CN1637669A - 过程控制系统 - Google Patents

过程控制系统 Download PDF

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CN1637669A
CN1637669A CNA2005100036199A CN200510003619A CN1637669A CN 1637669 A CN1637669 A CN 1637669A CN A2005100036199 A CNA2005100036199 A CN A2005100036199A CN 200510003619 A CN200510003619 A CN 200510003619A CN 1637669 A CN1637669 A CN 1637669A
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control system
mpc
predictive control
model predictive
process control
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CN100527025C (zh
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E·A·加勒斯泰
A·斯托特尔特
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ABB Schweiz AG
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    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B13/00—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
    • G05B13/02—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
    • G05B13/04—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators
    • G05B13/048—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators using a predictor
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B13/00—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
    • G05B13/02—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
    • G05B13/04—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators
    • G05B13/042—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators in which a parameter or coefficient is automatically adjusted to optimise the performance

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Abstract

本发明的过程控制系统使用最优控制(OC)和模型预测控制(MPC)技术来选择专家系统(ES)的目标U。ES的目标U是通过使性能标准J最小化来选择的。换句话说就是，建立了一种由过程P和ES给定的扩展系统的数学模型。就其中既存在连续动态特性(主要是过程)又存在逻辑关系(主要是ES)来说，这一数学模型具有混合特性。数学模型的受控变量是ES的目标U，而输入是测量值y和性能标准J。OC和/或MPC技术被用来计算U。OC/MPC的最优化器仅选择ES的目标U的值。与选择C相比，这一操作具有更低的采样率，这就使OC/MPC控制器的设计更加容易。

Description

过程控制系统
技术领域
本发明涉及用于矿业的工业过程控制系统的先进建模技术领域。
本发明涉及一种根据权利要求1的前序部分中所述的过程控制系统。
背景技术
最优控制(OC)和模型预测控制(MPC)是现代控制技术中的重要工具。它们使包括过程控制、监控、计划、安排和最优化等内容在内的所有程序都成为可能。它们的应用是基于一种数学模型的设计，例如公式
dx/dt＝f(t，x，u)                               (1)
0＝h(t，x，u)，
和一个最优化函数
J[x(0)，u(·)]＝∫f0(τ，x(τ)，u(τ))dτ       (2)
其中x是系统状态，u是受控变量。
参见图1，OC和MPC是通过使关于u(·)的函数J(2)最小化来实现的，而u(·)又受到了方程(1)的限制。当针对复杂过程和性能测量，并且用“恰好”的直觉几乎不可能找到最优策略时，基于OC/MPC的控制器是有用的。
另一方面，基于专家系统(ES)的过程控制属于另外一种构思。这种控制不需要明确建立公式(1)那样的数学模型，而是建立一种规则系统，如果这种规则能够被准确遵循，那么将可以使过程变量保持在确定的范围内，或者接近预先设定的目标值。这些规则来源于最好的操作实践或者换句话说就是，操作者的经验。这种专家系统的一个成功例子就是在WORLD CEMENT，2002年1月，卷33，Nr.1的“ExpertlyControlled(专家控制)”中描述的ABB的锅炉控制专家最优化器。
在对于怎样使过程保持在确定的范围内或接近确定的目标可以得到操作者的经验的情况下，ES是有效的。图2示出了这种情况：测量值y和过程目标U被馈送给专家系统ES。然后，ES遵循它的规则系统并且控制受控变量C以使过程尽可能地接近目标U。应当注意，到目前为止性能标准J并没有对设备被操作的方式发挥直接的作用。
这两种方法都有优点和缺点。OC和MPC既依赖于一个好的过程模型，又依赖于最优化器(即用于找到J[x(0)，u(·)]的argmin的算法)的性能(速度)和可靠性。专家系统的一个缺点与用系统方法使复杂过程的性能最大化的难度有关。
OC、MPC和ES算法都通过计算机程序来实现，在这些程序中，给定过程的测量值通过反馈用一种自动的方式来驱动过程。
发明内容
本发明的目的是建立一种用于操作工业设备的改进的过程控制系统。
这些目的是通过根据权利要求1所述的过程控制系统来实现的。
本发明的过程控制系统使用数学模型技术来选择ES的目标U。U是通过使性能标准J最小化来选择的。数学模型的受控变量是ES的目标U，而输入是测量值y和性能标准J。数学模型使用OC和/或MPC技术来计算U。
与具有标准OC/MPC应用的过程控制系统相比，本发明的过程控制系统允许OC/MPC应用的最优化器在较低的采样率下运行。
本发明的过程控制系统改进工业设备的经济性能。
附图说明
在下文中将参考附图更详细地解释本发明的主题，其中：
图1表示一种最优控制/模型预测控制的配置，
图2表示一种专家系统的配置，和
图3表示本发明的配置。
具体实施方式
本发明的过程控制系统使用OC和/或MPC技术来选择ES的目标U。ES的目标U是通过使性能标准J最小化来选择的。换句话说就是，建立了一种由过程P和ES给定的扩展系统的数学模型。就其中既存在连续动态特性(主要是过程)又存在逻辑关系(主要是ES)来说，这一数学模型具有混合特性。数学模型的受控变量是ES的目标U，而输入是测量值y和性能标准J。现在，参见图3，OC和/或MPC技术被用来计算U。
本发明的过程控制系统的其中一个主要优点可以由下面的事实反映出来：
在标准的OC/MPC应用中，最优化器需要实时地选择控制器值C。这种情况给系统带来了很大的压力并且对系统的可靠性提出了更高的要求。随着本发明的过程控制系统的建立，最优化器仅选择ES的目标U的值。与选择C相比，这一操作具有更低的采样率，这就使OC/MPC控制器的设计更加容易或者更加切实可行。此外，构造相关的数学模型也更加容易，因为它有可能忽略关于快速时间常数的现象。
优选地使用混合逻辑动态(MLD)系统方法(参见Bemporad A.和M.Morari的“Control of systems integrating logic，dynamics，and constraints(结合逻辑、动力学和约束来控制系统)”Automatica，Special issue on hybrid systems，1999年，卷35，n.3，407-427页)来建立扩展系统“Plant+ES”的模型。但是，很明显这些数学模型能够使用基本原理方法学、黑箱技术或它们的任意组合来构造。
这种方法的另一个优点就是使升级经典(传统)ES控制方案变得容易。事实上，使用这一级别的控制器，ES控制性能可以被影响和改变以满足不同的性能标准，从而延长“旧”系统的使用寿命。
在本发明的过程控制系统的另一个应用实施例中，上述方案的多个层都可以被放置在适当位置以便最优化过程控制。在该情况下，一个ES受到一个基于OC/MPC的控制器的监督，而这一控制器又受到另一个ES的监督，等等。这种结构可以为每个特定的任务提供对每种方法的优点的最优化利用。
为了进一步说明，现在通过一个水泥窑炉的实际例子来描述本发明的过程控制系统。
通常窑炉控制专家系统的一个目标就是燃烧区域的温度(BZT)。有规则会指示ES如何使系统接近这个固定的目标。另一方面，OC/MPC控制器已经被设计用来监督熔渣的化学性质。
现在，考虑这样一种情况，其中数学模型检测到相对较冷的供给将要到达燃烧区域的情况，该情况具有低BZT值的很大的潜在风险。在这种情况下，用来监控温度对熔渣质量的影响的OC/MPC控制器，很可能会在这种情况发生之前的几个采样时间改变BZT的目标使其达到一个更高值。因此，最优的过程温度将不会受到影响。
在另一个例子中，一种OC/MPC控制器降低原料供给速率的目标以便满足某种发射率的限制，或者在一些特殊的情况下，用来增加某种低质量(热值)的替代燃料的使用。
ES系统更加关注过程的稳定性，而OC/MPC系统在某种意义上更加关心“最优性”，例如他们使过程达到它们的经济最优点。因此，在本发明的过程控制系统中，性能标准J优选地涉及某种经济性能测量。
本发明的过程控制系统也可以应用在垃圾焚烧设备的控制中。

Claims (3)

1.过程控制系统，包括：
专家系统，该系统：
在过程的操作区域范围内对过程操作者的动作进行建模，
表示一组动作的规则，这些动作是一旦在过程的操作中出现预定情况时，操作者为了满足给定的过程目标而采取的动作，和
在其中一个预定的情况存在时，采取动作以便通过改变一个或多个根据所述规则组控制过程的过程控制变量来控制过程的操作；和
模型预测控制系统，该系统可以被用来：
接收表示过程的测量变量的输入，和
根据模型预测控制系统的输出来预测控制过程的受控变量的预测控制值；
其特征在于：
模型预测控制系统和专家系统是串联连接的，使得模型预测控制系统的预测控制值作为专家系统的过程目标。
2.如权利要求1所述的过程控制系统，其特征在于：
控制专家系统的过程目标的模型预测控制系统受到另一个专家系统的监督。
3.如权利要求2所述的过程控制系统，其特征在于：
多层的模型预测控制系统和专家系统是串联连接的。
CNB2005100036199A 2004-01-09 2005-01-07 过程控制系统 Expired - Fee Related CN100527025C (zh)

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CN100527025C (zh) 2009-08-12

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Granted publication date: 20090812