基于PLS交叉积矩阵非相似度分析的MPC性能监控与诊断

针对传统基于输出协方差矩阵的性能监控方法未充分考虑过程变量与输出变量之间的相关性问题,提出一种基于偏最小二乘（Partial least squares,PLS）交叉积矩阵非相似度分析的性能监控与诊断方法,用于多变量模型预测控制（Model predictive control,MPC）系统.首先,考虑模型预测控制系统的控制结构,构造包含预测误差的增广过程变量与输出变量相关性的PLS交叉积矩阵,通过非相似度分析方法将交叉积矩阵的非相似度比较转化为转换矩阵特征值的比较.然后提取转换矩阵中表征最大非相似度的l个特征值构造实时性能指标,对MPC系统进行性能监控.检测到性能下降后,进一步利用转换矩阵的特征值诊断性能恶化源.Wood-Berry二元精馏塔上的仿真结果表明,所提方法能够有效地提高监控性能,并准确地定位性能恶化源.     

基于协方差基准的模型预测控制性能评价与监视

为了对预测控制(MPC)性能进行评价和监视,本文运用基于数据协方差基准的摔制性能评价与监视方法,通过对所监视时段数据和基准时段数据进行广义特征值分析,得到相应的性能优/劣的特征向量.进一步利用统计推断方法得出特征值在相应特征方向上的置信区间,并得到优/劣子空间下的性能指标,从而用来评价和临视MPC件能.将该方法成功应用于Wood-Berry塔这一个典型的化工对象,研究了三种模型失配下的性能评价与监视,仿真结果表明了该方案的有效性和可行性.     

一种基于改进设计性能指标的模型预测控制器性能监控方法

受随机噪声的影响,传统的设计性能指标是一个自相关的随机变量,传统的统计过程控制方法无法对其进行有效的监控。针对此问题,本文提出一种基于改进设计性能指标的模型预测控制器性能监控方法。为了对控制系统一段时间内的整体性能做出有效评价,首先将性能指标转化为平均形式,建立该性能指标的时间序列模型并对其进行预测,对预测误差进行统计过程控制,包括对预测残差及其累积和的监控,实现了对控制系统总体性能的实时评价。当检测出控制系统性能出现下降时,通过基于距离的判别分析方法快速定位性能下降源。在Wood-berry塔上的仿真研究验证了方法的可行性与有效性。     

基于分布对象的网格性能监控和预测系统

采用基于Java的ProActive并行分布对象工具和开发环境，设计了基于分布对象的网格性能监控和预测系统，其中的部分已鲐实现，初步实验表明，该系统能简便地实现系统组件在网格网络环境下自动进行部署和收集性能数据，表明了系统框架的可行性和可用性。     

基于历史性能基准的模型预测控制性能监控

针对实际工业过程中希望只利用过程的日常运行数据对控制系统进行监控的情况,提出了1种用历史性能基准对预测控制系统进行性能评估、监控以及分析的方法.利用运行状态良好的1段历史数据计算出历史性能基准,并将历史性能基准与实际性能的比值用于控制器性能的实时监控.根据历史性能指标的残差监控检测出性能变化时,进一步通过历史数据协方差监控及广义特征值分析,区分出性能显著变差或变好的方向和子空间.为现场工程师提供性能变化的一些原因,用于系统维护.最后通过Shell重油分馏塔仿真证明该方法可以获得很好的评估与监控结果.     

基于历史目标函数基准的数据驱动子空间预测控制系统的性能监控

采用历史目标函数基准对数据驱动的子空间预测控制系统进行性能监控, 提出了一种新的历史数据集的选取算法, 该算法克服了以往历史数据集只能依赖经验选取的不足, 不仅可以由用户根据实际控制需求定义性能指标, 而且提高了监控的灵敏度与准确度. 通过Wood-Berry精馏塔的仿真验证了所提算法的有效性.     

基于多特征协方差矩阵的图像匹配算法

针对灰度及红外图像的匹配过程中经常出现的一些问题,如缺乏丰富的目标特征、易遭受复杂背景及噪声等外界因素干扰、目标出现放大缩小或偏转等,抽取目标图像的梯度幅值与方向,腐蚀与膨胀以及信息熵等特征,通过协方差矩阵将其融合在一起,构成新的特征模型.通过全图遍历求取矩阵间相似度距离的方法找到最佳匹配重心,将新方法与其它3种已有的匹配方法进行了对比说明.实验结果表明:在灰度图像匹配时新方法准确率高、鲁棒性好,同时也可以应用于红外图像中,满足了在一些条件下提高匹配准确度的要求.     

基于Pushlet的网络性能实时监控系统

Pushlet是一种基于Servlet的技术,用来将服务器端的动态对象主动推送到客户端浏览器.网络性能实时监控是电信管理网的一个重要组成部分,它负责对网络中的一些动态资源进行实时监控.描述一个用Pushlet来实现网络性能实时监控的系统,该系统具有高效性、易扩展性和平台无关性.     

基于模型残差基准的模型预测控制性能监控

针对控制系统性能评估中的模型失配问题,提出了一种新的闭环模型评估方法.该方法首先分析了控制系统中扰动是具有反馈不变性的,其次推导了可以用常规闭环回路数据估计扰动序列,并利用扰动序列的反馈不变性原则定义了模型质量指标,建立该指标的时间序列模型对其进行预测,对预测残差进行统计过程控制,实现了对控制系统性能的实时评价.当检测出控制系统性能出现下降时,通过基于距离的判别分析方法快速定位性能下降源.最后通过Wood-Berry二元精馏塔上的仿真研究验证了所提方法的有效性.     

Prediction Error Based Performance Monitoring,Degradation Diagnosis and Remedies in Offset‐Free MPC: Theory and Applications

We present in this paper a control performance monitoring method for linear offset‐free model predictive control (MPC) algorithms, in which the prediction error sequence is used to detect whether the internal model and the observer are correct or not. When the prediction error is a white noise signal, revealed by the Ljung‐Box test, optimal performance is detected. Otherwise, we use a closed‐loop subspace identification approach to reveal the order of a minimal realization of the system from the deterministic input to the prediction error. When such order is zero, we prove that the model is correct and the source of suboptimal performance is an incorrect observer. In such cases, we suggest an optimization method to recalculate the correct augmented state estimator. If, instead, such order is greater than zero we prove that the model is incorrect, and re‐identification is suggested. A variant for (large‐scale) block‐structured systems is presented, in which diagnosis and corrections are performed separately in each block. Two examples of different complexity are presented to highlight effectiveness and scalability of the method.     

基于2维性能参考模型的2维模型预测迭代学习控制策略

将迭代学习控制(Iterative learning control, ILC)系统看作一类具有2维动态特性的控制系统,根据模型预测控制(Model predictive control, MPC)和性能参考模型控制思想, 提出了一种基于2维性能参考模型的2维模型预测迭代学习控制系统设计方案.在该控制系统设计方案中,可以通过选择适当的2 维性能参考模型来构造2 维动态变化的设定值信号和预测控制信号,从而引导迭代学习控制系统收敛到合理的控制性能,并有效避 免系统性能收敛过程中控制输入可能发生的剧烈波动.通过对控制系统的结构分析可知,所得的迭代学习控制器本质上是由沿时 间指标的参考模型预测控制器和沿周期指标的迭代学习控制器组成,闭环系统的收敛性等价于一个2维滤波系统的稳定性.数值仿 真结果证明了该设计方案的有效性和鲁棒性.     

Distributed stochastic MPC for systems with parameter uncertainty and disturbances

A distributed stochastic model predictive control algorithm is proposed for multiple linear subsystems with both parameter uncertainty and stochastic disturbances, which are coupled via probabilistic constraints. To handle the probabilistic constraints, the system dynamics is first decomposed into a nominal part and an uncertain part. The uncertain part is further divided into 2 parts: the first one is constrained to lie in probabilistic tubes that are calculated offline through the use of the probabilistic information on disturbances, whereas the second one is constrained to lie in polytopic tubes whose volumes are optimized online and whose facets' orientations are determined offline. By permitting a single subsystem to optimize at each time step, the probabilistic constraints are then reduced into a set of linear deterministic constraints, and the online optimization problem is transformed into a convex optimization problem that can be performed efficiently. Furthermore, compared to a centralized control scheme, the distributed stochastic model predictive control algorithm only requires message transmissions when a subsystem is optimized, thereby offering greater flexibility in communication. By designing a tailored invariant terminal set for each subsystem, the proposed algorithm can achieve recursive feasibility, which, in turn, ensures closed‐loop stability of the entire system. A numerical example is given to illustrate the efficacy of the algorithm.     

Event-triggered MPC Design for Distributed Systems With Network Communications

This paper deals with the communication problem in the distributed system, considering the limited battery power in the wireless network and redundant transmission among nodes. We design an event-triggered model predictive control (ET-MPC) strategy to reduce the unnecessary communication while promising the system performance. On one hand, for a linear discrete time-invariant system, a triggering condition is derived based on the Lyapunov stability. Here, in order to further reduce the communication rate, we enforce a triggering condition only when the Lyapunov function will exceed its value at the last triggered time, but an average decrease is guaranteed. On the other hand, the feasibility is ensured by minimizing and optimizing the terminal constrained set between the maximal control invariant set and the target terminal set. Finally, we provide a simulation to verify the theoretical results. It's shown that the proposed strategy achieves a good trade-off between the closed-loop system performance and communication rate.      

Data‐Driven Performance Monitoring for Model Predictive Control Using a mahalanobis distance based overall index

This paper proposes a data‐driven approach for model predictive control (MPC) performance monitoring. It explores the I/O data of the MPC system. First, to evaluate the MPC performance and capture the fluctuation of the process variables, we present an overall performance index based on Mahalanobis distance (MDBI) with its deduced benchmark. The Mahalanobis distance can better characterize the change of the process variable in both principal component space and residual space. As the proper vectors of the two spaces are orthogonal, the MDBI eliminates the correlation between the process variables while considering the variables’ characteristics in both spaces simultaneously, which helps evaluate the MPC performance more effectively with fewer monitoring parameters. Furthermore, for the MPC performance diagnosis, we use the MDBI as inputs and construct a support vector machine (SVM) pattern classifier. The classifier can achieve a higher accuracy when recognizing four common performance degradation patterns and determine the root cause of performance degradation. The results of simulations on the Wood‐Berry distillation column process and experiments on NIAT multifunctional experiment platform illustrate the effectiveness of the proposed performance assessment/diagnosis strategies.     

A Survey of Output Feedback Robust MPC for Linear Parameter Varying Systems

For constrained linear parameter varying (LPV) systems, this survey comprehensively reviews the literatures on output feedback robust model predictive control (OFRMPC) over the past two decades from the aspects on motivations, main contributions, and the related techniques. According to the types of state observer systems and scheduling parameters of LPV systems, different kinds of OFRMPC approaches are summarized and compared. The extensions of OFRMPC for LPV systems to other related uncertain systems are also investigated. The methods of dealing with system uncertainties and constraints in different kinds of OFRMPC optimizations are given. Key issues on OFRMPC optimizations for LPV systems are discussed. Furthermore, the future research directions on OFRMPC for LPV systems are suggested.      

基于网络时滞补偿的模型预测控制

为了解决网络控制系统中不确定时滞、尤其是随机的长时延对控制性能造成的影响,提出了基于模型匹配和多步预测输出补偿的预测控制思想来改善控制性能。该算法通过采集到的传感器端至控制器端的时滞来估算控制器端至执行器端的时滞,并给出了传感器端和执行器端数据处理的算法,建立起与实际网络结构匹配的预测控制模型,采用多步预测输出来补偿控制量的传输滞后。该模型不依赖于网络参数和时滞分布特性,可根据不同对象选取合适的预测控制算法,适用于实时系统。通过基于倒立摆对象的仿真和控制实例验证了该算法能有效地改善控制性能。     

Distributed MPC for Reconfigurable Architecture Systems via Alternating Direction Method of Multipliers

This paper investigates the distributed model predictive control (MPC) problem of linear systems where the network topology is changeable by the way of inserting new subsystems, disconnecting existing subsystems, or merely modifying the couplings between different subsystems. To equip live systems with a quick response ability when modifying network topology, while keeping a satisfactory dynamic performance, a novel reconfiguration control scheme based on the alternating direction method of multipliers (ADMM) is presented. In this scheme, the local controllers directly influenced by the structure realignment are redesigned in the reconfiguration control. Meanwhile, by employing the powerful ADMM algorithm, the iterative formulas for solving the reconfigured optimization problem are obtained, which significantly accelerate the computation speed and ensure a timely output of the reconfigured optimal control response. Ultimately, the presented reconfiguration scheme is applied to the level control of a benchmark four-tank plant to illustrate its effectiveness and main characteristics.      

Distributed Economic MPC for Synergetic Regulation of the Voltage of an Island DC Micro-Grid

In this paper, distributed model predictive control(DMPC) for island DC micro-grids(MG) with wind/photovoltaic(PV)/battery power is proposed, which coordinates all distributed generations(DG) to stabilize the bus voltage together with the insurance of having computational efficiency under a real-time requirement. Based on the feedback of the bus voltage, the deviation of the current is dispatched to each DG according to cost over the prediction horizon. Moreover, to avoid the excessive fluctuati...