基于小波神经网络的通用模型控制

针对通用模型控制要求被控对象有显式解的局限性,应用小波神经网络来建立非线性被控对象的逆模型.再结合通用模型控制算法,将非线性过程模型直接嵌入到控制器中,来实现对被控对象的逆控制.其参考轨迹是一条典型的二阶曲线,控制器参数具有明显的物理意义,且易于整定.仿真结果验证了该控制策略的有效性.     

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FGPC的单元机组协调控制系统研究

针对单元机组的大迟延、强耦合、参数时变且不确定性的特点,将T—s模糊模型引入预测控制中,作为预测模型。首先,用改进的模糊c一均值聚类算法和随机牛顿法辨识得到非线性系统的T-s模型;然后基于线性化后的系统模型设计模糊广义预测控制器,并对非线性对象进行在线控制。仿真结果表明：FGPC对于时变的非线性系统具有很好的控制效果。     

加热炉炉温的模糊广义预测控制仿真研究

针对加热炉炉温的大惯性、大滞后及非线性等特点,提出一种基于T-S模糊模型的模糊广义预测控制策略。T-S模糊模型的前件和后件参数分别采用粒子群优化的模糊C-均值算法和递推最小二乘法辨识,根据输入变量更新模型隶属度并将T-S模糊模型等价转换为线性模型,以此作为预测模型应用于广义预测控制。仿真结果表明:该方法在不同工况下均具有较短的调节时间,在扰动作用下有很强的鲁棒性。     

基于IGA和Hammerstein模型的非线性系统广义预测控制

针对非线性程度较高的系统,设计了一种广义预测控制器。该设计基于Hammerstein模型的动静态可分离特性,首先利用具有全局搜索能力的免疫遗传算法（IGA,Immune Genetic Algorithm）在线辨识模型的一些关键参数,然后运用广义预测控制策略实现对该系统的预测控制。仿真试验结果表明,该设计能够准确预测,而且稳定性好、稳态误差小。     

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.     

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。     

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。     

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。     

一种基于模型切换的加热炉炉温广义预测控制器设计方法

为改善加热炉炉温控制自动化程度低、控制品质差的问题,并兼顾加热炉对象强非线性的特点,设计了一种多层次多模型广义预测控制器。给出了该控制器的设计方法和参数。仿真结果和投运效果表明:该方案实施后,系统的响应速度加快,超调量减少,加热炉炉温的控制品质明显改善。     

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.     

双侧送引风机变增益广义预测控制

在分析母管制机组送引风回路控制情况的基础上,提出一种双侧变增益广义预测控制算法。该算法利用回路输入输出数据采用递推最小二乘法对回路对象进行建模,并依此设计了先进的控制系统。该控制方法与原PID控制的对比分析结果表明:该控制方法效果较好,解决了双侧风机不同步的问题。     

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.     

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.     

Nonlinear Model Algorithmic Control of a pH Neutralization Process

Control of pH neutralization processes is challenging in the chemical process industry because of their inherent strong nonlinearity. In this paper, the model algorithmic control （MAC） strategy is extended to nonlinear processes using Hammerstein model that consists of a static nonlinear polynomial function followed in series by a linear impulse response dynamic element. A new nonlinear Hammerstein MAC algorithm （named NLH-MAC） is presented in detail. The simulation control results of a pH neutralization process show that NLH-MAC gives better control performance than linear MAC and the commonly used industrial nonlinear propotional plus integral plus derivative （PID） controller. Further simulation experiment demonstrates that NLH-MAC not only gives good control response, but also possesses good stability and robustness even with large modeling errors.     

Adaptive Internal Model Control of a High‐Purity Heat‐Integrated Air Separation Column

An internal model control scheme based on a second‐order internal model (SI‐IMC) is proposed for the heat‐integrated air separation column (HIASC). An adaptive internal model control (ASI‐IMC) scheme is further presented to make the model more accurate. The IMC scheme based on the first‐order model (F‐IMC) and the multi‐loop PID (M‐PID) scheme are also explored as the comparison basis of ASI‐IMC and SI‐IMC schemes. Comparative researches among these four control schemes are carried out in detail. The results indicate that ASI‐IMC presents the best performances among the four control schemes in both servo control and regulatory control, which proves the improvement of ASI‐IMC over the SI‐IMC and the superiority of ASI‐IMC for the high‐purity HIASC.     

离线辨识和在线辨识相结合的广义预测控制算法在固定床反应器温度控制中的应用

An improved generalized predictive control algorithm is presented in this paper by incorporating offline identification into onlie identification.Unlike the existing generalized predictive control algorithms.the proposed approach divides parameters of a predictive model into the time invariant and time-varying ones,which are treated respectively by offline and onlie identification algorithms.Therefore,both the reliability and accuracy of the predictive model are improved,Two simulation examples of control of a fixed bed reactor show that this new algorithm is not only reliable and stable in the case of uncertainties and abnormal distrubances,but also adaptable to slow time varying processes.     

广义预测控制在维生素C生产中的应用

根据维生素C生产工艺的操作分析和对控制的要求,本文开发应用了隐式自校正广义预测控制算法和相应的程序,并针对过程的时滞特性,在性能指标中引入输出预测误差加权矩阵,较好地解决了该生产过程慢时变、大滞后的跟踪控制问题.     

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。     

Rotary Dryer Control Using a Grey-Box Neural Model Scheme

The application of a Grey-box Neural Model (GNM) in a nonlinear model predictive control scheme (NMPC) of a direct rotary dyer is presented in this work. The GNM, which is based on the combination of phenomenological models and empirical artificial neural network (ANN) models, was properly developed and validated by using experimental fish-meal rotary drying information. The GNM was created by combining the rotary dryer mass and energy balances and a feed forward neural network (FFNN), trained off-line to estimate the drying rate and the volumetric heat transfer coefficient. The GNM results allowed us to obtain the relation between the controlled variable (solid moisture content) and the manipulated variable (gas phase entrance temperature) used in the predictive control strategy. Two NMPC control strategies, one with a fixed extended prediction horizon and another with an extended range prediction horizon, were applied to a simulated industrial fish-meal drying process. The results showed that a correct rotary dryer representation can be obtained by using a GNM approach. Due to the representation capability of the GNM approach, excellent control performances of the NMPCs were observed when the process variables were subject to disturbances. As analyzed in this work, the fixed extended prediction horizon MPC surpassed recognized control methodologies (quadratic dynamic matrix control).