稳态目标优化的稳定MIMO约束预测控制

A two-stage multi-objective optimization model-predictive control algorithms(MPC) strategy is presented. A domain MPC controller with input constraints is used to increase freedom for steady-state objective and enhance stabilization of the controller. A steady-state objective optimization algorithm oriented to transient process is adopted to realize optimization of objectives else than dynamic control. It is proved that .the stabilization for both dynamic control and steady-state objective optimization can be guaranteed. The theoretical results are demonstrated and discussed using a distillation tower as the model. Theoretical analysis and simulation results show that this control stratek--v is efl$cient and provides a good strategic solution to uractical urocess control.     

Optimization and Control of Pressure Swing Adsorption Processes Under Uncertainty

The real‐time periodic performance of a pressure swing adsorption (PSA) system strongly depends on the choice of key decision variables and operational considerations such as processing steps and column pressure temporal profiles, making its design and operation a challenging task. This work presents a detailed optimization‐based approach for simultaneously incorporating PSA design, operational, and control aspects under the effect of time variant and invariant disturbances. It is applied to a two‐bed, six‐step PSA system represented by a rigorous mathematical model, where the key optimization objective is to maximize the expected H₂ recovery while achieving a closed loop product H₂ purity of 99.99%, for separating 70% H₂, 30% CH₄ feed. The benefits over sequential design and control approach are shown in terms of closed‐loop recovery improvement of more than 3%, while the incorporation of explicit/multiparametric model predictive controllers improves the closed loop performance. © 2012 American Institute of Chemical Engineers AIChE J, 59: 120–131, 2013     

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.     

基于预测控制的全流程节能降耗优化控制方法

研究了基于预测控制模型的面向相互耦合的多个过程单元的以节能降耗为目的的全流程优化控制方法,并针对某炼油厂2×105 t/a气分装置先进控制技改项目,在各个精馏塔实施了多变量模型预测控制的基础上,提出了基于全流程优化控制方法的产品质量和能耗的整体优化方案,并在虚拟生产装置上进行仿真验证,结果表明该方法有效可行.     

基于模型预测控制的反应再生工艺控制实现

介绍了先进控制系统中的模型预测控制技术。具体阐述了模型预测控制中的动态矩阵控制算法（DMC）,包括预测模型,滚动优化,反馈校正。并以炼油厂催化裂化装置的反应再生工艺为例,分析了反应再生工艺控制的多变量、多耦合特性,指出了控制的难点。最后,采用动态矩阵控制算法对反应再生工艺的一再、二再温度进行了优化控制,最终实现了控制目标。     

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。     

多变量约束预测控制在常压塔系统中的应用

利用多变量系统能处理变量之间强耦合作用以及预测控制对模型要求不高的特点，将多变量约束预测控制应用于常压塔温度控制系统中，效果较好。     

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.     

基于Tent混沌优化的神经网络预测控制

With the unique ergodicity, irregularity, and special ability to avoid being trapped in local optima, chaos optimization has been a novel global optimization technique and has attracted considerable attention for application in various fields, such as nonlinear programming problems. In this article, a novel neural network nonlinear predictive control (NNPC) strategy based on the new Tent-map chaos optimization algorithm (TCOA) is presented. The feedforward neural network is used as the multi-step predictive model. In addition, the TCOA is applied to perform the nonlinear rolling optimization to enhance the convergence and accuracy in the NNPC. Simulation on a laboratory-scale liquid-level system is given to illustrate the effectiveness of the proposed method.     

基于Tent混沌优化的神经网络预测控制

With the unique erggdicity, i rregularity, and.special ability to avoid being trapped in local optima, chaos optimization has been a novel global optimization technique and has attracted considerable attention for application in various fields, such as nonlinear programming problems. In this article, a novel neural network nonlinear predic-tive control （NNPC） strategy baseed on the new Tent-map chaos optimization algorithm （TCOA） is presented. Thefeedforward neural network＇is used as the multi-step predictive model. In addition, the TCOA is applied to perform the nonlinear rolling optimization to enhance the convergence and accuracy in the NNPC. Simulation on a labora-tory-scale liquid-level system is given to illustrate the effectiveness of the proposed method.     

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.     

偏差加权协调预测控制算法研究

提出适用于瘦系统的偏差加权协调控制算法，并且分析其参数确定方法和稳定性。实际应用表明该算法能实现平稳过渡．控制效果良好。     

多变量预测控制在锅炉燃烧系统中的应用

针对锅炉燃烧系统多变量、强耦合、强干扰、大滞后的复杂特性,采用基于状态空间模型的预测控制算法设计多输入多输出预测控制器,以一个控制目标为主,同时协调处理多个控制目标的优化方法,很好地实现了锅炉燃烧控制的三大主要任务,从而为这一复杂系统的过程控制提供了一种新的思路;文中还详细研究了预测控制器关键参数对控制性能的影响规律,据此优选参数,可以获得很好的控制效果.     

基于误差校正的预测控制算法综述

在分析了模型预测误差的来源后,对近20年来典型的基于误差校正的预测控制算法作了较全面的回顾,分析各种方法的研究现状及其成功应用的实例。     

Dynamic Optimization and Non‐linear Model Predictive Control to Achieve Targeted Particle Morphologies

An event‐driven approach based on dynamic optimization and nonlinear model predictive control (NMPC) is investigated together with inline Raman spectroscopy for process monitoring and control. The benefits and challenges in polymerization and morphology monitoring are presented, and an overview of the used mechanistic models and the details of the dynamic optimization and NMPC approach to achieve the relevant process objectives are provided. Finally, the implementation of the approach is discussed, and results from experiments in lab and pilot‐plant reactors are presented.     

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.     

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。     

基于广义预测控制的间歇生产迭代优化控制

针对间歇生产,提出了一种基于广义预测控制的批次迭代优化控制策略--BGPC,在间歇过程中引入批次间优化的思想,将迭代学习控制ILC和广义预测控制GPC相结合,在GPC实时结构参数辨识的基础上利用前面批次的模型预测误差修正当前批次的模型预测值.该算法能够有效地克服模型失配、扰动和系统参数变化等情况.文章最后以一个数值例子和间歇反应器为对象进行仿真试验,验证了该算法是有效的.     

最优自适应广义预测控制在常压加热炉中的应用

对常压加热炉的最优操作条件和最优自适应广义预测控制进行研究。维持过剩空气系数在最优值附近，能够有效提高常压加热炉的热效率。针对燃烧控制与优化的特殊要求，通过在线优化方法得到最优值，并采用广义预测控制算法实施控制。引入阶梯式和自校正控制策略后，系统的鲁棒性和抗干扰能力得到改善。