基于内模控制的Smith反向解耦控制器设计

针对多变量时滞系统提出了一种基于内模控制(Internal Model Control,IMC)的Smith动态解耦控制器设计方法。其中,Smith补偿器被应用在该控制结构中,包含带有时滞项和不带时滞项的补偿结构,以解决不能完全补偿的问题,通过反向解耦设计实现多变量时滞系统动态解耦。多变量系统被解耦成一系列互相独立的单回路对象,通过内模控制原理对Smith控制器进行参数整定,并分析了系统的鲁棒性。仿真实例表明了该方法的有效性,能够较好地克服系统参数扰动导致的干扰,并具有较好的动态性能。     

大纯滞后信号解耦内模控制系统研究

针对多变量大纯滞后强耦合控制系统的设计研究,采用了多变量系统信号解耦方法和大纯滞后系统的内模控制方法。通过对大滞后系统的信号解耦方法研究,提出了大滞后耦合系统信号解耦的"耦合特征矩阵"的构造方法,给出了大滞后系统的"能解耦准则"和"系统完全动态解耦准则",以及信号解耦系统设计的"分离原理"和信号解耦系统的"稳定性判据"。实例表明信号解耦方法对大纯滞后耦合控制系统的设计非常简洁方便。     

基于C-R模型的非线性系统模糊内模控制算法研究

针对大多数非线性系统的内模控制难以建立精确的对象模型和逆模型的问题,引入了基于C-R模型的非线性系统建模方法,并在此基础上研究基于C-R模型的非线性系统的模糊内模控制设计。由C-R模型的辨识递推算法获得对象模型和逆模型,并建立模糊内模控制算法。通过对连续搅拌反应釜控制系统的仿真表明,该控制方法有效、可行,具有良好的控制品质。     

基于动态补偿逆的非线性RBF内模控制及其应用

针对非线性不确定系统和传统的非线性内模控制在控制上存在的不足,提出一种基于动态补偿逆的非线性不确定系统RBF内模控制,在引入RBF建立逆模型的同时,将无模型自适应控制方法作为附加控制器,用于模型偏离被控对象时在线修正逆模型。仿真结果表明,本文提出的方法不仅对系统的常量摄动具有较好的鲁棒性,对时变不确定性仍然保持较好的跟踪效果,具有较好的实时性、鲁棒性和在线校正功能。     

工业过程多变量系统的辅助常规控制设计

利用PID控制器对多变量系统进行控制,主要采取分散常规控制或解耦控制的策略。对于存在耦合的系统,分散常规控制无法消除各变量间的相互扰动,而直接设计动态解耦矩阵有时候会存在困难,静态解耦的效果却并不明显。针对已设计好分散常规控制器的方系统,以解耦为目标和原则,设计多变量系统中分散常规控制方案剩余配对的控制器作为辅助控制器,以频域近似的方法整定其PID参数,实现多输入多输出的控制结构,降低了各变量的相互耦合作用,提高了控制性能。通过对Shell公司的典型控制问题的辅助控制器设计与仿真实验,取得了比原有分散常规控制与静态解耦控制更好的控制效果,验证了控制策略的可行性与有效性。     

基于内模控制的滤波方法改进及参数优化实施

针对实际工业控制中出现的系统的随机噪声和量测噪声会严重影响现场生产的控制效果,传统的内模控制方式无法很有效地解决这一问题。将Kalman滤波器引入到传统的内模控制原理中,通过Kalman滤波器来减小甚至消除噪声对控制系统的影响,提高系统的控制精度。同时,利用NLJ算法在考察系统性能指标的情况下,对传统内模原理中的低通滤波器的滤波参数进行自动寻优。通过对滤波环节的改善,充分发挥Kalman滤波和NLJ自动寻优的特点,使得控制系统的鲁棒性和快速性都得到了提高。仿真结果表明提出的设计方法能较好解决单变量和多变量被控对象的实时性和时滞性,并且具有良好的抗噪性,该方法参数调节容易,易于实施。     

改进的内模控制方法对一阶非自衡对象的控制研究

针对传统的内模控制方法在非自衡对象中应用的不足,提出一种改进的内模控制方法。该方法在传统的内模控制结构中添加了一个比例控制器和一个比例微分控制器,分别用于镇定不稳定对象和改善控制系统的抗干扰特性及鲁棒特性,并采用全极点近似法对被控对象的纯滞后项进行近似处理。改进的内模控制方法具有很好的目标跟踪特性和干扰抑制特性,各个控制器的设计简单,且整个控制系统只有一个调节参数。最后通过对一个一阶非自衡对象进行仿真,其结果说明了本文所提出的内模控制新方法的有效性。     

一类多变量系统的自抗扰非线性动态解耦控制

针对一类多变量系统控制中的耦合问题,提出了一种基于自抗扰技术的非线性动态解耦控制(ADRC)方法。该方法不依赖于系统的精确数学模型,分别在控制器耦合矩阵部分已知和未知的情形下,在局部静态解耦的基础上,将各子系统的模型摄动、外扰和包括输入变量相互作用在内的动态耦合视为各通道上的扰动总和,通过引入虚拟控制和状态量,设计扩张状态观测器(ESO)估计总扰动并进行反馈补偿,进而再对各解耦子对象分别设计非线性单输入单输出ADRC以保证闭环系统稳定。最后以蒸馏塔模型的过程控制仿真验证了该方法具有良好的动态解耦效果,对模型不确定性和外部扰动具有较好的鲁棒性和适应能力。     

基于球磨机的LS-SVM广义逆内模控制研究

给出一种最小二乘支持向量机(Least Square Support Vector Machine,LS-SVM)广义逆内模控制方法。利用LS-SVM辨识这类系统的广义逆,再与原被控系统串联成具有近线性伪线性的开环控制系统,引入内模控制使其变成稳定的闭环控制回路,将这种方法应用在球磨机控制系统中。经仿真分析,该方法不依赖于被控系统精确的数学模型,实现了小样本训练的准确辨识,提高了系统的动态响应,并与内模控制相结合,使其闭环控制系统鲁棒稳定性增强。     

基于滑模的多变量广义预测解耦控制

针对多变量控制系统的耦合问题,将广义预测控制和滑模控制结合起来,提出一种基于滑模的多变量广义预测解耦控制方法.首先把m个输入n个输出的多变量耦合系统分解成m个输入单个输出子系统,再通过对子系统输出预测得到滑模切换函数值,求解开环优化求得控制律,最后通过仿真实验表明,该控制方法对多变量耦合系统的控制是正确有效的.     

Design of a multivariable internal model controller based on singular value decomposition

In this article, a novel internal model control (IMC) approach based on singular value decomposition (SVD) is proposed for the control of multiple‐input–multiple‐output (MIMO) systems with multiple time delays. This approach achieves decoupling using a compensation term and improves the robustness using SVD in the inverse of the steady‐state gain matrix of process. Meanwhile, a novel filter is designed for decoupling and fast response speed of multivariable systems with multiple delays. The design of the controller can be extended to non‐square systems where there are more inputs than outputs. Examples are included to illustrate the effectiveness of the method. © 2012 Canadian Society for Chemical Engineering     

Decoupling internal model control for multivariable systems with multiple time delays

In this paper, the decoupling internal model control (IMC) with stability is investigated for multivariable stable processes with multiple time delays. All the stabilizing IMC controllers which solve this decoupling problem and the resulting closed-loop systems are characterized in terms of the open-loop system's time delays and non-minimum phase zeros. It shows that the inclusion of some time delays and non-minimum phase zeros might be necessary to make a decoupling solution realizable and stabilizing. Based on this characterization, a control design method for best achievable performance is presented. However, owing to the high complexity of the theoretical controller, a practical controller design procedure is developed with the help of the proposed model reduction algorithm. Examples are given to illustrate our analysis and design. Significant performance improvement over the existing multivariable Smith predictor control has been achieved with the proposed approach.     

Multi-loop nonlinear internal model controller design based on a dynamic fuzzy partial least squares model

In this paper, a dynamic fuzzy partial least squares (DFPLS) modeling method is proposed. Under such framework, the multiple input multiple output (MIMO) nonlinear system can be automatically decomposed into several univariate subsystems in PLS latent space. Within each latent space, a dynamic fuzzy method is introduced to model the inherent dynamic and nonlinear feature of the physical system. The new modeling method combines the decoupling characteristic of PLS framework and the ability of dynamic nonlinear modeling in the fuzzy method. Based on the DFPLS model, a multi-loop nonlinear internal model control (IMC) strategy is proposed. A pH neutralization process and a methylcyclohexane (MCH) distillation column from Aspen Dynamic Module are presented to demonstrate the effectiveness of the proposed modeling method and control strategy.     

ADAPTIVE INTERNAL MODEL CONTROL OF HAMMERSTEIN-NONLINEAR SYSTEMS

Adaptive internal model control is analyzed for a single-input-single-output nonlinear system represented by the Hammerstein model wherein the nonlinearity is an odd order polynomial. The recursive least-squares method of Chang and Luus (1971) is used for parameter estimation. To obtain a stable approximation to the inverse of the process model for use as the controller, Vogel-Edgar's method (1980) for linear system is employed. Simulation results have shown that the adaptive internal model control (IMC) performs well even with non-minimum phase Hammerstein systems in the presence of unmeasured load changes and dead-time variations. The performance of nonlinear IMC is found to be superior to that of linear IMC.     

A feedback control scheme for SISO systems with time delays and constrained inputs

A feedback control scheme for SISO systems with time delays and constrained inputs is proposed. The controller is designed through the internal model control (IMC) technique and the Smith predictor structure is used for dead time compensation. Through computer simulation, the performance of the proposed scheme has been compared with the classic feedback scheme designed via the IMC procedure. The results show that the proposed scheme performs better due to the preservation of the predictive property.     

About the possibility of a direct concentration control for distillation columns

The results of simulations of direct concentration control of a distillation column in industrial scale are presented. Both controlled variables, the two product concentrations, are determined by on-line gas chromatographs and are characterised by high dead times of about 30 min. For the resulting scanning control of the product concentrations corresponding decentralised PI controllers were developed and applied. The control of the product concentrations developed presents a conventional DV structure and was exemplarily tested for disturbances in feed flow and feed concentrations. The calculations carried out exhibit a good time behaviour of the whole system. On this occasion it can be stated that the control quality could be clearly improved if a steady-state decoupling by output transformation was employed. In this case the results achieved are totally comparable to those of internal model control (IMC), which was in use at the production plant for over two years.     

化工双输入输出时滞过程解耦PID控制器解析设计

基于常规单位反馈控制结构,针对具有时滞特性的化工双输入输出过程提出一种解耦PID控制器的解析设计新方法.该解耦PID控制器的突出优点是控制器设计过程简单直观;控制器可以实现标称系统输出间的显著解耦;系统输出分别使用相应控制器对角元素中的调节参数进行单参数整定.同时,对于实际生产过程中常见的被控过程乘性不确定性,分析了系统保持鲁棒稳定的充要条件.仿真实例验证了该方法优于其它方法.     

基于ARX-PLS解耦框架下的多回路约束迭代模型预测控制方法(英文)

A multi-loop constrained model predictive control scheme based on autoregressive exogenous-partial least squares （ARX-PLS） framework is proposed to tackle the high dimension, coupled and constraints problems in industry processes due to safety limitation, environmental regulations, consumer specifications and physical restric-tion. ARX-PLS decoupling character enables to turn the multivariable model predictive control （MPC） controller design in original space into the multi-loop single input single output （SISO） MPC controllers design in latent space. An idea of iterative method is applied to decouple the constraints latent variables in PLS framework and recursive least square is introduced to identify ARX-PLS model. This algorithm is applied to a non-square simulation system and a stirred reactor for ethylene polymerizations comparing with adaptive internal model control （IMC） method based on ARX-PLS framework. Its application has shown that this method outperforms adaptive IMC method based on ARX-PLS framework to some extent.