前馈反馈线性时变多变量控制系统性能评价

本文研究一类具有可确定“时滞”的线性时变多变量(linear time-varying multiinput-multioutput, LTV MIMO)控制系统的性能评价方法。定义了该类系统中相当于单回路“过程滞后”的矩阵因子——关联矩阵，并利用该关联矩阵和正常乘法提出一套建立线性时变多变量控制回路前馈反馈最小方差(minimum variance, MV)基准的系统化算法和过程．需要考虑到LTV系统特有的计算特性，利用实际最小方差输出而不是关联滤波最小方差输出获得MV基准。理论和仿真研究表明，所提方法能准确和有效地评价线性时变多变量过程．     

基于CARIMA模型的全局收敛的多变量随机自适应控制算法

本文针对多变量受控自回归积分滑动平均模型(CARIMA)提出了新的直接随机自适应控制算法,该算法不仅能鲁棒抑制任何定常负荷干扰而且应用于非最小相位系统仍具有全局收敛性,该算法不要求事先知道系统的关联矩阵只要求知道关联矩阵的整值参数。     

基于终端凸集约束的新MPC 控制器

针对一类离散系统,研究了带有终端约束凸集的MPC控制问题.通过离线设计一组椭圆不变集,并将其组合成一个终端约束凸集,其中凸集参数作为在线优化变量.在线运算时,根据实际的终端状态即时地选择合适的终端不变集,从而有效地扩大了系统的可行域.分别给出了设计MPC控制器的离线和在线算法,仿真实例说明了该方法的有效性.     

一种改进的Petri网S不变量计算方法

用于计算Petri网S不变量的M-S算法将所有正负行两两做线性组合变换,增加了算法的复杂度,得到的最终结果也并不一定是最小S不变量支撑。针对该问题,提出一种改进算法。通过增加对Petri网关联矩阵的预处理步骤,减少线性组合运算的次数,并得到最小S不变量支撑。理论与实验结果证明,M-S算法的复杂度为s×t,而改进算法的复杂度为s＋t,该算法能有效减少计算复杂度。     

基于GMEC转换算法的Petri网结构控制器综合方法

针对含不可控变迁Petri网系统禁止状态控制器设计问题,提出了一种基于矩阵变换和整数线性规划的结构控制器综合方法。该方法的关键是对代表系统合法状态的广义互斥约束(generalized mutual exclusion constraint, GMEC)进行转换。首先,根据Petri网系统的关联矩阵,将库所集分为无关库所集、不可控库所集和补足库所集。其次,通过对非允许GMEC中补足库所的权值和不可控库所的权值进行处理,并运用整数线性规划将非允许GMEC转换为允许GMEC。在允许GMEC的基础上,根据库所不变量原理设计出Petri网系统的结构控制器。最后,以某零件加工系统为例验证了所提方法的泛用性和高效性,为实际智能制造系统的监督控制器设计提供有效参考方案。     

几何约束求解的简化迭代算法

针对几何约束系统图分解中复合顶点的求解问题,提出复合顶点的图分解算法和等价自由变量的简化迭代求解算法.通过去除复合顶点部分边界约束对复合顶点进行图分解,对求解序列中的欠约束顶点添加等价自由变量、以等价自由变量的部分迭代求解、替代系统的整体数值求解,以提高求解效率和稳定性.该算法具有很强的通用性,并在实际应用中得到验证.     

具有下界约束的关联规则算法研究

约束关联规则是关联规则研究中的重要问题,目前的研究大多集中在单变量约束,对双变量约束的研究较少,而双变量约束在实际中也有重要作用.针对这种情况,提出了双变量约束中具有下界约束的关联规则问题.在此基础上,给出了下界约束的定义,然后分析了满足下界约束频繁集的性质,并给出了相关的证明.最后提出了基于FP-Tree的下界约束算法,采用了预先测试的方法,降低了需要测试项集的数量和计算成本.实验结果表明,该算法具有较高的效率.     

两种高效局部搜索算法求解RB模型实例

RB （revised B）模型是一种在约束可满足问题中具备精确相变增长域的随机实例模型,提出两种高效的启发式局部搜索算法用于解决RB模型生成的大值域约束可满足问题。首先为基于权重指导搜索的W-MCH算法,该算法通过约束判断和违反约束数计分来进行搜索,并引入了基于约束违反概率的权重计算公式,根据其关联的约束权重进行修正,再对变量进行迭代调整。然后提出最小化值域的MDMCH算法,该算法通过记录违反约束和逐步消除已违反约束变量的启发式策略来减少搜索空间,并在最小化后的变量域内重新校准变量赋值,进而有效提高算法的收敛速度。此外,还提出了融入模拟退火策略的WSCH和MDSCH算法,这两种算法都能根据变量的表征特点对变量域进行针对性的搜索。实验结果表明,与多种启发式算法相比,这两种算法在精度与时间效率方面均呈现明显提升,在复杂难解的实例中能够提供高效的求解效率,验证了算法的有效性和优越性。     

基于投票机制的融合聚类算法

以一趟聚类算法作为划分数据的基本算法,讨论聚类融合问题.通过重复使用一趟聚类算法划分数据,并随机选择阈值和数据输入顺序,得到不同的聚类结果,将这些聚类结果映射为模式间的关联矩阵,在关联矩阵上使用投票机制获得最终的数据划分.在真实数据集和人造数据集上检验了提出的聚类融合算法,并与相关聚类算法进行了对比,实验结果表明,文中提出的算法是有效可行的.     

从系统框图描述求传递函数的两个算法

针对线性多变量和单变量系统的控制系统计算机辅助设计，给出从复杂控制系统框图求传递函数的两个算法，对解的存在性进行了讨论，并给出计算实例。该算法适用于对复杂系统稳定性、抗摄动性能等方面的分析和计算。     

基于广义模型的复杂电力系统的故障诊断

讨论了广义大系统故障诊断的一种新方法。首先,给出了复杂电力系统不对称故障的定义,以及传统的分析方法。接着,对所给的广义大系统,构造它的一个全阶广义状态观测器。考虑广义系统的3种可能故障形式,残差由交联项部分和系统的故障两部分组成。为了排除交联项对故障的干扰,实现交联项的解耦,分两种情况讨论残差,并分别设计加权矩阵,给出故障诊断的方法。所给的故障诊断方法在实际应用中切实可行,且能提高故障诊断效率。     

用虚拟连杆构造冗余残差对多关节机械臂进行故障诊断

提出了一种多关节机械臂的故障诊断方法.基于机械臂各个关节间的转动传递关系,对多个关节构造虚拟连杆的残差,利用虚拟连杆与机械臂工作空间残差间构成的冗余关系进行故障诊断.将残差阈值的设计与机械臂末端位置的渐变相联系,改进了现有方法中判别关节故障没有利用关节间位置关系的不足.     

光伏微逆变器电流传感器和功率管开路故障诊断

当电流传感器出现性能蜕化、故障或失效时,光伏微逆变器系统的输出会受到严重影响,甚至微逆变器系统其他部件有可能被直接损坏而导致整个系统永久失效;微逆变器系统中反激式变换器功率管的开路故障会引起2个交错支路电流不平衡,导致输出电流波形畸变率变大.为此,提出一种基于状态观测器的光伏微逆变器电流传感器和功率管开路故障诊断方法.建立两路反激式变换器的数学模型;构建状态观测器以实现对两路反激式变换器原边电流的在线估计,并生成残差;将残差与阈值进行比较,实现对微逆变器系统中电流传感器与功率管的实时故障诊断.仿真结果验证该方法可行且有效.     

Sensor fault diagnosis of nonlinear processes based on structured kernel principal component analysis

A new sensor fault diagnosis method based on structured kernel principal component analysis （KPCA） is proposed for nonlinear processes. By performing KPCA on subsets of variables, a set of structured residuals, i.e., scaled powers of KPCA, can be obtained in the same way as partial PCA. The structured residuals are utilized in composing an isolation scheme for sensor fault diagnosis, according to a properly designed incidence matrix. Sensor fault sensitivity and critical sensitivity are defined, based on which an incidence matrix optimization algorithm is proposed to improve the performance of the structured KPCA. The effectiveness of the proposed method is demonstrated on the simulated continuous stirred tank reactor （CSTR） process.     

The concept of residuals for fault localization in discrete event systems

In this paper an approach for fault localization in closed-loop Discrete Event Systems is proposed. The presented diagnosis method allows fault localization using a fault-free system model to describe the expected system behavior. Via a systematic comparison of the observed and the expected behavior, a fault can be detected and a set of fault candidates is determined. Inspired by residuals known from diagnosis in continuous systems, different set operations are introduced to generate the fault candidate set. After fault detection and a first fault localization, a procedure is given to render the fault localization more precisely by an analysis of the further observed system behavior. Special emphasis is given to the use of identified models for the fault-free system behavior. The approach is explained using a laboratory manufacturing facility.     

基于MVRVM回归和RVM二叉树分类的自确认气动执行器故障诊断算法

为了解决自确认气动执行器的故障诊断问题,提出了一种基于多变量关联向量机( MVRVM)回归和关联向量机二叉树分类的气动执行器故障诊断方法,该方法利用多变量关联向量机回归建立气动执行器的正常模型,然后将实际输出与模型输出比较,产生残差作为气动执行器的非线性故障特征向量。以残差作为输入建立关联向量机二叉树多分类机,诊断气动执行器故障类型。利用DABLib生成的故障数据对所研究方法进行了验证,并与基于RVM一对一分类的故障诊断方法进行了比较,结果表明该方法是解决气动执行器故障诊断的小样本和非线性问题的一种有效方法。     

A robust fault diagnosis scheme based on signal modal estimation

A novel fault detection/diagnosis technique for linear dynamic systems is proposed. In comparison with existing schemes, the proposed method achieves fault detection/diagnosis using neither observer residuals nor parameter estimation errors; instead, it relies on the estimation of the underlying modal parameters of the system. The estimated modal parameters are compared with pre-calculated characteristic patterns of the system, which are represented as a set of root loci in terms of the physical system parameters. The modal parameter estimation is carried out using a numerically robust least-squares algorithm based on singular value decomposition. A pattern recognition technique employing linear multiprototype distance functions is used to classify the faults according to the variations of physical parameters. The proposed method has been applied to a simulated DC servo system where faults are introduced as abrupt changes in physical system parameters. It is shown that the proposed scheme is capable of diagnosing most of changes in physical system parameters.     

On‐Line Fault Diagnosis Based on B‐Spline Neural Networks Using Asymptotic Local Approach

Fault diagnosis is crucial in monitoring industrial processes. Faults con be often detected from residuals generated from the system model. For systems with known models, residuals can be readily generated. However, for systems with unknown models, neural networks can be used to model the system. For small or incipient faults, it is difficult to detect faults directly from the residuals. The asymptotic local approach, which transforms the fault diagnosis problem into one that detects statistical changes in a random variable, is proposed here. The proposed scheme is illustrated by a simulation example, and comparison with faults obtained directly from the residuals is also made.     

A new method of sensor fault diagnosis for under-measurement system based on space geometry approach

Sensor fault cannot be converted to system equation under the condition of under- measurement system. Aiming to solve this problem, we present a new method which treats sensor fault as state variable to enforce fault diagnosis. Firstly, the system model of sensor fault is constructed by putting sensor fault into the state equation. Then, the residual generator is designed using the space projection operation to solve the relevant parameter matrices. Since the proposed algorithm satisfies one-to-one correspondence of faults and residuals, it can achieve single and multiple sensors FDI. Simulation results show the effectiveness of the proposed approach.     

Fault diagnosis based on identified discrete-event models

Fault diagnosis of Discrete-Event Systems consists of detecting and isolating the occurrence of faults within a bounded number of event occurrences. Recently, a new model for discrete-event system identification with the aim of fault detection, called Deterministic Automaton with Outputs and Conditional Transitions (DAOCT), has been proposed in the literature. The model is computed from observed fault-free paths, and represents the fault-free system behavior. In order to obtain compact models, loops are introduced in the model, which implies that sequences that are not observed can be generated leading to an exceeding language. This exceeding language is associated with possible non-detectable faults, and must be reduced in order to use the model for fault detection. After detecting the fault occurrence, its isolation is carried out by analyzing residuals. In this paper, we present a fault diagnosis scheme based on the DAOCT model. We show that the proposed fault diagnosis scheme is more efficient than other approaches proposed in the literature, in the sense that the exceeding language can be drastically reduced, reducing the number of non-detectable fault occurrences, and, in some cases, reducing also the delay for fault diagnosis. A practical example, consisting of a plant simulated by using a 3D simulation software controlled by a Programmable Logic Controller, is used to illustrate the results of the paper.