存在通讯限制的网络控制系统鲁棒故障检测

对一类具有随机丢包和通讯限制的网络控制系统的鲁棒故障检测问题进行研究。考虑控制器到执行器间存在通讯限制以及传感器到控制器间存在数据丢包,并将丢包用Bernoulli随机二进制分布进行描述。在此基础上建立带有故障的离散时间模型,基于所建立的模型设计故障检测滤波器,使得残差系统随机稳定,同时滤波误差系统的H∞范数满足给定的衰减水平。所设计的故障检测滤波器不但保证了残差系统对故障的灵敏,同时对系统的外部扰动输入具有鲁棒性。数值算例验证了本文所提方法是可行的。     

基于异步采样的多回路网络控制系统的建模与控制

针对异步采样机制下的多回路网络控制系统，研究了系统的建模与控制器设计问题。首先，分析了同步采样机制引起的数据冲突等问题，提出了多传感器异步采样的传输机制。考虑固定的网络传输时延，将多回路网络控制系统建模为一类具有时延的离散线性系统；考虑同步控制要求，设计了基于一致性的控制策略，并利用Lyapunov函数法，给出了闭环系统渐近稳定的充分条件；应用锥补线性化方法（CCL），给出了状态反馈控制器设计方法。最后的数值算例验证了上述方法的有效性。     

非线性过程控制系统传感器故障检测新方法

针对电厂热力系统故障检测和定位准确性低的问题,提出了基于鲁棒输入训练网络(Robust Input-training Network, RITN)的传感器故障检测模型。采用带参数限制项的目标函数对网络进行训练,并在测试目标函数中引入影响因子,增加了模型训练精度,抑制了网络计算过程故障数据对正常值的影响,减小了残差污染,提高了模型准确性。以某300MW电厂热力系统20组测点为对象进行算例分析,通过反复的实验,结果表明,该模型能够更加准确的对非线性系统故障点进行检测和分离,并更加精确重构各变量真实值,验证了该模型用于非线性过程传感器故障检测的有效性和可靠性。     

网络控制系统H∞指数稳定容错控制器的设计

随着网络控制系统的广泛应用,容错控制问题也成为了大家研究的重点,尽管网络控制系统的容错控制方法很多,但是还有一些方法被忽略,为了更好的实现容错控制,提出一种设计,针对一类时延网络控制系统(NCS),在执行器出现故障为乘性故障情况下,对系统建立离散形式的数学模型,运用离散系统的有界实引理,设计此系统的H∞指数稳定容错控制器。最终通过LMI工具箱求出状态反馈矩阵,获得设计的容错控制器。数据示例证明了此控制器符合设计要求,并且能够实现容错控制。     

基于模糊聚类算法的工控网络控制故障检测方法研究

针对传统大型工控网络控制故障检测过程中,没有考虑故障延时特性,从而导致的故障信号检测准确率下降,检测效率降低。为此,提出一种基于模糊算法的大型工控网络控制故障检测方法。引入模糊算法,对大型工控网络控制中的故障信号延迟进行模糊化建模,通过随机时延切换设计故障观测参数和故障观测参数的残差对大型工控网络控制系统进行故障检测,克服信号延迟弊端。实验结果表明,利用本文方法进行大型工控网络控制系统故障检测,能够有效提高故障的准确率,效果令人满意。     

基于Min-Max方法的混沌系统采样同步控制研究

针对含有扰动的混沌系统, 设计采样同步控制器, 利用输入时滞法将含有采样同步控制器的混杂系统转换为具有输入时滞的连续系统. 并考虑对系统影响最坏的干扰程度, 在该种情况下, 基于线性矩阵不等式(LMI)技术和min-max鲁棒控制方法, 给出了使误差系统稳定的充分条件, 确保混沌系统在所容许的扰动下均能实现完全同步. 仿真结果说明所设计的采样同步控制方案具有很强的鲁棒性, 适合应用于保密通信系统中. 关键词： 混沌同步 采样控制 min-max方法 输入时滞     

平台间光束耦合传输与控制技术研究

介绍了平台间光路耦合传输系统的构成及光轴稳定控制的实现方法,开展了耦合校正系统和探测控制系统的设计,对校正系统进行了动态范围和模态仿真,优化设计后研制出光束耦合传输与控制系统。在对快反镜性能参数测试之后,开展了平台间光束耦合传输与控制实验,当振动台加载0 db振动谱且控制系统开环时,光轴X轴抖动10.9″@RSM,Y轴抖动102.3″@RSM,闭环时,光轴X轴抖动0.75″@RSM,Y轴抖动1.11″@RSM,通过频谱分析发现,快反镜光轴耦合系统闭环时对28 Hz以内光轴抖动具有较好地抑制作用,在系统开环残差较大的频率段2～6 Hz的抑制比为?40～?30 dB。实验结果表明,该光轴耦合控制系统对平台间光束传输过程中光束抖动具有较好地抑制和稳定效果。     

基于飞行器网络控制系统的多速率控制器设计

为了减少网络环境中的时延和数据包丢失对飞行器网络控制器系统的影响,设计了一种具有多速率的保性能控制器设计方法;多速率是指在具有多通道数据传输的系统中,各通道所需控制输入的频率不完全相同;在存在时延和丢包的情况下,利用增广技术对飞行器控制系统在整个循环周期内建立离散模型,此时为了使多速率控制方法更加有效,先基于此模型在控制器输入端构造一个预测器,根据预测器的输出为整个系统设计一个具有多控制速率的动态输出反馈控制器;然后给出并证明保性能控制器的存在条件和求解方法;最后通过某飞行器网络控制系统的数值算例验证了所提方法的有效性。     

基于Fibonacci序列准周期结构的小角度低通空间滤波器

利用常规材料构造了Fibonacci序列准周期结构,运用传输矩阵法研究了该结构的空间传输特性,并基于该结构优良的空间传输特性设计了小角度低通空间滤波器.数值模拟结果表明,该小角度空间滤波器的角域带宽可通过改变序列的结构类型和序列数来调谐,其调谐规律为:随着Fibonacci序列F(m,1)中m值的增加,对应空间滤波器的角域带宽减小;随着序列数的增大,对应角域带宽也减小.在调谐的基础上,还可通过改变构成准周期结构的介质折射率参量来精确调节其角域带宽.相比于基于超材料的小角度空间滤波器而言,基于Fibonacci序列的小角度空间滤波器制备更简单,且有望应用于新一代的高功率激光系统中.     

基于BP神经网络的传感器网络动态采样模型研究

能耗控制对于农业环境监测无线传感器网络系统具有重要意义。基于误差反向传播的多层前馈神经网络预测和阈值分析建立了一种土壤温度传感器网络动态采样模型,实现了基于土壤温度周期变化特征的采样频率实时调整方法,达到减少网络冗余数据,降低网络功耗的目的。以环境温度和空气相对湿度为BP神经网络实测输入,土壤温度为预测输出,通过判断输出是否进入阈值区间动态调整采样周期。仿真实验结果表明,对于具有周期性特点的土壤温度,BP网络模型对其预测值和实测值之间的均方根误差RMSE及绝对误差AE分别为0.83℃和0.54℃。相比于连续采样,阈值分析动态采样次数减少30%。     

Exponential networked synchronization of master-slave chaotic systems with time-varying communication topologies

The networked synchronization problem of a class of master-slave chaotic systems with time-varying communication topologies is investigated in this paper. Based on algebraic graph theory and matrix theory, a simple linear state feedback controller is designed to synchronize the master chaotic system and the slave chaotic systems with a time- varying communication topology connection. The exponential stability of the closed-loop networked synchronization error system is guaranteed by applying Lyapunov stability theory. The derived novel criteria are in the form of linear matrix inequalities (LMIs), which are easy to examine and tremendously reduce the computation burden from the feedback matrices. This paper provides an alternative networked secure communication scheme which can be extended conveniently. An illustrative example is given to demonstrate the effectiveness of the proposed networked synchronization method.     

160 km all-optical OFDM transmission system with inline chromatic dispersion compensation

An experimental demonstration of an all-optical sampling orthogonal frequency division multiplexing (AOS-OFDM) transmission system with inline chromatic dispersion (CD) compensation is carried out to test the nonlinear influence. With five subcarriers non-return-to-zero (NRZ) modulated, the total bit rate is 50 Gb/s without polarization multiplexing. The receiver end is highly simplified with direct detection using optical Fourier transform filter. After transmission in 160-km standard single-mode fiber (SSMF) link with 130-ps/nm residual CD, an optimum input optical power for the system performance is achieved.     

Impulsive synchronization of networked nonlinear dynamical systems

In this Letter, we investigate the problem of impulsive synchronization of networked multi-agent systems, where each agent can be modeled as an identical nonlinear dynamical system. Firstly, an impulsive control protocol is designed for network with fixed topology based on the local information of agents. Then sufficient conditions are given to guarantee the synchronization of the networked nonlinear dynamical system by using algebraic graph theory and impulsive control theory. Furthermore, how to select the discrete instants and impulsive constants is discussed. The case that the topologies of the networks are switching is also considered. Numerical simulations show the effectiveness of our theoretical results.     

Hybrid-triggered consensus for multi-agent systems with time-delays,uncertain switching topologies,and stochastic cyber-attacks

We propose a new approach to discuss the consensus problem of multi-agent systems with time-varying delayed control inputs, switching topologies, and stochastic cyber-attacks under hybrid-triggered mechanism.A Bernoulli variable is used to describe the hybrid-triggered scheme, which is introduced to alleviate the burden of the network.The mathematical model of the closed-loop control system is established by taking the influences of time-varying delayed control inputs,switching topologies, and stochastic cyber-attacks into account under the hybrid-triggered scheme.A theorem as the main result is given to make the system consistent based on the theory of Lyapunov stability and linear matrix inequality.Markov jumps with uncertain rates of transitions are applied to describe the switch of topologies.Finally, a simulation example demonstrates the feasibility of the theory in this paper.     

Impulsive control of nonlinear systems with time-varying delays

A whole impulsive control scheme of nonlinear systems with time-varying delays, which is an extension for impulsive control of nonlinear systems without time delay, is presented in this paper. Utilizing the Lyapunov functions and the impulsive-type comparison principles, we establish a series of different conditions under which impulsively controlled nonlinear systems with time-varying delays are asymptotically stable. Then we estimate upper bounds of impulse interval and time-varying delays for asymptotically stable control. Finally a numerical example is given to illustrate the effectiveness of the method.     

Bounded consensus tracking of second-order multi-agent systems with sampling delay under directed networks

The bounded consensus tracking problems of second-order multi-agent systems under directed networks with sam- pling delay are addressed in this paper. When the sampling delay is more than a sampling period, new protocols based on sampled-data control are proposed so that each agent can track the time-varying reference state of the virtual leader. By using the delay decomposition approach, the augmented matrix method, and the frequency domain analysis, necessary and sufficient conditions are obtained, which guarantee that the bounded consensus tracking is realized. Furthermore, some numerical simulations are presented to demonstrate the effectiveness of the theoretical results.     

基于迭代学习的离散切换系统故障估计

针对一类具有任意切换序列的离散切换系统的故障估计问题,提出了一种新的故障估计算法.该算法利用引入的虚拟故障信号构建出故障估计器,并利用残差信号通过迭代学习方法对引入的虚拟故障进行逐次修正.使虚拟故障随着迭代次数的增加逐渐逼近实际故障.利用压缩映射方法严格证明了算法在各个子区间上的收敛性,给出了算法的收敛条件.理论分析表明,所提算法能够在有限区间上精确估计出切换系统发生的不同类型故障.最后通过仿真实验进一步验证了所提算法的有效性.     

基于遗传神经网络的旋转机械故障预测方法研究

许多大型旋转机械运行工况恶劣,非平稳、非线性特征明显,以及各种突发性、偶然性因素的影响,给基于振动信号处理的状态预测和状态维护分析带来困难。神经网络以其强大的处理非线性系统的能力在故障预测中得到广泛的应用,但由于其在追求高精度训练目标时易陷入局部极值,且收敛速度慢甚至发散。针对这个问题,提出了采用遗传算法对神经网络连接权值和阈值进行优化,这样不仅发挥了神经网络广泛的映射特性也使遗传算法的全局搜索优势尽显无疑。通过组合这两种算法,在提升网络学习的准确度方面,优点尤其突出,最终提高对旋转机械故障预测和寿命估计的性能,这在某环境模拟试验系统动力风机的轴承磨损故障预测中得到了验证。