隐式多变量广义自校正控制器

本文提出了具有在线选择加权阵的两种多变量自校正控制器.加权阵的在线选择和自校 正控制算法都采用隐式方案来实现.本文还证明了该自校正控制器具有全局收敛性.     

统一的自校正控制器

本文提出两种统一的广义最小方差自校正控制器,以控制具有任意维数输入/输出的 (ADIADO)系统.这两种新的控制器解决了以前无法处理的输入/输出维数不等、Bo奇异系 统的自校正控制问题.几个典型的仿真例子证明了这两种控制器的统一性和有效性.     

多变量自校正控制器的状态空间设计

本文针对输入、输出维数相同的多变量随机系统提出了两种状态空间自校正控制器-- 多变量状态空间极点配置自校正控制器和多变量状态空间自校正跟踪控制器.文中给出两种 控制器结构和理论分析,前者适用于稳定/不稳定、最小相位/非最小相位线性时不变多变量系 统,后者适用于稳定/不稳定、最小相位线性时不变多变量系统,简单的结构和优良的性能使两 种算法能较好地应用于实时系统.     

一种多变量隐式极点配置自校正控制器及其应用A

本文提出了一种极点配置自校正控制器.该控制器不仅能控制开环不稳定或非最小相位 系统,而且能控制具有任意、未知或变化传输延时结构的多变量系统.该控制器采用隐式方式 来实现即利用输入输出数据直接辨识控制器的参数.这样不仅避免在线解矩阵方程,而且增 加算法的鲁棒性.仿真和实时控制多变量电加热系统的结果表明该控制器具有好的控制性能.     

过程—FORTRAN-Ⅳ与自校正控制在线软件包

本文介绍一个适用于小型机的自校正控制在线软件包,同时给出了一个编写控制程序的软件工具—过程—FORTRAN-Ⅳ。这个软件包可以同时完成单输入单输出系统的在线辨识,离线自校正控制的参数选择和在线自校正控制。     

多输入预报自校正前馈控制器及其在高频焊管焊接过程控制中的应用

本文针对高频焊管焊接过程的控制,提出了一种适用于具有多种扰动对象的多输入预报自校正前馈控制器,其特点是综合了广义最小方差控制的最优性、极点配置控制的鲁棒性和多输入前馈控制的抗扰性等优点。该控制器采用间接算法实现,在线选择二次型性能指标中加权项。它不仅可以消除可测干扰的影响,消除稳态跟踪误差,而且即使用于非最小相位系统也具有全局收敛特性。理论分析和仿真实验表明,提出的控制器具有良好的控制性能,可以适应焊接过程环境的变化,从而获得满意的控温效果。     

一种极点配置自校正导弹控制器的设计方法

根据导弹的非线性时变的特点,以及自适应控制的优点,结合计算机技术与现代控制理论,设计了一种极点配置自校正导弹控制器.该文为滚转通道建立了二阶离散传递函数模型,给出了模型参数的在线辨识方法,研究了极点配置自校正控制器的设计方法,给出了控制量的计算公式和仿真结果.仿真的结果表明,该控制器对非线性时变的控制对象具有满意的控制效果.     

广义自校正控制器及其在飞行高度控制中的应用

本文将广义最小方差控制与极点配置控制结合起来提出了具有加权项在线选择的广义自校正控制器.该控制器采用最小二乘辨识算法来辨识控制器参数,采用显式方式来实现.本控制器可以保证自适应控制系统的整体稳定性.本文还进行了广义自校正控制器应用于飞行高度控制中的仿真研究.研究结果表明,采用本控制器完全可以适应当飞行条件变化时被控对象参数的变化,从而获得令人满意的控制效果.     

基于自校正分散控制的Web服务器比例延迟保证

Apache Web服务器一般采用单变量模型,处理多优先级延迟保证时需多次建模,可扩展性不佳。为此提出一种Web服务器多输入多输出(MIMO)模型,并结合分散控制理论和自校正控制(STC)理论设计分散自校正控制器。该控制器动态调节处理不同优先级请求的工作线程数目,能保证较高优先级请求更快得到处理且维持不同优先级请求的平均延迟比为设定值,模型和控制器参数根据在线辨识结果实时更新。仿真表明,过载情况下即使并发客户连接数目急剧变化,闭环系统中的服务器仍能维持较好的比例延迟保证。     

极点配置自校正预估PID控制器

对于时滞过程,本文提出了一种新的自校正预估 PID 控制器,它实现了极点配置和静态抗干扰性能,克服了文献[2]提出的自校正预估 PI 控制器和文献[5]提出的自校正预估 PID 控制器不能在线校正控制器参数的缺点.仿真例子说明了本文结果的有效性.     

Multivariable nonlinear control applications for a high purity distillation column using a recurrent dynamic neuron model

A dynamic, nonlinear, multi-input multi-output application using the Recurrent Dynamic Neuron Network (RDNN) model is presented for a two-by-two distillation column case study. It is shown that the RDNN model, though compact (in terms of number of neurons and parameters to be estimated) performs well in both open- and closed-loop simulations. Open-loop simulations show that the RDNN is able to predict nonlinear output responses. The dual composition control problem is also investigated to demonstrate the model-based applications attainable with the RDNN. Due to the control affine nature of the RDNN structure, and the fact that it has finite vector relative degree, Input-Output Linearization techniques were used within the Internal Model Control framework for controller design. Nonlinear Model Predictive Control applications were also demonstrated using the RDNN. Simulations show that a combination of closed-loop and open-loop identification for the RDNN model results in a model-based controller which achieves robust closed-loop performance.     

Globally linearized control system design of a constrained multivariable distillation column

The goal of this paper is to develop a discrete-time multivariable globally linearized control (GLC) algorithm, which provides low computational requirements with constraint handling ability. The control strategy is constructed with four elements: a transformer that accounts for process nonlinearities; an estimator, which observes the required unmeasured states; a variable constraint mapping optimizer that transforms the input constraints of the nonlinear process into constraints on the manipulated inputs of the globally linearized system and a quadratic dynamic matrix controller (QDMC) that provides constraints handling ability. The effectiveness of the designed controller has been tested on a multi-input multi-output (MIMO) nonlinear distillation column through extensive numerical simulations. The control law showed a high quality performance for set point tracking and disturbance rejection in presence of parametric uncertainty. The effect of unmeasured disturbance also has been studied through the simulation experiment. In the comparative study, the proposed GLC-QDMC control technique outperformed the GLC-DMC control law.     

Self-tuning regulator applied to a binary distillation column

Utilization of a self-tuning regulator (STR) for control of top product composition of a binary distillation column has been investigated. Results from simulation studies and experimental evaluation of the STR on a pilot scale column are compared with the performance achieved using conventional proportional plus integral control. The STR resulted in significantly improved control for both servo and regulatory control.     

The nonlinear model-predictive control of a chemical plant using multiple neural networks

A combination of multiple neural networks (NNs) is selected and used to model nonlinear multi-input multi-output (MIMO) processes with time delays. An optimisation procedure for a nonlinear model-predictive control (MPC) algorithm based on this model is then developed. The proposed scheme has been applied and evaluated for two example problems, including the MPC of a multi-component distillation column.     

基于模式识别的自整定控制及应用

本文提出一种基于模式识别的自整定控制方法,以模式表示和模式分类来描述系统的动态特性和调节器的结构,用模式识别优化方法求取调节器的参数,当系统动态模型参数未知时,这种自整定方法是建立在系统测量数据序列基础上的。该方法在实验性二元精馏塔上用IBM-PC微机获得成功应用。其结果优于常规PID调节器。     

精馏塔的自适应解耦控制

本文针对某精馏塔提出了一种简单的自适应解耦控制器。该控制器将广义最小方差控制策略和解耦补偿器结合起来,不仅可以对随机多变量系统实现动静态解耦而且具有良好的伺服跟踪性能。该控制器与常规PI控制器在某精馏塔双组分控制中的对比实验结果表明自适应解耦控制的性能优越于常规PI控制。     

一种鲁棒逆基控制器的设计方法

针对多变量病态过程的控制问题,提出一种鲁棒逆基控制器的设计方法。该方法把频域响应近似法(Frequency Response Approximation,FRA)和高级鲁棒控制方法相结合,采用频域响应近似法设计一个低阶逆基控制器来近似,通过高级鲁棒控制方法获得的高阶鲁棒控制器。得到的控制器结构简单,容易实现,并且具有良好的鲁棒控制性能。通过对一个在过程控制领域被广泛研究的病态精馏塔模型进行设计与仿真,验证了该方法在病态过程控制问题中的有效性。     

多变量极点配置自校正Smith控制器及其应用

本文根据许多工业被控过程具有大纯滞后的特点,将自校正调节器与Smith预估器相结 合,给出一种多变量极点配置自校正Smith控制算法.该算法结构简单,不仅能对输出进行预 报和无偏跟踪,而且适用于非最小相位系统.该算法已成功地应用于太原钢铁公司轧辊罩式 退火炉微机自适应群控系统,实际运行结果表明控温精度和炉内温度场均匀度均在±3℃以 内,远比常规PID仪表的控温效果好.     

Control of integrated processes: A case study on reactive distillation in a medium-scale pilot plant

This paper deals with the systematic design of a multivariable controller for a medium-scale reactive distillation column that is operated in semi-batch mode. This is a challenging problem because of the time-varying and strongly nonlinear dynamics of the process and considerable deviations of the behaviour of the real plant from the rigorous model used for process design. The design procedure consists of three steps: first, a suitable control structure that enables the operation of the column near the economically optimal operating point is determined based upon the rigorous nonlinear process model. In a second step, a linear model of the column is identified from experiments and used to compute the best attainable control performance for the chosen control structure. In this step, actuator limitations and model uncertainties described by confidence intervals that were obtained in the identification procedure are considered. In the third step, the resulting high-order controller is approximated by a low-order controller that gives nearly the same performance and preserves robust stability for the computed uncertainty bounds. The controller performance is demonstrated in a series of experiments that were performed at the real reactive distillation column.