多变量模糊自校正控制器及其应用

本文介绍了表达ＭＩＳＯ动态系统的模糊模型，并提出了有关参数和结构的在线辨识算法。根据辨识的模糊模型，利用Ｃｌａｒｋｅ的单变量广义预测控制（ＧＰＣ）原理［１］设计了多变量模糊自校正控制器。仿真研究表明，该模糊自校正控制方法应用于火电机组负荷系统的控制，可以收到良好的效果。     

非线性系统的多模态ARMAX模型及其参数辨识

本文利用非线性系统多个工作点上的线性化模型，建立了一类参数内插型多模态ＡＲＭＡＸ模型。与传统的线性化模型相比较，在更大的范围内有较高的精度，本文并给出了如何辨识多模态模型参数的方法，仿真例子表明了多模态模型及其参数辨识算法的有效性。     

含ARMA噪声系统模型的参数辨识方法*

实际问题中，大量的动态系统控制问题可归结为含ＭＡ，ＡＲＭＡ噪声系统模型的参数辨识问题。本文提出ＲＭＡ，ＲＡＲＭＡ两种系统模型参数辨识的一种新方法，主要手段是构造和研究特殊的辅助线性模型。理论分析和实际计算表明，本文方法较传统表度有明显提高。     

多变量ARMA信号自校正最优去卷平滑估计方法及其渐近稳定性*

本文讨论信号去卷估计问题。运用新息理论和射影方法，基于ＡＲＭＡ新息模型设计多变量ＡＲＭＡ信号最优去卷平滑器，讨论了平滑器的渐近稳定性。在信号模型及噪声统计未知时，通过在线辨识ＡＲＭＡ新息模型提出了ＡＲＭＡ信号自校正去卷平滑器。     

风洞系统非线性块状结构模型

本文将非线性块状模型的建模思想引入风洞系统模型的建立过程中,针对主排气阀和栅指电液伺服机构具有死区非线性特性,分别用含有死区输入的Hammerstein块状模型描述其动态特性,将主排气阀和栅指机构的输出作为风洞流场的输入,建立两输入两输出多变量耦合动态模型.两个独立的Hammerstein子模型与线性动态耦合的风洞流场模型串联构成一个非线性多变量块状模型.采用自适应加权递推辨识算法在线辨识Hammerstein子模型参数,采用带有遗忘因子的递推最小二乘法辨识风洞流场模型参数.仿真与风洞现场测试结果验证了本文方法的有效性.     

带白色观测噪声的ARMA模型参数的无偏估计

本文研究了如何利用受白色噪声污染的观测数据辨识ＡＲＭＡ（ｐ，ｑ）模型参数数据的问题，提出了一种递推辅助变量法．利用这种方法首先辨识出ＡＲ（ｐ）部分的参数及观测噪声的方差，然后根据所得的估计利用常用的Ｎｅｗｔｏｎ－Ｒａｐｈｓｏｎ方法确定ＭＡ（ｑ）部分的参数。     

基于组合式信号源的Hammerstein-Wiener模型辨识方法

针对含有有色噪声的非线性Hammerstein-Wiener模型,提出一种基于组合式信号源的辨识方法.通过利用可分离信号和随机信号组成的组合信号源实现有色噪声干扰下Hammerstein-Wiener模型各串联模块参数辨识的分离,简化辨识过程.首先,基于可分离信号的输入和输出,采用相关分析方法抑制过程噪声的干扰,辨识输出静态非线性模块和动态线性模块的参数;然后,基于辅助模型技术,利用辅助模型的输出和残差的估计值分别取代辨识模型中的不可测中间变量和噪声变量,推导辅助模型递推增广最小二乘方法,根据随机信号的输入输出数据辨识输入静态非线性模块和噪声模型的参数;最后,通过理论分析和仿真结果表明,所提出方法能够有效辨识有色噪声干扰下的非线性Hammerstein-Wiener模型,具有较好的鲁棒性.     

线性多变量系统Markov参数估计的MATLAB语言实现

本介绍了系统辨识中线性多变量系统Markov参数估计MATLAB语言实现方法，并给出了求解实例，说明了MATLAB语言使用灵活简便。     

带有色观测噪声的多变量自校正去卷滤波器

本文运用新息理论和射影的方法，通过在线辨识ＡＲＭＲ新息模型提出了多变量非平稳ＡＲＭＡ信号自校正去卷滤波器。     

多变量复杂系统的模糊控制

多变量复杂系统的模糊控制ＦｕｚｚｙＣｏｎｔｒｏｌｏｆｔｈｅＭｕｌｔｉｐｌｅＶａｒｉａｂｌｅｓＳｙｓｔｅｍ●马维明ＭａＷｅｉｍｉｎｇ１引言在实际生产过程中，特别是化工生产过程中会遇到多变量控制系统，被控参数Ｙ是Ｘ１、Ｘ２、Ｘ３…Ｘｎ的函数，而Ｘ１、Ｘ２...     

降阶模糊控制算法及其应用研究

提出降价模糊控制算法,利用多级控制策略,采用降阶函数融合信号,可以简化多输入情况下模糊控制器设计。使用线性降价模糊方法成功实现二级倒立摆的控制,证明了算法的有交效性和鲁棒性。     

一种基于可扩展散列表的倒排索引更新策略

该文提出一种新的基于可扩展散列表的倒排索引更新策略，使倒排索引具有良好的可扩展性。它既支持文档的插入、删除操作，又具有较高的查询效率和空间利用率。并在它的基础上，实现了倒排索引的增量更新和实时更新。     

Decentralized closed-loop parameter identification for multivariable processes from step responses

This paper presents a novel technique for on-line decentralized closed-loop parameter identification of multivariable processes from step responses. Based on simple sequential step tests, the coupled closed-loop n-inputs and n-outputs (n × n) multivariable process is decoupled equivalently into n² independent single open-loop processes with an unit step input signal acting on the n² transfer functions. By using virtual step response signals, the parameters of each element in the transfer function matrix can be directly identified by the well-developed least squares methods. The significance of the proposed method is that it relaxes most restrictions of existing multivariable process identification methods, it is universally applicable to closed-loop identification for cross-coupling multivariable processes. Simulation examples are given to show both effectiveness and practicality of the identification method for a wide range of multivariable processes.     

多变量造纸过程的开环辨识研究

为了得到造纸过程的精确模型,对造纸过程的开环辨识进行了仿真研究,得到了模型的时间常数与辨识的采样时间有密切的关系和增加采样时间可以提高辨识精度的结论。     

基于逆系统的赖氨酸发酵多变量解耦内模控制

针对赖氨酸发酵过程的时变、非线性和高耦合性,提出基于逆系统的赖氨酸发酵多变量解耦内模控制方法。根据动态递归模糊神经网络(DRFNN)的非线性辨识原理离线建立发酵过程的逆模型,将得到的逆模型串联在发酵系统之前,实现了发酵过程输入输出解耦线性化,从而得到伪线性系统;对复合后的伪线性系统采用内模控制。仿真结果表明,该方法能够适应赖氨酸发酵过程模型的不确定性和参数的时变性,具有较强的鲁棒性,且结构简单,易于实现。     

A tutorial review on process identification from step or relay feedback test

Step and relay feedback tests have been widely used for model identification in the process industry. The corresponding identification methods developed in the past three decades are surveyed in this paper. Firstly, the process models with time delay mainly adopted for identification in the literature are presented with a classification on different response types. By categorizing the major technical routes developed in the existing references for parameter estimation relating to different applications, the identification methods are subsequently clustered into groups for overview, along with two specific categories for robust identification against load disturbance and the identification of multivariable or nonlinear processes. The rationales of each category are briefly explained, while a typical or state-of-the-art identification algorithm of each category is elucidated along with application to benchmark examples from the literature to illustrate the achievable accuracy and robustness, for the purpose of facilitating the readers to have a general knowledge of the research development. Finally, an outlook on the open issues regarding step or relay identification is provided to call attention to future exploration.     

Case studies on closed-loop identification for MPC

In this work we study multivariable and closed-loop identification of industrial processes for use in model predictive control. The advantages of closed-loop identification are discussed and related problems of identification are outlined. Subsequently, two case studies are used to demonstrate the advantages of closed-loop identification. The first process is a simulated high purity distillation column; the second process is a deethanizer of an industrial scale ethylene unit.     

对角CARIMA模型多变量自适应约束广义预测控制

为了简化约束存在时多变量广义预测控制算法的设计与实现,依据对角CARIMA模型的结构特点,将多输入多输出对象的参数辨识和模型预报问题转化为一系列多输入单输出子对象的参数辨识和模型预报问题.推导了输入输出的约束形式及优化求解过程.简化了多变量对象的参数辨识、模型预报、目标函数和约束条件系数矩阵的计算.在由DCS控制的非线性液位装置上的对比实验结果表明了该方法的有效性.     

Multivariable adaptive predictive control of an aerated lagoon for a wastewater treatment process

The application of an adaptive multivariable predictive model-based control scheme for the control of biotechnological processes is reported. Control design consists of regulating the residual concentrations of two main variables of a multistage wastewater treatment process. Unavailability of measurements leads to the development of an identification technique derived to estimate simultaneously unavailable state variables and time-varying parameters of a nonlinear process. Convergence of the estimation scheme is demonstrated via a theorem and its proof using Lyapunov's method. The estimated variables are used in the explicit design of the control algorithm. Good simulation results have been obtained in regulation, tracking, disturbance rejection and transient behaviour, showing the efficiency of this adaptive control strategy.     

Reduction and identification of multivariable processes using regression analysis

The paper is concerned with the application of multivariate regression analysis to the reduction of multivariable control problems and to the identification of linear and nonlinear time-varying processes. Reduction is performed by grouping the input and output variables of a many variable process into a small number of groups. Control is exercised in terms of a few variables, each representing such a group. Regression is further applied to the dynamic identification of reduced or unreduced linear and nonlinear multivariable processes where no a priori information of the dynamic characteristics is available. Both reduction and identification may be performed on-line. The resulting techniques are conveniently incorporated in control procedures based on dynamic programming and on predictive adaptation.