On‐line Tuning of Model Predictive Controllers Using Fuzzy Logic

This paper addresses the problem of tuning model predictive controllers for good performance. An automatic online tuning strategy is developed to adjust the prediction horizon, P, the diagonal elements of the input weight matrix, Λ, and the diagonal elements of the output weight matrix, Γ. The control horizon is left constant because its relative value with respect to P is more important. The tuning algorithm is based on the fuzzy logic concepts. Predefined fuzzy rules that formulate the general tuning guidelines available in the literature and the performance violation measure in the form of fuzzy sets determine the new tuning parameter values. Therefore, the tuning algorithm is cast as a simple and straightforward mechanism with modest computational requirements. This feature makes it more appealing for online implementation. The effectiveness of the proposed tuning method is tested through simulated implementation on a binary distillation column example and on a non linear CSTR example. The result of the simulations revealed the success of such a method.     

多变量预测控制在乙醛精制装置中的应用

提出了一种乙醛精制装置的多变量预测控制策略。该策略应用自主开发的多变量预测控制软件将该装置两个精馏塔统一考虑实施多变量预测控制,减小了成品塔温度的波动,稳定了最终乙醛产品的质量,并使得其运行于最优的稳态工作点上。     

基于区间运算的字典序多目标模型预测控制

考虑优化级多目标优化控制问题,提出一种基于区间运算的字典序多目标模型预测控制算法。对约束线性系统,结合字典序列法和滚动优化原理,建立按优先级定义的分层多目标模型预测控制策略。在此基础上,引入区间运算法则,在线求解预测控制量。通过一双容水箱单输入双输出液位控制,仿真验证了该方法的有效性。     

Enhanced Performance Assessment of Subspace Model‐Based Predictive Controller with Parameters Tuning

This study focuses on performance assessment of model predictive control. An MPC‐achievable benchmark for the unconstrained case is proposed based on closed‐loop subspace identification. Two performance measures can be constructed to evaluate the potential benefit to update the new identified model. Potential benefit by tuning the parameter can be found from trade‐off curves. Effect of constraints imposed on process variables can be evaluated by the installed controller benchmark. The MPC‐achievable benchmark for the constrained case can be estimated via closed‐loop simulation provided that constraints are known. Simulation of an industrial example was done using the proposed method.     

基于统计分析的多变量预测控制性能检测与诊断

A statistic-based benchmark was proposed for performance assessment and monitoring of model predictive control; the benchmark was straightforward and achievable by recording a set of output data only when the control performance was good according to the user＇s selection. Principal component model was built and an autoregressive moving average filter was identified to monitor the performance; an improved T^2 statistic was selected as the performance monitor index. When performance changes were detected, diagnosis was done by model validation using recursive analysis and generalized likelihood ratio （GLR） method. This distinguished the fact that the performance change was due to plant model mismatch or due to disturbance term. Simulation was done about a heavy oil fractionator system and good results were obtained. Thediagnosis result was helpful for the operator to improve the system performance.     

A Composite Model Predictive Control Strategy for Furnaces

Tube furnaces are essential and primary energy intensive facilities in petrochemical plants. Operational optimization of furnaces could not only help to improve product quality but also benefit to reduce energy consumption and exhaust emission. Inspired by this idea, this paper presents a composite model predictive control (CMPC) strategy, which, taking advantage of distributed model predictive control architectures, combines tracking nonlinear model predictive control and economic nonlinear model predictive control metrics to keep process running smoothly and optimize operational conditions. The controllers connected with two kinds of communication networks are easy to organize and maintain, and stable to process interferences. A fast solution algorithm combining interior point solvers and Newton's method is accommodated to the CMPC realization, with reasonable CPU computing time and suitable online applications. Simulation for industrial case demonstrates that the proposed approach can ensure stable operations of furnaces, improve heat efficiency, and reduce the emission effectively.     

变增益的非线性预测控制算法

采用变增益策略,用输入与稳态输出的映射表示系统的静态非线性,用一个增益为1的ARX模型表示系统的动态模型,代替多数文献中常用的分段线性多模型方法进行非线性系统的预测控制.文中通过对连续搅拌釜反应器(CSTR)的仿真,验证了本算法的有效性.     

多变量分散型预测控制应用研究

研究了多变量分散型预测控制在应用中的主要问题,并提出了在多变量过程控制中采用各子系统分散建模、分散预测控制并辅以基于规则的智能协调,以解决多变量系统的解耦和优化控制问题。其工程应用优点是建模简单、优化计算量小并且保证控制的安全性。     

工业过程PI控制器性能评价

针对工业过程的PI控制器,提出了一种控制器性能评价的新算法。该算法以广义最小方差为目标,推导出最优PI控制器参数。以该最优控制器为基础,得到PI控制下系统的最优控制性能。然后,以最优控制的输出为基准,进行性能评价,并给出了性能评价的可操作步骤。最后通过仿真实验验证了该方法的有效性。     

基于Matlab/Simulink的气体分离装置多变量预测控制

依据丙烯精馏塔的工艺流程,介绍丙烯精馏塔机理模型和辨识模型的构建方法,并通过系统辨识方法得到了被控对象的多输入多输出模型。介绍预测控制及其算法的基本原理,通过编写基于状态空间模型的单步预测控制算法,对丙烯精馏塔进行多变量预测控制仿真,比较了预测控制算法对可测干扰,不可测干扰的抑制作用。     

RBF网络模型在预测函数控制中的应用

对三容液位系统的非线性复杂特点,利用RBF网络对系统建立预测模型,着重分析了RBF网络结构的选取、模型参数辨识以及网络优化的问题.通过预测函数控制验证了RBF网络模型在非线性系统建模中的优越性.     

非线性多变量系统的多模型广义预测解耦控制

针对实际工业过程中多变量系统存在着非线性、工况范围广、耦合强的特点,提出基于设定值观测器的非线性多模型广义预测解耦控制算法。该方法由线性广义预测控制器、一种新的设定值观测器和切换机构组成。理论分析和仿真结果表明,该控制策略不但可以保证闭环系统B IBO稳定和渐近收敛,而且能够得到很好的控制效果。     

基于稳态模型的自适应预测控制策略及其应用

针对丙烯精馏塔,结合模型预测控制器的设计,提出基于系统稳态模型的自适应模型预测控制策略,利用稳态模型在不同操作点上被控变量对操纵变量及扰动变量的相对变化率的变化,来刷新调整预测模型各通道的增益,以有效提高对丙烯塔塔顶、塔底温度控制的性能.现场应用表明:采用该模型自适应调整方法,其控制温度的波动性降低了一个数量级,有利于产品质量的稳定.     

基于主元回归方法的多变量控制系统性能评估

针对最小方差评价方法利用不切实际和不期望的指标进行性能评价所造成的性能评估缺陷,提出基于主元回归（PCR）的性能评价方法。该方法通过对输入输出数据进行建模可以得到比较符合实际的理论方差,以此理论方差与实际输出方差求出的控制系统性能指标,更能客观地反映控制系统的真实性能,且主元回归性能评价方法在性能计算中不需求解关联矩阵问题,极大地简化了求解的复杂度。最后通过仿真实例验证了主元回归性能评价方法更能客观反映控制系统真实性能和计算相对简易的优点。     

模型预测控制在钠氨合成工艺上的应用研究

基于钠氨合成连续化生产工艺,从化学反应机理出发,建立钠氨塔式反应器动态机理模型。并对模型进行动态分析,对多变量非线性模型进行集总化、线性化处理。推广单变量模型预测控制到多变量模型预测控制,对反应器温度进行控制。通过仿真比较,验证了MPC控制算法比PID控制具有更好的控制效果。     

纸机模型的预测函数控制

造纸过程是一个多变量、强耦合、大时滞的过程,采用传统的PID控制要达到很好的控制效果是很困难的。采用三种预测控制方法分别对纸机模型进行控制,并分别进行了仿真研究。从仿真结果可以看出,动态矩阵的跟踪效果不如广义预测控制,广义预测控制算法的跟踪性能较好,但是计算量较大,预测函数控制的响应速度较快,计算简单,控制效果也较好。     

基于广义预测控制为主的综合控制算法构成控制器的开发与应用

提出一种以广义预测控制算法为主,以由Smith预估参数组成的无模型控制律为辅,以多变量状态反馈为前馈的综合控制算法控制器,其中广义预测控制算法在PC机中实现并通过与DCS通讯完成数据交换,而Smith预估控制、无模型控制率、多变量状态反馈算法均在DCS中实现。此种控制器克服了广义预测控制抗干扰能力弱的缺点,在加热炉控制应用中其优越性得到充分体现,解决了加热炉自动控制的难题,保证了工艺指标。     

催化裂化反应再生系统的先进控制策略

催化裂化装置具有复杂的过程动态特性,操作约束与经济目标之间存在矛盾,从而为自动控制提出了深入的研究课题.从过程控制的角度讨论了催化裂化反应再生系统的过程特点,并在此基础上提出预测控制与协调决策一体化的先进控制策略.实际工业对象的应用结果表明,这样的控制方案是切实可行的.     

Multi-model Predictive Control of Ultra-supercritical Coal-fired Power Unit

The control of ultra-supercritical (USC) power unit is a difficult issue for its characteristic of the nonlinearity, large dead time and coupling of the unit. In this paper, model predictive control (MPC) based on multi-model and double layered optimization is introduced for coordinated control of USC unit. The linear programming (LP) combined with quadratic programming (QP) is used in steady optimization for computation of the ideal value of dynamic optimization. Three inputs (i.e. valve opening, coal flow and feedwater flow) are employed to control three outputs (i.e. load, main steam temperature and main steam pressure). The step response models for the dynamic matrix control (DMC) are constructed using the three inputs and the three outputs. Piecewise models are built at selected operation points. Double-layered multi-model predictive controller is implemented in simulation with satisfactory performance.     

考虑裕量损失函数的预测控制新架构

在常规预测控制性能指标函数的基础上加入一个表征操作变量空间变化情况的裕量损失函数,提出一种考虑裕量损失函数的预测控制新架构,并推导其优化求解过程。同时,通过稳态最优值分析新架构保留操作变量空间的效果,并采用稳态分析在预测控制中加入裕量损失函数对控制效果的影响。最后,通过实例仿真证实了新算法的有效性。