV) systems considering the unknown but bounded disturbance with an adaptive event-triggered scheme. In this study, the actuator faults are considered in the low-frequency domain. First, to save the communication bandwidth and improve communication efficiency, an adaptively adjusted event-triggered (AAET) mechanism is proposed. Then, in order to make the designed observer gain satisfy both fault sensitivity and disturbance robust conditions, an H-/L∞ multiobjective optimization problem is proposed and solved by appropriate linear matrix inequalities. Next, the upper and lower bounds of the generated residual are calculated by the zonotope method when considering the estimation uncertainty. Fault detection can be achieved by judging whether the zero value belongs to the generated range of the residual signal. Finally, a simulation case is used to verify the effectiveness of the proposed method.

Adaptive Event-Triggered Finite-Time Dissipative Filtering for Interval Type-2 Fuzzy Markov Jump Systems With Asynchronous Modes.

This article investigates the adaptive event-triggered finite-time dissipative filtering problems for the interval type-2 (IT2) Takagi-Sugeno (T-S) fuzzy Markov jump systems (MJSs) with asynchronous modes. By designing a generalized performance index, the H∞ , L2-L∞ , and dissipative fuzzy filtering problems with network transmission delay are addressed. The adaptive event-triggered scheme (ETS) is proposed to guarantee that the IT2 T-S fuzzy MJSs are finite-time boundedness (FTB) and, thus, lower the energy consumption of communication while ensuring the performance of the system with extended dissipativity. Different from the conventional triggering mechanism, in this article, the parameters of the triggering function are based on an adaptive law, which is obtained online rather than as a predefined constant. Besides, the asynchronous phenomenon between the plant and the filter is considered, which is described by a hidden Markov model (HMM). Finally, two examples are presented to show the availability of the proposed algorithms.

Adaptive Event-Triggered Decentralized Dynamic Output Feedback Control for Load Frequency Regulation of Power Systems With Communication Delays

In order to ensure that the power system frequency and tie-line power remain at the nominal value when the load fluctuates, while reducing the release number of decentralized sensor, this work presents a novel adaptive event-triggered scheme for the load frequency regulation via designing the decentralized dynamic output feedback controller (DOFC), where the communication delay issue is also considered due to communication constraints. Distinct from the existing ones, the proposed adaptive event-triggered strategy automatically tunes the threshold according to the local extremum of the system output signal, which can significantly reduce the number of unnecessary signal transmissions to ensure system performance. First, the proposed adaptive event-triggered transmission scheme is integrated with the decentralized DOFC and communication delays under the framework of a linear time-delay system. Then, the asymptotic stability of the closed-loop power system is analyzed through the Lyapunov stability theory and a procedure is given for the design of decentralized dynamic output load frequency controllers by solving some linear matrix inequalities (LMIs). Finally, a three-area power system is used to verify the effectiveness and usefulness of the proposed results.