Event-triggered sampled-data synchronization of complex networks with uncertain inner coupling and time-varying delays

This paper focuses on event-triggered sampled-data synchronization of uncertain complex networks with time-varying coupled delays. First of all, a discrete event-triggered sampled-data control scheme is adopted, which not only makes the state of the system be monitored in discrete time, but also the sampling information is effectively transmitted. The proposed event-triggered mechanism effectively prevents Zeno behavior. In addition, we also use some novel piecewise time-dependent Lyapunov–Krasovskii and Wirtinger inequality to handle the time-varying delays and parameter uncertainties of complex networks. Then, synchronization criteria are given for uncertain complex networks. Finally, the simulation results show that the control scheme can significantly reduce the number of transmitted signals while maintaining the uncertain complex networks synchronization.     

Decentralized adaptive synchronization of an uncertain complex delayed dynamical network

In this paper, we investigate the locally and globally adaptive synchronization problem for an uncertain complex dynamical network with time-varying coupling delays based on the decentralized control. The coupling terms here are bounded by high-order polynomials with known gains that are ubiquitous in a large class of complex dynamical networks. We generalize the usual technology of searching for an appropriate coordinates transformation to change the network dynamics into a series of decoupled lower-dimensional systems. Several adaptive synchronization criteria are derived by constructing the Lyapunov-Krasovskii functional and Barbalat lemma, and the proposed criteria are simple in form and convenient for the practical engineering design. Numerical simulations illustrated by a nearest-neighbor coupling network verify the effectiveness of the proposed synchronization scheme.     

Event-triggered synchronization for stochastic delayed neural networks: Passivity and passification case

This article investigates the event-triggered synchronization problem of stochastic neural networks under passivity and passification cases. For saving communication resources, an event-triggered approach is engaged in the design of synchronization for the delayed stochastic neural networks. To decrease network trouble, an event-triggered scheme is suggested between the sampler and communication network. A nonfragile event-triggered controller is intended to guarantee the finite-time stability of the subsequent closed-loop system. By applying the Lyapunov–Krasovkii functional (LKF) and the novel integral inequalities, a stability criteria for an interval-time varying delay error system ensure the designed controller can fulfill the necessities of passivity and passification performance. The desired control gain and event-triggered parameters are then found based on the linear matrix inequalities (LMIs). Finally, illustrative examples are given to show the benefits and validity of the desired control law.     

Fault Accommodation for a Class of Nonlinear Uncertain Systems With Event-Triggered Input

The event-triggered fault accommodation problem for a class of nonlinear uncertain systems is considered in this paper.The control signal transmission from the controller to the system is determined by an event-triggering scheme with relative and constant triggering thresholds.Considering the event-induced control input error and system fault threat,a novel eventtriggered active fault accommodation scheme is designed,which consists of an event-triggered nominal controller for the time period before detecting the occurrence of faults and an adaptive approximation based event-triggered fault accommodation scheme for handling the unknown faults after detecting the occurrence of faults.The closed-loop stability and inter-event time of the proposed fault accommodation scheme are rigorously analyzed.Special cases for the fault accommodation design under constant triggering threshold are also derived.An example is employed to illustrate the effectiveness of the proposed fault accommodation scheme.     

多链接复杂网络指数同步的间歇事件触发控制

本文研究了基于间歇事件触发控制策略的延迟多链接复杂网络的指数同步问题, 设计了一种间歇动态事 件触发控制策略. 相比于间歇静态事件触发控制策略, 本文提出的控制策略是基于控制区间内的动态事件触发, 极 大地减少了事件触发次. 并且, 考虑的间歇控制的控制增益是自适应的, 这具有动态调整的作用. 基于图论和李雅 普诺夫方法, 本文给出了实现延迟多链接复杂网络指数同步的充分条件. 此外, 本文设计的控制策略能够排除Zeno 现象. 并且, 将理论结果应用于一类振子系统的指数同步问题. 最后, 文章给出相应的数值仿真来验证理论结果的有 效性和可行性.     

Event-triggered robust distributed state estimation for sensor networks with state-dependent noises

This paper is concerned with the event-triggered distributed state estimation problem for a class of uncertain stochastic systems with state-dependent noises and randomly occurring uncertainties over sensor networks. An event-triggered communication scheme is proposed in order to determine whether the measurements on each sensor should be transmitted to the estimators or not. The norm-bounded uncertainty enters into the system in a random way. Through available output measurements from not only the individual sensor but also its neighbouring sensors, a sufficient condition is established for the desired distributed estimator to ensure that the estimation error dynamics are exponentially mean-square stable. These conditions are characterized in terms of the feasibility of a set of linear matrix inequalities, and then the explicit expression is given for the distributed estimator gains. Finally, a simulation example is provided to show the effectiveness of the proposed event-triggered distributed state estimation scheme.     

Finite-time event-triggered extended dissipative control for discrete time switched linear systems

This paper considers the problem of finite-time event-triggered extended dissipative control for a class of discrete time switched linear systems. The proposed system is modeled as a discrete time switched linear system with an event-triggered control scheme. Under the event-triggered transmission schemes, we give some sufficient conditions to guarantee the finite-time extended dissipative performance of the closed-loop switched system in terms of linear matrix inequalities. Furthermore, the state feedback controller gains are proposed by solving a set of linear matrix inequalities. Finally, a numerical example is given to show the effectiveness of the proposed methods.     

Guaranteed cost control of synchronisation for uncertain complex delayed networks

In this article, the synchronisation for a class of complex delayed dynamical networks with uncertain inner coupling configuration is investigated under the quadratic guaranteed cost control. The coupling delay and nodes delay are considered in the networks. Based on Lyapunov–Krasovskii stability theory, sufficient conditions for the existence of the optimal guaranteed cost control laws are given in terms of linear matrix inequalities. Under these sufficient conditions, the networks are globally asymptotically synchronous, and the optimal upper bound is also guaranteed. Numerical examples are given to illustrate the effectiveness of the proposed methods.     

DoS攻击下网络化控制系统基于观测器的记忆型事件触发预测控制

本文研究了DoS攻击下网络化控制系统记忆型事件触发预测补偿控制问题. 首先, 由于网络带宽资源有限 和系统状态不完全可观测性, 引入了记忆型事件触发函数, 为观测器提供离散事件触发传输方案. 然后, 分析了网络 传输通道上发生的DoS攻击. 结合上述记忆型事件触发方案, 在控制节点设计一类新颖的预测控制算法, 节省网络 带宽资源并主动补偿DoS攻击. 同时, 建立了基于观测器的记忆型事件触发预测控制的闭环系统, 并且分析稳定性. 通过线性矩阵不等式(LMI)和Lyapunov稳定性理论, 建立了控制器、观测器和记忆型事件触发矩阵的联合设计方案, 并验证了该方案的可行性. 仿真结果表明, 该方案结合记忆型事件触发机制可以有效补偿DoS攻击, 节约网络带宽 资源.     

基于单向耦合法的不确定复杂网络间有限时间同步

针对具有不确定性的复杂网络有限时间同步问题, 提出一种新颖的单向耦合控制方法. 构建含有未知参量及未知拓扑结构的驱动?响应复杂网络模型, 考虑两个网络具有不同的节点数, 同时受到时变耦合时滞的影响, 并且网络内部分别具有不同的节点系统. 基于有限时间稳定性理论和线性矩阵不等式变换, 通过在响应网络中引入单向耦合项, 实现两个网络间的有限时间同步, 同时准确辨识未知参量及未知拓扑结构. 仿真实验验证所提同步方法的有效性, 对比实验结果表明所提方法在减少耦合数量的同时具有更快的同步速率及更小的波动范围.     

Robust synchronization in an array of fuzzy delayed cellular neural networks with stochastically hybrid coupling

In this paper, the robust synchronization control problem of an array of fuzzy cellular neural networks with uncertain stochastically coupling is investigated, which involves constant coupling, discrete time-varying delay coupling and distributed time-varying delay coupling. By using adaptive feedback control scheme and exploiting some stochastic analysis techniques, several sufficient conditions are developed to ensure the synchronization of stochastically hybrid coupled fuzzy neural networks with all admissible uncertainties in mean square. Finally, a numerical example illustrated by scale-free complex networks is provided to show the effectiveness and the applicability of the proposed method.     

Synchronization for general complex dynamical networks with sampled-data

In this paper, the sampled-data synchronization control problem is investigated for a class of general complex networks with time-varying coupling delays. A rather general sector-like nonlinear function is used to describe the nonlinearities existing in the network. By using the method of converting the sampling period into a bounded time-varying delay, the addressed problem is first transformed to the problem of stability analysis for a differential equation with multiple time-varying delays. Then, by constructing a Lyapunov functional and using Jensen's inequality, a sufficient condition is derived to ensure the exponential stability of the resulting delayed differential equation. Based on that, the desired sampled-data feedback controllers are designed in terms of the solution to certain linear matrix inequalities (LMIs) that can be solved effectively by using available software. Finally, a numerical simulation example is exploited to demonstrate the effectiveness of the proposed sampled-data control scheme.     

任意相对阶下非线性切换系统的事件触发漏斗控制

针对一类具有任意相对阶且带有部分非输入到状态稳定逆动态的非线性切换系统, 提出一种动态事件触 发漏斗跟踪控制方案. 首先, 引入一个虚拟输出将任意相对阶的非线性切换系统转换为相对阶为一的非线性切换系 统. 其次, 设计各子系统的事件触发漏斗控制器和切换的动态事件触发机制, 解决候选事件触发漏斗控制器和子系 统之间的异步切换问题, 所提方案消除已有文献中为所有子系统设计共同控制器带来的保守性. 在一类具有平均驻 留时间切换信号的作用下, 保证切换闭环系统的所有信号都是有界的, 且跟踪误差一直在预设的漏斗内演化, 并排 除采样中的奇诺现象. 最后, 一个仿真例子验证方案的实用性和有效性.     

Synchronization of Complex Networks with Coupled and Self‐Feedback Delays Via Aperiodically Intermittent Strategy

In this paper, we concern the exponential synchronization problem for hybrid‐coupled delayed dynamical networks via aperiodically intermittent control. Different from previous works, the delayed coupling term considered here contains the transmission delay and self‐feedback delay, and the intermittent control can be aperiodic. By utilizing a different technique compared with some previous results, several useful criteria are derived analytically to realise exponential synchronization for a class of coupled complex network. As a special case, some sufficient conditions ensuring the exponential synchronization for a class of coupled neural network are obtained. Finally, a numerical example is given to demonstrate the validness of the proposed scheme.     

随机干扰下拓扑时变Kuramoto网络事件触发固定时间同步

本文针对一类网络拓扑时变且耦合通道存在随机干扰的Kuramoto型振子网络的同步问题, 设计了事件触 发机制下的固定时间控制算法. 针对耦合网络的拓扑在切换中保持连通的假设, 给出了一种具有多层结构的事件 触发分布式控制策略, 并能够抵消随机干扰对同步性能带来的负面影响. 利用随机Lyapunov稳定理论和固定时间稳 定性理论给出了达到实际固定时间相位同步的控制参数条件和同步调节时间的上界. 另外, 证明了所使用的事件 触发机制在引入双曲正切函数之后不会产生Zeno现象, 并给出了触发间隔的下界. 同时, 在推论中给出了本文提出 的控制方法也可以应对振子耦合拓扑图不连通情况的结论. 最后, 通过两组具有不同噪声强度的仿真实验验证了理 论分析结果.     

时变复杂动力学网络的同步控制

研究时变复杂动力学网络的同步控制问题.假定网络模型中的同步轨迹及耦合配置矩阵、内部耦合矩阵的各元素均有界,当这些界值可以预估或不确定时,分别应用线性反馈控制和自适应反馈控制策略使该网络实现全局同步.通过构造Lyapunov函数,证明了对于任意初始状态的时变复杂动力学网络模型在相应的控制手段下总可以实现渐近同步.特别是设计的自适应反馈控制策略对网络的拓扑结构、节点间的耦合强度及时滞等因素表现出较强的鲁棒性.数值仿真证实了上述结论的有效性.     

Event-triggered state estimation for Markovian jumping impulsive neural networks with interval time-varying delays

This paper investigates the event-triggered state estimation problem of Markovian jumping impulsive neural networks with interval time-varying delays. The purpose is to design a state estimator to estimate system states through available output measurements. In the neural networks, there are a set of modes, which are determined by Markov chain. A Markovian jumping time-delay impulsive neural networks model is employed to describe the event-triggered scheme and the network- related behaviour, such as transmission delay, data package dropout and disorder. The proposed event-triggered scheme is used to determine whether the sampled state information should be transmitted. The discrete delays are assumed to be time-varying and belong to a given interval, which means that the lower and upper bounds of interval time-varying delays are available. First, we design a state observer to estimate the neuron states. Second, based on a novel Lyapunov-Krasovskii functional (LKF) with triple-integral terms and using an improved inequality, several sufficient conditions are derived. The derived conditions are formulated in terms of a set of linear matrix inequalities , under which the estimation error system is globally asymptotically stable in the mean square sense. Finally, numerical examples are given to show the effectiveness and superiority of the results.     

Fixed time event-triggered control for high-order nonlinear uncertain systems with time-varying full state constraints

The fixed time event-triggered control for high-order nonlinear uncertain systems with time-varying full state constraints is investigated in this paper. First, the event-triggered control (ETC) mechanism is introduced to reduce the data transmission in the communication channel. In consideration of the physical constraints and engineering requirements, time-varying barrier Lyapunov function (BLF) is deployed to make all the system states confined in the given time-varying constraints. Then, the radial basis function neural networks (RBF NNs) are used to approximate the unknown nonlinear terms. Further, the fixed time stability strategy is deployed to make the system achieve semiglobal practical fixed time stability (SPFTS) and the convergence time is independent of the initial conditions. Finally, the proposed control scheme is verified by two simulation examples.     

Synchronization of Multi-links Memristor-Based Switching Networks Under Uniform Random Attacks

A variety of influence factors are common to the support networks which are used as cyber-physical systems. In this paper, we consider the problem of finite-time and exponential synchronization for the memristor-based switching networks (MSNs) with multi-links and multiple time-varying delays under uniform random attacks via asymptotic controller and adaptive controller. We propose a more general system model and utilize an analytical method which is different from the classical analytical techniques like set-valued mappings technique and differential inclusions to preprocess the MSNs to a class of switching networks with some uncertain parameters. Then, based on appropriate Lyaponov functionals and linear matrix inequality, several useful criteria ensuring the finite-time synchronization or asymptotic synchronization of MSNs with multi-links and time-varying delays under uniform random attacks via designed control law are obtained. Finally, two numerical examples are designed to show the feasibility and the correctness of our proposed results.     

Synchronization of Uncertain Complex Networks with Time‐Varying Node Delay and Multiple Time‐Varying Coupling Delays

This paper investigates the synchronization problem of a class of complex dynamical networks via an adaptive control method. It differs from existing works in considering intrinsic delay and multiple different time‐varying coupling delays, and uncertain couplings. A simple approach is used to linearize the uncertainties with the norm‐bounded condition. Simple but suitable adaptive controllers are designed to drive all nodes of the complex network locally and globally synchronize to a desired state. In addition, several synchronization protocols are deduced in detail by virtue of Lyapunov stability theory and a Cauchy matrix inequality. Finally, a simulation example is presented, in which the dynamics of each node are time‐varying delayed Chua chaotic systems, to demonstrate the effectiveness of the proposed adaptive method.